When a group is adjourning, you should expect students to lose some engagement or focus. You should remind students of their shared purpose or take actions to improve engagement (e.g., team building activities or reflection).

Help students recognize that conflict will occur and is healthy and that respectful and low-stakes conflict is a key part of the inquiry learning process. Strategies to make conflict low-stakes include:

• Acknowledge conflict early, before action has already been taken and a group's project rides on the outcome of the conflict.

These dynamics can be difficult to monitor simultaneously in multiple groups. You can bring multiple groups together for whole-class check-ins to assess dynamics. Seeing peer progress or reflecting on their own group's progress can sometimes help student groups move between stages.

Students may be better able to recognize and self-regulate group dynamics if they are familiar with what to expect. Consider presenting Tuckman's stages of group development and leading a discussion of what actions each group can incorporate to recognize and act on those (e.g., team contracts during forming, reflection time during adjourning).

## *Grade-Level Differentiation*

#### **Elementary (3–5)**

State the importance of care for self and care for others. Suggest explicit phrases for voicing an opinion about a group plan or activity and for resolving disagreement.

#### **Middle (6–8)**

Discuss the need for and benefit of conflict and disagreement. Model resolution strategies and emphasize that disagreement does not need to be taken personally.

#### **High (9–12)**

As noted earlier. Discuss group development stages and strategies for dealing with conflict at each stage.

# *Examples*

*Figure 6.9* Working together to place a measuring tape over rocks for a vegetation transect

*Figure 6.10* Snacks shared among the team provide an opportunity to relax and re-energize

*Figure 6.11* Building team identity through shared experience; here, giving permanent marker plant tattoos

*Figure 6.12* Marking shared team identity through art; here, a cartoon of a teacher (left), a volunteer (second from left), and student team members

# **6.5 Team Building**

#### *Concepts*

When students are assigned into a group, they are not yet a team. They may not have any shared experience or trust, or even know each other. Teamwork requires interpersonal relationships to be negotiated. To build a group into a team, they need to establish trust and shared experience and develop approaches for acknowledging and resolving conflicts. Team building activities model this process. While these activities may not always appear to be directly linked to inquiry projects you are facilitating, they provide a foundation. They also help students learn how their peers behave in challenging situations.

Learning outcomes from team building include building positive collaboration, communication, and listening skills. For example, developing respectful communication and active listening skills will help students develop project ideas and interpret results; sharing responsibilities through a challenge will help students divide responsibilities and collaborate during an inquiry project. These skills also can transfer to the student's classroom learning or personal development.

Team building activities can be fun challenges or games for students. Spending time on these activities can provide physical and mental breaks for students, help maintain engagement, and delineate milestones during the day.

#### *Equity and Inclusion*

Team building can intensify the power dynamics that students experience daily. Invest time in reflection to build positive interactions, and step in early to cut off inappropriate behavior when needed. Chapter 13 provides facilitation strategies.

## *Implementation*

Begin with team building, rather than with inquiry. If you rush into the inquiry process right away, your students may not yet be prepared to work well with each other or to think about their local place (their schoolyard or park) critically. By taking time to develop these areas, you will have more success later.

Introduce activities to the whole class, then implement with multiple small teams in parallel. You may want to allow students to repeat the activity (to improve performance) after they discuss outcomes within teams. After one or a few repetitions, hold a reflection session.

Reflection allows students to scaffold their experience into principles and trust they can apply within their inquiry projects. Reflection should mirror the teamwork and conflict resolution processes used in projects. Ask students to use "I. . . " or "When you do X. . . I feel. . . Y" statements when talking. Ensure only one person speaks at a time and that all students have a chance to speak at least once (e.g., by passing around a ball or stick that grants the holder speaking privileges). Gathering in a circle helps signal equality.

Example questions to ask students:

Time spent on team building activities should be considered an investment in enabling future success. You may be tempted to skip these activities, thinking that your students already know each other and work together well. Team building helps prime inquiry groups for immediate success and also helps set behavioral expectations as students work together in teams that may bring together students who usually do not spend time with each other. Each activity may take 10–20 minutes and may save one to two hours of lost student time later on. For a week-long project you may consider interspersing three to four activities within the project.

All activities should be done in small groups. Additional activities can be found in Bordessa (2005) and Price et al. (2015). All activities can be made more challenging by restricting students' methods of communicating (e.g., no talking, no signing) or by providing a time limit.

#### **Getting to know names and each other**

• **Animal noises**—Put one student in the center of a circle of the other students. The center student closes their eyes and spins around, then stops after a few seconds. They then point at the person in front of them. The center student names an animal (e.g., goose) and the student being pointed at has to make that animal's noise. If the center student can guess the name of the student on the outside of the circle, they switch; otherwise, repeat, or switch after two to three incorrect guesses.

#### **Building communication skills**

until it is picked up again. If there is ever a pad with no one touching it, then it is lost. If any student falls in, the team starts over. Begin with five to six pads for a group of eight students. As a "lava monster," steal pads from students to make the game harder.

• **Lava lake**—Find a large rock or sidewalk block. Tell students that lava is filling their site, and they will catch fire unless they can all stand on the platform above the lava. They have to be able to balance without falling on the platform for at least ten seconds. Once they succeed, tell them that the lava is rising and the platform is getting smaller. Find a smaller object and repeat.

#### **Building positive relationships**

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

Be prepared for groups becoming frustrated or unable to understand how to solve a team building challenge. In these cases you may need to provide additional guidance or hints. See later.

#### **Middle (6–8)**

Set clear behavioral expectations for appropriate types of physical touch in activities that require it (e.g., only hands/shoulders, no causing pain), if school guidelines do not already exist.

#### **High (9–12)**

As noted earlier.

## *Examples*

*Figure 6.13* Magic stick

*Figure 6.14* Lava river, with blue foam pads

*Figure 6.15* Predator-prey, with predator in center

*Figure 6.16* Monster game

# **6.6 Team Contracts and Check-Ins**

#### *Concepts*

Working on a team is effective when a common set of values and a high level of trust exist. Team members should set expectations for what to expect from each other's behavior, how they will work together, what they want to achieve, how they will handle conflict, what skills each student brings, and how to get in touch (if working out of school). Preparing a team contract sets clear expectations and promotes healthy discussion in advance of conflicts. In the case that conflict does occur later, it provides a mechanism to promote conflict resolution and accountability.

Mid-project check-ins provide a time for students to provide feedback on how their projects are going. This enables them to recognize their teammates for helpful behaviors and also works toward conflict resolution for unhelpful behaviors. Setting a formal context for check-ins normalizes feedback and healthy conflict and ensures that there is at least one opportunity for improvement to occur.

#### *Equity and Inclusion*

Contracts and check-ins empower students who might not usually speak up or feel comfortable in group settings. These structures enable these students to have their voices heard and listened to. Because some students may be less comfortable expressing their needs, it can be helpful for students to write down their needs privately or anonymously before sharing with peers.

Students may begin with limited empathy for the needs of their teammates if those needs differ strongly from theirs. Consider journaling exercises that allow students to share more information about themselves to help others understand their needs.

A contract and check-in are only helpful if students are accountable to what has been agreed upon. Take the conflict resolution process seriously. Otherwise, you may inadvertently teach students who may already be in marginalized positions that their needs are subordinate to those of other students.

#### *Implementation*

Introduce the team contract and check-in expectations during the inquiry project process so that expectations are clear. Start the team contract after students have met each other and gotten to know each other through initial team building or sense-of-place activities, so that some baseline trust is in place. Begin from a template document (see Appendix 1) and have students create a consensus document. Give students a copy for their own reference and keep a copy for yourself.

Start the check-in midway through the project (see the template in Appendix 1). Gather students and ask them to revisit their team contract and reflect on their behaviors. Ask them to share feedback on what they (as a team) should start, stop, or continue doing.

In cases of conflict or low performance, use the team contract as a starting point for encouraging student accountability via group discussions. You should acknowledge in a group setting that conflict is occurring, but should resolve the conflict privately to maintain respect for affected students. Ask for critical feedback about individuals to be shared with you privately instead of in groups. Use facilitation techniques (Chapter 13) to guide private discussions with students.

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

Minimize writing and instead gather consensus through a discussion about how students would like to be treated. Integrate any existing classroom standards for behaviors such as kindness and respect. Expect to personally check in frequently on appropriate behavior.

#### **Middle (6–8)**

Instead of using a template, have students write out their own expectations of their peers on a whiteboard, then discuss to generate a consensus set of items for the team.

#### **High (9–12)**

Each group can develop their own contract using a template. Have groups share their contracts with the whole class and discuss, then give groups more time to see if they want to modify their contract based on other ideas they liked.

## *Examples*

*Figure 6.17* Team contract (fourth grade) with signatures in the center

*Figure 6.18* Team contract (fifth grade)

*Figure 6.19* Digital team contract

# **6.7 Handling Multiple Groups**

## *Concepts*

When a class is divided into groups, you have to make choices about where to invest your attention. Student-centered teaching means that you will not need to monitor each individual as much as you may be used to, and letting go of some of that control is what allows students to learn responsibility and self-efficacy. Your task is to identify which groups may be derailed due to conflict or distraction or confusion. With small groups, you will be able to effectively facilitate while paying attention to each individual within the group, but at the price of having less time with each group.

Larger groups means fewer groups; however, larger groups are less functional due to unclear student roles, more likely off-task behavior, and a greater probability of conflict. Larger groups also mean your attention is more likely to be taken by few individuals and that students feel less responsibility and self-efficacy.

# *Equity and Inclusion*

Be alert to your own tendencies to spend more time with certain groups. Some groups may need more support, while others may be more selfsufficient, but reflect regularly on which evidence or criteria you are using to allocate that time and how it relates to the identity of students in the groups. This reflection can be helpful for identifying your own patterns of behavior to see if you need to make any adjustments.

# *Implementation*

Working with volunteers can help keep group size small, but even with volunteers you will need to balance between the needs of multiple groups. You can:

• **Float between groups**—You should regularly walk between groups to do short check-ins. Visit groups in random order so students do not know when to expect you. Check-ins also give you a chance to gauge progress and keep projects and behavior on track. Observe groups before making any conclusions or taking actions. You can also volunteer to help students by taking part in their work (e.g., by offering to record data).

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

Supervision is critical in small groups, so volunteers are especially useful.

#### **Middle (6–8)**

Students may get sidetracked by social or environmental distractions. Focus on floating between groups for regular check-ins.

#### **High (9–12)**

Give students more independence in project management while communicating clear expectations for project progress. Provide more supervision initially and transition to a hands-off approach. Encourage groups to come to you with problems and questions, giving them space to work through issues on their own before deciding they need help.

# *Examples*

*Figure 6.20* Student groups working out of hearing distance but within the line of sight of adults

*Figure 6.21* Keeping groups separated while walking minimizes distractions

# **6.8 Getting Groups Back on Track**

#### *Concepts*

Sometimes a group can appear to be stuck and can remain in that state until you intervene. This is especially common with students who have not worked together before or with students who are not accustomed to the challenges of independent thinking or behavior management. Groups can also get stuck when they run into a major obstacle they are unable to solve, when they no longer understand what they are doing, or when they lose motivation. The latter three scenarios can occur in a cascading sequence, so it is important to provide help for stuck groups early on.

Ideally, students should find their own ways of getting unstuck, though you can facilitate. They are usually capable of resolving the obstacle, gaining understanding, or building intrinsic motivation. This can require patience, as you may be able to think of a solution from your own experience, but sharing that solution may increase a group's dependence on you and promote undesired power dynamics. Instead, you may need to let a group take an unfruitful path, experience conflict and setbacks, then provide questions and reflection to support them in moving past their challenge.

# *Equity and Inclusion*

Providing low-stakes opportunities for initial failure and later success is empowering. Remind students regularly of their ownership of the process and of the learning goals associated with the process, rather than some outcome on paper, especially if they seem to be experiencing anxiety. Some students may not be as comfortable with low-stakes failure than others due to family resources and socialization.

Be careful to not assume your cultural expectations are being implicitly applied to student groups. For example, the volume of a discussion (very quiet or very loud) may not indicate a problem is occurring, depending on the students' identities.

# *Implementation*

You can often notice a stuck group when students are clearly off-task, are not interacting with each other, appear uncomfortable (e.g., not making eye contact, doodling, playing with sticks), are experiencing negative interpersonal dynamics, or are unable to answer basic questions about the project and their progress.

A few strategies include:

• **Sharing and reviewing**—Provide a few key pieces of information or ideas as hints to help the group move forward. You may temporarily need to pivot away from inquiry to direct instruction if there are key concepts students need to understand but do not yet (e.g., how to make a graph).

Avoid giving explicit directive feedback (e.g., "You need to measure X differently" or "Your idea is wrong"). Such feedback can be discouraging and detrimental to the group process. Students may sometimes learn more if they make a mistake or if their question's answer is unexpected or undesired.

You should give directive feedback primarily when it is oriented around group process or planning. For example, you can ask students to spend more time considering their hypotheses or to wrap up a certain measurement, or tell students they need to finalize their data analysis by a certain time.

Directive feedback is useful if you foresee students making choices that will prevent them from finishing their work on time or that will yield a study that is wrong with a failure mode that is uninteresting. For example, a group may spend most of their time planning their work or may fail to measure something because they are using their equipment incorrectly. While both situations still represent learning experiences, you can provide directive feedback early on to enable more useful learning experiences to occur.

## *Grade-Level Differentiation*

#### **Elementary (3–5)**

Provide more direction on timelines and milestones.

# *Examples*

*Figure 6.22* Listening to a student explain a data analysis challenge

*Figure 6.23* Sometimes taking a break is a good idea

# **Developing a Question and Study Design**

# **7.1 Facilitating Question Development**

#### *Concepts*

**7**

An inquiry project answers a specific question about the world. The question should be meaningful to students. Their connections to a place, societal values, prior or traditional knowledge, and curiosity can all influence their choices. Students should lead the question generation process with your facilitation and support.

The question's answer should be unknown. If an answer is already known, no inquiry needs to be made; similarly, if an unexpected answer will not be accepted, the process cannot uncover new knowledge. Letting the observations and data determine the answer develops confidence and connection to place through discovery.

Identifying a strong question requires creativity. It can be facilitated by focusing on an exploration stage, in which students gather ideas without prejudgment, often by allowing an environment to inspire them, and a winnowing stage, in which students take gathered ideas and evaluate them, with more structured criteria, to arrive at a final choice.

## *Equity and Inclusion*

Students with identities that differ from yours may develop questions that you do not find as interesting or relevant. Hold your criticism and consider the negative power that your dismissal can create. For students to develop creative ideas, you need to withhold censorship. Your goal is to help students feel ownership and connection and become confident learners. You can reach these goals with any topic being investigated. The actual outcome of a project is not as relevant as the inquiry process.

#### *Implementation*

You can motivate question generation through any of three routes: directly observing interesting phenomena in a place (e.g., during exploration), thinking about issues that affect students' relationships or actions in a place, or obtaining background place knowledge from expert/traditional sources and wondering about things that remain unexplained.

During or after place exploration, engage students individually or in pairs through question generation activities. All students should participate in question generation. Students may be hesitant to pose questions if they are not often asked to do so or if they feel that their ideas are not good enough. Encourage a beginner mindset and emphasize the value of creativity. You should not yet judge any of the questions, except to provide positive affirmation.

Some activities to support question generation include:

All questions should be written down or recorded. This supports formulating thoughts clearly and remembering ideas from one session to another.

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

Students may not be able to write down questions as fast as their ideas flow. You can instead write down questions for students on a whiteboard. Using a whole-class anchor chart (see Appendix 1) can be helpful. Notebooks are still useful for drawing, pressing leaves, or writing short observations. Students have short attention spans. Allowing physical movement and a lot of individual student freedom to explore or move at their own pace will help keep them engaged. Teacher-imposed transitions between steps for the youngest grades every few minutes will prevent students from getting bored or too involved with off-topic activities. Be careful not to interrupt students who are productively engaged.

#### **Middle (6–8)**

If you decide to allow each student or small group to select their own question, you will have to manage more, and likely different, projects. On the other hand, students will be able to work on projects over longer periods of time, thus giving you more time to check in with each project.

#### **High (9–12)**

You can direct projects toward a specific topic area connected to other lessons being taught in the same year. However, it is important to still allow for creativity and student-led decisions to take place about project selection.

# *Examples*

#### **Urban schoolyard**

#### **Suburban schoolyard**

#### **Forest/lake area**

*Figure 7.1* Brainstorming questions on sticky notes (here, a question about bananas with a book answer)

*Figure 7.2* Sharing individually generated questions on a group whiteboard

*Figure 7.3* Starring questions that seem most interesting

# **7.2 Building a Strong Question**

#### *Concepts*

The questions students generate usually will require refinement to become stronger. The basic requirements of a strong scientific question are:

There will be many possible questions, but only time to answer one or a few. It is important to build consensus so that all group members feel motivated to find an answer to whatever question(s) are chosen.

Strong questions also provide opportunities for learning and engage all students. Questions can be very simple and still worthwhile (e.g., "How many birds live in the schoolyard?").

Some questions are more tractable than others. Generally, questions with descriptive answers are often the simplest to answer (e.g., those with "what," "where," "when," or "how many" phrasings). Questions with process-based answers (e.g., those with "why" phrasings) can often be broken up into specific sub-questions (e.g., those with "how" or "do" phrasings) that can be addressed via multiple hypotheses, and synthesized to answer the broader "why" question.

Weak questions often fall into common categories and can be redirected into strong questions by considering the following table.

(*continued*)

#### *Equity and Inclusion*

Coming to consensus on a question can be difficult because the process is intimately tied to identity and power dynamics. Students of some identities may be less comfortable sharing questions or their motivations for asking them. Use facilitation approaches that empower all students to provide input, and set strong expectations for respectful and inclusive discourse (Chapter 6).

#### *Implementation*

After completing the question generation process, you can transition to the question winnowing process.

Have each student in each group share their questions with their peers and you. If you have too many questions, ask each student to only share their favorite one to three questions with the group. If a student is reluctant, allow them to pass with the understanding that you will come back to them. Write down all questions where all students can see them.

Next, ask students to use the previous criteria to determine which questions are strong ones. Also, ask them to think of whether or how they could strengthen each question, and allow them to do so. A thinkpair-share can be helpful at this stage. Using such criteria to rule out questions helps groups make progress without students whose ideas were not chosen feeling left out. Your job is to act as a facilitator and guide, offering practical advice by asking questions (e.g., "How could you answer that question without a thermometer?" or "Would it be ethical to do that?").

It is not always obvious whether a question is tractable. The best way to find out is to allow your students to brainstorm the required study designs, hypotheses, and methods required. Thinking about the implications of hypotheses is critical—choosing a study design is thus an important component of the question selection process.

Once your students have identified a small set of strong questions, help them come to a consensus on which question they want to answer as a group. Ask them to discuss:

Encourage students to work together to find consensus or compromise. Often the final question emerges as a combination of many students' ideas.

In rare cases, students may be disappointed that their idea was not chosen by the group. Returning to the question winnowing stage may allow a compromise to be found. If not, you may be able to help engage these students by giving them first choice of roles in the implementation of the project.

This consensus process will take an unknown amount of time, so build flexibility into your schedule. Patience is key because building consensus and engagement early on helps everything that comes afterward.

The process requires positive interpersonal dynamics to be successful, so you may also find it helpful to break up the question generation and winnowing process with additional team building or sense-of-place activities. You may also need to step in to guide the discussion or make hard choices, but it is better if you are able to step back.

#### *Grade-Level Differentiation*

#### **Elementary (3–5)**

Losing focus during question winnowing can occur. Use a whiteboard to guide discussion. Consider using larger groups so you can provide more support and facilitation. You can also be more direct in guiding question choice.

#### **Middle (6–8)**

Interpersonal dynamics may play a larger role in the winnowing process. Invest extra time in team building and facilitation.

#### **High (9–12)**

Give students full agency and responsibility to discuss and find consensus. Provide primarily technical advice to guide decisions.

## *Examples*

#### **Plants**

#### **Animals**

#### **Earth**

#### **People**

146

*Figure 7.4* Selecting the strongest question (circled) by consensus

# **7.3 Designing a Study and Using an Anchor Chart**

#### *Concepts*

Study design refers to determining what type of observations or data can answer a question. It is a creative process (Scheiner & Gurevitch, 2019).

To answer a question, a study's aim can either be descriptive or use hypothesis-testing methods. In a descriptive study, the specific and immediate measurements themselves are of greatest interest. In a hypothesistesting study, the measurements help to distinguish between different answers. Descriptive studies are most appropriate when your goal is to characterize a phenomenon you do not know much about. Hypothesistesting studies are most appropriate when your goal is to infer something that cannot be directly observed, such as an underlying mechanism. Both can lead to new knowledge about the world.

In both aims, one or two variables are measured: "something that needs explanation" (dependent variable or metric of interest, Y)

*Figure 7.5* Generic graph template

and "something that may explain it" (independent variable or possible explanatory metric, X). The data (i.e., your measurements) will then illustrate whether or not the independent variable influences the dependent variable (i.e., the phenomenon of interest). In a hypothesistesting study, prior ideas exist about how the variables are related; in a descriptive study, no prior ideas exist. The main focus in a descriptive study is the dependent variable; independent variables may be used to group the measurements for summarization (e.g., number of birds [dependent variable] in different parts of the schoolyard [independent variable]) or may be skipped (e.g., if the project is interested in the number of birds overall).

A hypothesis-testing study design will often fall into several common categories depending on logistical feasibility and ethics. However, any measurements or observations that allow you to confirm or refute the relationship between a dependent and independent variable can help test a hypothesis. The following table shows common study designs. The examples all address the question: "Do plants (of a particular species) grow better when they are well-watered?"

149

(*continued*)

Once a study design has been chosen, the types of analyses necessary to answer the question are straightforward. Each study design can be recast as yielding a certain type of graph. Most graphs plot two axes: the X-axis (left to right) depicts the independent variable, the Y-axis (bottom to top) depicts the dependent variable. Different types of data can be represented using different colors or multiple panels.

Each hypothesis yields a certain prediction for the graph type of interest. You can compare the data you collected to these expectations with visual interpretation or with statistical tests (Chapter 10); either process yields the answer to your question.

*Figure 7.6* Example set of mutually exclusive hypotheses and predictions (top) that can be compared to data (bottom), rejecting Hypotheses 0 and 1

*Figure 7.7* Example bar plot prediction for a randomized controlled trial design investigating plant growth

*Figure 7.8* Example scatter plot prediction for a cross-sectional design investigating drivers of leaf size

*Figure 7.9* Example frequency plot prediction for a cohort design investigating cricket personalities

152

(*continued*)

Example study designs for different questions are shown in the following table.

#### *Implementation*

First, consider whether the project should be descriptive (i.e., focused on exploration of directly observable things) or hypothesis-testing (i.e., focused on evaluating which of multiple possible answers is correct). This decision may depend on the age of your students and other considerations discussed in the next section. In a descriptive project, you can skip the hypotheses component of an anchor chart.

Second, consider different study designs. The choice will likely be determined by the practical considerations outlined earlier. You do not necessarily need to teach students terminology for study designs, but you should ensure they understand that intentional planning is required to obtain data that are useful for answering their question. In a hypothesistesting study, some hypotheses may lend themselves more naturally to certain study designs (e.g., randomized controlled trials).

Third, you should explore different metrics to measure; that is, the particular variables that will appear on the graphs and that students will

*Figure 7.10* Generic anchor chart (see also Appendix A1.1)

physically measure. Sometimes these metrics are obvious (e.g., amount of water added to soil), while in other cases, they will require discussion (e.g., "plant growth" potentially represented by height, width, or number of leaves).

These steps can be structured using an anchor chart (see Appendix 1). The anchor chart indicates the flow of reasoning from question to hypotheses to predictions to data. Note that imagining different possible outcomes is a key and difficult part of this step. Later, this allows students to connect the data they collected back to the question they asked. By illustrating predictions visually as graphs, the anchor chart also streamlines later data analysis steps.

Each group can be given a blank anchor chart with the goal of filling in all the components they are certain about (e.g., a particular question, hypothesis, prediction, or measurement). Empty sections of the chart or places where there is not a clear item to fill in indicate places where students need to do additional thinking. The process is complete when the study design part of the chart is completely filled in and the information within it is internally consistent.

Ideas that do not fit into the chart can be flagged as unneeded, helping to minimize extraneous effort. An anchor chart also helps students recall their past work, especially when working on projects over multiple days, and allows them to make connections as new learning happens. When students present their findings later on, they can use the anchor chart to anticipate how they might organize and share their findings.

Encourage students to treat the chart iteratively. Sometimes, a question is decided first and then study designs are considered second. There should be a back-and-forth through the creative process. Have students work with pencils or erasable markers with their anchor charts to encourage this iterative process.

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

Minimize steps between measurements and interpretation. Students may suggest metrics of interest; you may have to suggest possible study designs. Use strip plots to display all data values and do not calculate summaries like averages. Continuous X-axis plots are more complex and will require additional practice.

#### **Middle (6–8)**

More of the study can be designed with active student participation. You can develop the anchor chart on a class whiteboard, so that while all students can contribute, you can keep control of the possibilities discussed and gently add information on possible graph types. Review the concept of scatter plots and practice finding the correct location for data points.

#### **High (9–12)**

Students can develop their own study design. Reviewing graph types is still useful.

#### *Examples*

*Figure 7.11* Example anchor chart for a cricket personality project, with study design predetermined by the teacher

**Idea brainstorming:** A third-grade teacher wanted to incorporate an inquiry-based group project in her unit on ecology, plants, and climate. They did not have any specific equipment or experience.

What could they measure? Around the school building were some (watered) grassy areas, some trees, and a bare desert area (not watered). From prior exploration with their class, they came up with the following list of possible measurements that could be included in a project with only a ruler, a thermometer, and the ability to count:

160

They realized there were some properties of locations that the students could assess:

They realized that their class would be able to address several different questions, for example:

	- *Option 1*: air temperature (Y) under trees vs not under trees (X)
	- *Option 2*: ground temperature (Y) vs the height of plants (X)
	- *Option 1*: number of ants present (Y) on the artificial lawn vs the natural landscape fragments at the edge of the schoolyard (X)
	- *Option 2*: places with/without birds (Y) vs type of land cover in the schoolyard (X)

Other questions may be about the size of leaves on the same type of plant (e.g., grass or same type of tree) on different sides of the building to test whether leaf size responds to shading; does shade/air temperature affect the use of different schoolyard areas by students; does the temperature of the air reflect the ground temperature; is it better (cooler) to be in the shade of a tree than in the shade of a building; how much does the temperature of the ground or the air change across the school day; is a tree itself divided up into different microclimates.

# **7.4 Identifying Multiple Hypotheses and Predictions, or Not**

# *Concepts*

Hypotheses are plausible answers to the question you have posed or processes that explain the patterns you see. They are creative ideas originating with students and represent key engagement with the inquiry process.

Strong hypotheses help generalize your understanding of the world beyond your immediate observations. When you are starting out with inquiry-based projects, often both questions and hypotheses will be directly tied to visible or measurable phenomena (e.g., "Is the ground under the trees hotter than in the parking lot?"). The possible answers are simply "yes" or "no." However, when hypotheses contain more general ideas about the world, more learning and conceptual synthesis can occur. For example, if students start with the question posed earlier, you might guide them to expand on why the answer may be yes or no or why they are interested in this question. This could lead to the question, "Would planting trees help maintain a cooler city environment?" with at least three interesting hypotheses:

These hypotheses are connected to Disciplinary Core Ideas and would potentially allow generalization to other places.

Hypotheses also need to make predictions (i.e., correctly anticipating previously unknown results). A prediction is what you expect the data to look like if the hypothesis is true, not what you imagine will actually happen. Predictions may be shown as graphical representations or statements like "X should be smaller than Y" or "when X increases, Y increases too." Predictions are logical consequences of each hypothesis, such that if they do not happen, the hypothesis can be considered false. Useful predictions are ones that help distinguish between hypotheses. If all your hypotheses make the same prediction, then this prediction is not useful for answering your question. The predictions you make identify the potential measurements that should be made. If variables do not appear in predictions, then they do not need to be measured. Alternatively, if an unaccounted-for variable seems interesting, then the hypotheses and predictions might need to be revised to include it.

Multiple hypotheses need to be allowed to compete against each other; this also implies that you always have at least two contradictory predictions. If there is only one hypothesis, then there is no way to empirically determine if another answer might be better. You should never have just one hypothesis that you are trying to "prove." This creates bias in the study, emotional attachment to a specific outcome, and potential resistance to accepting the actual outcome if a hypothesis turns out to be "wrong." A set of hypotheses should even include answers initially deemed unlikely. All interesting answers should be considered, even if not all can be tested in the project.

Hypotheses are tested by comparing their predictions to reality via "strong inference." You are only able to make strong inferences when you "rule out" rather than "rule in" hypotheses (Fuller, 2004; Platt, 1964). Any hypothesis which yields a prediction that does not agree with the data is "ruled out" (i.e., rejected). Any hypothesis not rejected is typically accepted (we say, "The study supports the hypothesis" or, more accurately, "The study cannot rule out the hypothesis"). It is possible that this inversion process (accepting as true the only hypotheses that are not ruled out) may sometimes produce erroneous inferences, so some caution is required in interpreting any hypothesis-testing study. It is also possible that all of the hypotheses you think of are ruled out by the data; this implies that you did not consider all possible answers. Several hypotheses may be ruled out at once if they made the same prediction; this should be avoided by ensuring you have enough hypotheses so that contradictory predictions are produced.

Inference is based on the data alone, not on opinion or desire. Data can include all the information you collected according to your study design, as long as you did not know what it would be as you drew up the hypotheses and predictions. Data can include direct or received human experience of the world (for example, consultation of elders or other community members).

The art of hypothesis formulation consists of identifying possible answers to your question that refer to interesting, general processes, while making concrete, realistically testable, and hopefully mutually exclusive predictions. This process requires creativity and anticipating both what can be measured and how actual measurements will be compared to predictions.

Some hypotheses may yield predictions that are unethical or impractical to test (e.g., involving violence or equipment you do not have). A study does not compare every prediction to reality, but it is critical to acknowledge these hypotheses as untested alternatives when doing strong inference.

Alternatively, descriptive studies are often used when you do not yet know enough about a phenomenon to even state any hypotheses. Such inquiry that focuses on exploration alone (i.e., without testing hypotheses) is also valuable and important. These inquiries expand curiosity, connection to place, and factual knowledge. Descriptive questions can support learning in terms of sharpening natural history and observation skills, analyzing data, and developing communication and teamwork skills. Students can also develop the same sense of ownership and actualization as with a hypothesis-driven question.

Descriptive studies can be a starting point to guide students to later hypothesis-driven studies. Observations provide a key foundation for science. Without exploration and observation of phenomena, you cannot build up to a more general understanding of processes necessary for formulating mechanistic hypotheses. Concepts and ideas about mechanisms can be built on top of observations later.

#### *Equity and Inclusion*

Scientific inquiry can appear to devalue knowledge that cannot be reduced to data or graphs and can hide underlying discussions of what *should be*. Other fields like philosophy, religion, and spirituality are concerned with how the world ought to be, what goals people should have, and what is morally good. Scientific inquiry cannot answer these types of questions (confusion about this point is often termed the "is-ought problem" or the "naturalistic fallacy"). Science can, however, be used to determine what and how people think about the world. Discussing what the world is like now, rather than what it could be, does not preclude broader learning in additional educational contexts, and in broader reflection (Chapter 11).

#### *Implementation*

Once students have started their anchor chart (see Appendix 1) and potentially selected a study design, have them brainstorm possible answers to the question. Include answers that come from prior knowledge, but also include unlikely or silly ones. List them all. Then go through the list systematically, filtering down to a smaller set of hypotheses that seem comprehensive but distinct.

After hypotheses are identified, shift students toward making predictions. Ask, "How would you know if that hypothesis was false? What evidence would convince you?" to stimulate a creative discussion of possible observations or experiments. Because hypotheses can only be ruled out rather than ruled in, think of predictions that would be inconsistent with each hypothesis. Go through this process for each hypothesis. Being open-minded at this stage is important.

Students benefit from drawing mock graphs that visualize predictions. Encourage students to be explicit about the axes of the graph and how they will be measured. These graphs also provide a framework, or "mental picture," for designing the methodology for the study and guiding future data analyses. Verbal predictions are good starting points (e.g., "I think A will get bigger if B is bigger") and can often then be transformed into graphs appropriate for the study design (e.g., a scatter plot of B vs A).

Predictions are not what you deem likely to happen, but what needs to happen for a hypothesis not to be ruled out; thus, you must have contradictory predictions for the same measurements for the different hypotheses. Making predictions enables students to identify the variables they need to observe or manipulate in their study. Useful predictions can be qualitative (e.g., "If trees cool the ground, then soil temperature under a tree should be lower than in the open") or quantitative (e.g., "If only gravity acts on an object, then the speed of a dropped object will be proportional to the falling time").

Alternatively, students can think about the equipment they have on hand to identify possible variables they could observe or manipulate. They can then use this list to think of possible predictions that link these variables together. Proxies can be used for variables that are difficult to measure (e.g., "playground use" could become "number of students on the swings"). Iterate through this process until students have at least two hypotheses that produce different predicted patterns on the same graph, where the graph includes quantities that are feasible to measure.

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than that of birds landing on trees

## *Grade-Level Differentiation*

#### **Elementary (3–5)**

Students need more guidance about what can be measured and how. Confronting students with problems that are too hard, abstract, or outside of their experience will lead them to lose focus and confidence. You may want to suggest specific study designs in response to student questions. Given a planned results graph, students can often come up with the predictions for each hypothesis. Students may try to guess, or claim to know from background knowledge, what the outcomes will be; remind them that all possible outcomes need to be imagined.

#### **Middle (6–8)**

Students are more able to focus on a problem for longer, and more of the process of figuring out *how* a question can be answered can be studentdriven. Nonetheless, you may want to suggest study designs after students select questions.

#### **High (9–12)**

Creative decisions about what variables should be measured can be assigned to students. Students will likely need support in evaluating various possible study designs and may need clear definitions of what equipment or materials are accessible or what budget can be used.

# *Examples*

*Figure 7.12* A single-hypothesis study (fourth grade). An alternative hypothesis was likely considered but was not stated by the students

*Figure 7.13* A two-hypothesis study (third grade; see Figure 7.8)

*Figure 7.14* A three-hypothesis study (fifth grade)

*Figure 7.15* A five-hypothesis study (middle school)

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*Figure 7.16* A four-hypothesis study (high school)

# **Planning Data Collection**

# **8.1 Choosing How Much to Measure**

#### *Concepts*

**8**

After identifying predictions and variables, you should decide how much work to invest in measuring them. Repeating measurement multiple times increases confidence in the resulting values. This is a critical lesson that should be taken away from any inquiry project: any measurements you make are only estimates of the true value. Making more measurements improves your estimate, no matter where the uncertainties come from. Any one measurement is affected not only by measurement error but also by many factors that are unknown and often unknowable. However, you do not need to know what these factors are, nor do you need to eliminate them; you only need to make multiple measurements and summarize them by taking an average.

For example, suppose you were trying to estimate the average height of all the students in your school. If you measured only a single student, you might (by chance) have chosen a very tall student; taking this as an estimate for the height of students generally would lead you to overestimate how tall students typically are. If you measure three or four students, you will likely (by chance) sample data from some short and tall students, so that your estimate of the average height begins to be more accurate. Eventually, if you sample all of the students in your school, you will arrive at the true average height. However, it is probably not practical to measure every single student in the school, so you will stop sampling at some point and decide that your estimate is a good representation of the true value.

172 DOI: 10.4324/9781003367192-8

Similarly, if you do a randomized controlled trial and only apply the control once and the treatment once, you cannot be sure that any difference between the groups is the result of the treatment and not of one of the many unknown factors that affect each measurement. Sometimes single pieces of extraordinary evidence would be sufficient to reject a hypothesis (e.g., the observation of a single alien would reject an aliens-do-not-exist hypothesis), but these scenarios likely will not arise in student projects.

Determining the appropriate number of repeat measurements (i.e., "sample size") is challenging. In practice, you will probably make the decision based on estimating available time and resources more than statistical considerations. For example, if you only have an hour to make measurements, your sample size will be smaller than if data collection takes place over a week; if you are measuring the height of trees in the schoolyard, you will make fewer measurements than if you are measuring the size of leaves. Additionally, the variability in your measurement (from a variety of unknown factors and errors) is probably larger than you expect. Thus, plan to make as many measurements as time allows.

#### *Implementation*

Confidence in results depends on how you replicate measurements. For example, consider the experiment where you are measuring rock temperatures. You could measure the temperature for different types of rocks, then your conclusion would apply across rock types. Or you could measure the same rock but on different days, for a conclusion that applies over time but not to different rocks. You could even replicate the measurement just on the same rock over a short time for more confidence that the variability does not come from the thermometer or student not measuring consistently. Generally, the wider you spread your measurements (across time, space, type, observers), the more generally applicable your conclusions will be. You typically should not attempt to hold constant any of the factors that lead to varying measurements (e.g., which student does the measuring or at what time you measure)—this will decrease variability but also reduce generalizability.

The accuracy of your overall conclusion thus depends on the number of samples you can get, but it also depends on how evenly or randomly you can spread them out across irrelevant factors. If your rule of picking a particular measurement is in any way biased toward particular outcomes, then your outcomes will be biased and not generalize well. For example, returning to the task of measuring the temperature of rocks, you should try to pick the rocks measured randomly with respect to what temperature they are.

Once a sampling plan has been proposed, ask students to determine how long each measurement will likely take, inclusive of setup and staying focused and travel time (e.g., ten minutes each). You may need to let them try the method or equipment to get a good sense of this. Divide the total time available by this value for the maximum number of possible samples (e.g., 2 hours/15 minutes = 8 measurements at most).

A simple solution is to obtain as many samples as possible in the time available. However, if this number is smaller than the desired number, then encourage students to determine what trade-offs they need to make; for example:

Come to a final proposal after a round of iteration. Large sample sizes may not be possible when each measurement is slow (e.g., a study of soil that requires digging a large hole) or because replicates do not exist (e.g., a study of flower color where there is only one yellow flower in the schoolyard). Do not discourage students when their sample sizes are small.

## *Grade-Level Differentiation*

#### **Elementary (3–5)**

Focus on the overall pattern of results while presenting intrinsic variability in measurements as real, not erroneous.

#### **Middle (6–8)**

Introduce more quantitative concepts, like graphing a frequency distribution. Students may also be able to collect larger sample sizes. Maintain your focus on the overall pattern of results, and present data variation as real, not erroneous.

#### **High (9–12)**

As noted earlier.

#### *Examples*

*Figure 8.1* Consulting a map to determine how many sites can be realistically sampled when walking between them

*Figure 8.2a* Teacher-provided datasheet for making multiple cricket behavior observations (third grade; see Figure 7.8)

*Figure 8.2b* Students observing crickets

# **8.2 Choosing What to Do (Protocols, Checklists, and Datasheets)**

#### *Concepts*

Being organized early on prevents confusion and chaos once outside. Once groups have chosen a question and identified variables, it is useful to plan how those variables will be measured by creating a protocol, a checklist of necessary equipment, and a datasheet for recording observations. A protocol ensures consistent data collection and provides an opportunity to decide on statistical and practical aspects of data collection. It helps students reach a consensus on their roles and the necessary tasks and acts as a reference point for check-ins. The checklist of necessary items helps students use outside time effectively by not forgetting or losing equipment. Making a datasheet ahead of time ensures students have thought carefully about what they need to record.

# *Equity and Inclusion*

There are often many ways to collect data to answer a single question. This means there is much opportunity to develop protocols that maximize student accessibility. Make sure that every student is able to have a meaningful and distinct role in data collection.

# *Implementation*

For a protocol, have students write out a set of numbered steps they will take to carry out their study. Steps should indicate what they are going to do, what they are going to use to do it, how many times it will be done, and what will be done with the collected data (see template in Appendix 1). Have students write down what units of measurement they plan to record and their goal for the number of samples or observations necessary. To ensure the steps of the protocols align with the allotted time, you can ask students to approximate how much time they expect each step will take. Leave room in protocols to make notes about problems and what was done to solve them.

Ask students to review the protocol to determine if it is:

Make changes until all three criteria are met.

After developing a protocol, have students make a checklist of all of the equipment they will need to implement the protocol. Once they know what they will do and what they need to implement the protocol, have students make their own datasheet. This is not a trivial task, as many students may not have made their own table for data collection before. It is helpful to have a header on datasheets where the date, observers, and location can be recorded and to label all variables with units (see template in Appendix 1). Observations can then be recorded in a table with variables arranged as columns and observations as rows. The datasheet itself can be hand-drawn in notebooks or typed on a spreadsheet.

Technology such as tablets or phones may be used to collect data either through direct entry of observations into spreadsheets or by recording digital data (e.g., sounds or photographs). Digital data may be used for quantification or identification (e.g., insects or plants) or simply to document protocols. If students propose to collect digital data, they should include a system for naming and organizing digital files in their protocol.

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

To keep students on task and ensure that recorded information is useful and consistent over time, use a structured datasheet that requires minimal reading and writing. A datasheet makes the data collection plan explicit in all its details. Support will be needed to help translate the plan from the anchor chart into a suitable datasheet.

#### **Middle (6–8)**

Students can take more responsibility in planning the details of their data collection. They can design their own datasheet and discuss what needs to be included. Collaboration between groups can allow for students to provide and receive feedback on their plans.

#### **High (9–12)**

As noted earlier.

# *Examples*

*Figure 8.3* Learning to use a digital weather meter

# **8.3 Choosing Roles**

## *Concepts*

Each member of the team will contribute in different ways. Students should decide in advance who will do what on each team to maximize the effectiveness of outside time and to avoid conflict.

#### **Equity and Inclusion**

Consider having students switch roles regularly to avoid any one person dominating.

#### **Implementation**

Assigning roles can be done as part of the protocol development process. Ask students to identify different roles that might be useful (e.g., someone measures X, someone measures Y). Remind students they also may want to have people responsible for other key roles including:

Depending on group size, some students may have more than one role or share a role. Some roles may seem more exciting than others, so conflict can occur. First, let students pick roles based on their preferences and stated skills. If there is conflict, you can suggest that students rotate between roles or share a role. Alternatively, you can encourage students to take an unwanted role by highlighting its major contribution to team success.

#### *Examples*

*Figure 8.4* Students in self-selected roles, from left to right: tree finder, tape runner, number reader, site describer

*Figure 8.5* Data recorder

*Figure 8.6* Illustrator

# **9 Collecting Data Outside**

# **9.1 Practicing Collecting Data**

# *Concepts*

Students typically have limited prior experience collecting or recording data, using equipment, learning outside, or managing the flow of a project. Confusion, conflict, or disempowerment can occur if students are expected to perform immediately at a high level when carrying out their projects. This can be exacerbated if there is a fixed and small amount of time that students have to be outside (e.g., if you only have bus transport for one day) or if students feel time pressure. To increase student confidence, you should emphasize the importance of working together as a team, learning to use equipment, and following their protocol.

# *Equity and Inclusion*

Ensure all the students who want to learn how to do a particular task get a chance. Interpersonal dynamics within a group during practice are likely to be repeated when outside, so be prepared to use facilitation skills to mitigate any issues early on.

# *Implementation*

During or after the protocol development process, provide training on safe use of equipment, then allow students to play with or practice with the

DOI: 10.4324/9781003367192-9 183

This chapter has been made available under a CC-BY license.

equipment they will ultimately use. Have student teams do a test run of their data collection process. Students can practice in the classroom or the schoolyard, substituting features of these places (if needed) for the features they expect to find in the focal place.

Changes will likely be necessary, so provide time for students to revise their documents. Students often find that their plans are too ambitious or complex. They may need to reduce sample sizes or reduce the number of measurements they make. Set an expectation that iterative refinement is normal.

Before starting, set the norms that mistakes and errors are expected and that practice and iteration are needed to develop data collection skills. You should circulate between groups to facilitate their learning; expect to spend at least a few minutes with each group. This is a key point for you to request and use adult volunteers to provide active support to students.

Expect to spend at least a half hour on practice. This time will be repaid with more efficient use of time later.

## *Grade-Level Differentiation*

#### **Elementary (3–5)**

Students will need more initial support and direction, including team building activities and practicing observation skills.

## *Examples*

*Figure 9.1* Practicing using a quadrat in the schoolyard

*Figure 9.2* Practicing recording data in the classroom

# **9.2 Collecting Data**

#### *Concepts*

Data collection is the heart of a project where students can feel closest to the places they are studying. Providing space and time to collect data can be critical for developing connection to place and confidence in students.

When your students go outside, set clear expectations around organization and structure to ensure that students stay focused and that they follow the plans they have made in the classroom. However, you also should provide sufficient freedom for students to feel they can make the place their own and that they are empowered to collect the data they feel they need.

Apparent failure can occur during data collection (e.g., nothing interesting is observed, the protocol does not work, measurements take too long, or equipment breaks). Encourage students about the value of the inquiry process itself and the value of *all* results; for example, recording a zero to indicate an event was not observed may still be valid and informative. Or redirect students to simpler versions of a question, related questions, or simpler methods. A small step backward can support a bigger step forward.

Serendipity can also occur during data collection. When students make observations or have experiences that are outside the domain of their plans, they may come to new and valuable perspectives. Oftentimes the things you are not looking for are the most interesting. Staying open to the unexpected can be supported by integrating observation through the data collection process and also by communicating a supportive attitude toward surprises.

#### *Implementation*

Set expectations for behavior and ground rules for space and time used before starting work. Use your risk management plan to keep safety in mind (see Appendix 2) and explain the rationale for any safety-related boundaries. It is often helpful to set a schedule for data collection with formalized breaks and check-ins when you will visit each group (e.g., you will visit each group at least once, or students have to reconvene as a large group after 30 minutes). Breaks can be opportunities to regroup and participate in sense of place (Chapter 5) or team building activities (Chapter 6), or to share obstacles and preliminary observations.

Be prepared for some behavior challenges and invest time in addressing them (Chapter 6). Floating between groups is vital in determining which groups have students fulfilling their roles, which are comfortable, and which are collecting data. Investing further time in team building and sense-ofplace activities can be a good investment immediately before data collection so that teams function smoothly and with motivation for being outside.

Ask students to use notebooks to keep data records, following the template datasheet they developed with their protocols. Additionally, encourage students to document all their observations during their study, especially for surprising data points or to documents interesting observations beyond the inquiry question or study design. If collecting digital data (e.g., photographs, sound recordings), students can use their notebooks or datasheets to keep track of file names and content.

Have a system to keep track of equipment. Use checklists to determine whether all equipment has been accounted for before and after going outside, do a "roll call" of items and ask members of each group to hold up items as called, or make individuals responsible for particular items.

Common problems to be aware of during data collection include:

• **Lost equipment**—It is very easy for students to misplace equipment. Use brightly colored equipment or use brightly colored tape to mark your tools. It can also be helpful to package equipment in backpacks or plastic tubs.

Some common data recording techniques include:

Common sampling techniques include:

• **Transect**—Pick a fixed sample length (e.g., a 10 m line) and walk along it. Count all objects within a certain distance from the line (e.g., 0.5 m). Then divide the number of objects by the length and width of the transect line (here, 10 × (2 × 0.5) = 10 m2), and multiply by the total area of the study area to get an estimate of the total count. If you do not have a measuring tape, you can use a rope to accomplish the same goal. Tying knots in the rope or drawing tick marks with a permanent marker at regular intervals allows counting distances. Student body lengths are also useful length units that can later be converted into standard units.

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

Use short, structured intervals of data collection interspersed with checkins, interpretation, or question-answer activities. Groups can also regularly rotate between the areas if enough adults are present.

## *Examples*

*Figure 9.3* Using a whiteboard for group data recording

*Figure 9.4* Measuring flow speed by floating leaves down a stream and timing their progress

*Figure 9.5* Safely measuring scorpion length in a plastic tub

*Figure 9.6* Collecting stream macroinvertebrates in ice cube trays

*Figure 9.7* Measuring soil temperature with a meat thermometer within a quadrat

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*Figure 9.8* Storing soil samples in plastic bags

*Figure 9.9* Setting up a vegetation transect with a tape

*Figure 9.10* Identifying species by morphoname

# **9.3 Securing Data**

#### *Concepts*

Data are not always ready for analysis and interpretation by a group because they may exist only in one student's notebook or contain errors. It is important to copy the data to a safe and shared location, then carry out quality checks before using it. During this process, it may become clear that additional data collection or changes in protocol are needed. Analyses are only as good as the data used to carry them out.

#### *Implementation*

Find a quiet area where students will be comfortable. Then copy all the data to a common and safe location. Students can use a whiteboard or poster paper to copy information. If useful, one student can read out data and another can transcribe it. If computers are available, use a shared spreadsheet file or shared folder.

Ensure you keep a copy of the raw data or notebooks in case students need to revisit them later. This might involve photocopying datasheets, collecting notebooks, or sharing photographs of datasheets using a phone camera.

Next, have students organize the data. If an anchor chart was used (Appendix 1), there should already be a natural structure for the data. If not, ask students to create one by asking them:

After the data are organized, have students check the data for common errors. Ask students to check the following items:

If errors are discovered, ask students to discuss the pros and cons of whether they should collect additional or replacement data, proceed after removing the erroneous data, or try to correct the data they do have. Key points to consider include time and resources, as well as the potential impact on the analyses. "Outlier" data points (very different from the rest) should be removed only if there is a clear rationale (e.g., student mistake while measuring) beyond dislike for the data point.

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

Collect notebooks after data collection to avoid data being lost or destroyed. Providing additional structure for data entry (Appendix 1) can be useful.

#### **Middle (6–8)**

Spreadsheet/computer use is more feasible.

#### **High (9–12)**

As noted earlier.

#### *Examples*

*Figure 9.11* Preventing rain damage to datasheets using an umbrella and box clipboard

*Figure 9.12* Teacher copying data to a whiteboard for sharing among students, then providing guidance on data analysis

*Figure 9.13* Combining multiple students' individual notes into a team dataset (middle school)

*Figure 9.14* Calculating biodiversity metrics from data for later analysis (high school)

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*Figure 9.16* Teacher-generated graph of student data to support later analysis (middle school)

*Figure 9.17* A dataset with an outlier point that students decided to not remove

# **10 Analyzing and Sense-Making**

# **10.1 Drawing Conclusions from Data**

#### *Concepts*

For descriptive projects, a conclusion can be made directly from the data (or visualization). Description is a process that reduces the complexity of the natural world into a dataset and, from there, reduces those data into a simpler summary. Description, therefore, loses information but gains interpretability.

Description helps students understand the world as it is (rather than how they may have thought it was), which is critical when intuition may be absent or misleading. Description also helps students build their relationship to place—both because they have now taken an action and found out something new about their home and also because descriptive data are always local and may reflect a reality different from similar phenomena in other places read about in textbooks.

Descriptive summaries of data can be quantitative and include summary statistics (e.g., mean + standard deviation + range) or graphs (e.g., bar plots, scatter plots). Summaries can also include narration (e.g., written, oral), collage (e.g., photo boards, edited video/audio, grouped sets of physical objects), or use other qualitative methods. Encouraging students to explore a variety of modalities to describe and summarize their findings will support a more intuitive understanding not only of the subject of the exploration but of mathematical concepts and presentation methods.

For hypothesis-driven projects, the process of making conclusions uses strong inference (Chapter 7) and the anchor chart (Appendix 1): your data either match or do not match each of the predefined predictions. Any hypotheses whose predictions do not match the data are rejected. If the hypotheses and predictions were set up as both contradictory to each other and covering all possible outcomes, some will always be rejected. You can then accept the hypothesis remaining as the correct answer. If multiple hypotheses remain (i.e., are not rejected), they are all potentially true answers, and the current study is insufficient to distinguish between them. Rejecting any hypotheses that did not correctly predict what the data would look like is a useful way to reduce the human tendency toward prejudice.

It can be unintuitive that positive conclusions can only be made by negative actions (rejecting alternative hypotheses). However, this is one of the most valuable learning outcomes: supporting a conclusion can only be done by first demonstrating that alternative explanations are wrong. This, in turn, requires listening to the data, whatever they may show.

Making an inference (coming to a conclusion) requires comparing the data to the predictions of the hypotheses. Recall that "prediction" does not mean "best guess"; instead it means "what would have to happen if the hypothesis were true." Recall also that you need to have at least two contradictory predictions for this process to work. In school projects, this comparison is primarily done via graphical interpretation. You draw a graph representing some feature(s) of the data and visually compare this graph to your verbal or graphical predictions on the anchor chart. Graphs that do not match a hypothesis's prediction reject that hypothesis. Graphing data is a valuable skill; the procedure trains students to interpret data in ways that allow them to arrive at conclusions by continuing the reduction process from reality to data to interpretation.

Data are never a perfect representation of reality. Measurements will always deviate by varying amounts. These deviations are usually not errors; rather, every measurement is affected by many factors (e.g., rock temperature affected by sunlight and wind speed). There is no hope of knowing or measuring all of the factors that influence the focal measurements. Instead, you can estimate the overall tendency in the data via repeated measurements. In this approach you let all of the factors vary randomly except for the one focal factor (e.g., measuring air temperature repeatedly, in sun and shade), then average out the effects of the unmeasured factors.

However, even when summarizing repeated measurements, the summaries may be subject to fluctuations based on variations in individual measurements. Inferential statistics, or "statistical tests," are tools that allow you to determine the level of uncertainty or confidence that is associated with any interpretation of averages (or other summaries of data). They give you quantitative ways to determine whether or not two graphs match and to determine your confidence in whether the data reject a given hypothesis (see Section 10.3). You may not arrive at a clear conclusion, or you may conclude something with limited confidence. This is a feature of reality and a key learning outcome for students.

Uncertainty in conclusions can also come from hypotheses and predictions that do not cover all possibilities. If all, or not enough, of your hypotheses have been rejected, then other hypotheses should have been considered. Or a different method for hypothesis testing (i.e., making additional predictions about other variables) should have been used. Careful study design can make such outcomes unlikely, but in practice, it does not prevent them altogether. If additional hypotheses are proposed in hindsight (perhaps due to new observations made during the study), these should be seen as important exploratory insights, even if they have not yet been tested by the study.

Data are necessarily place-based. This is both a strength and a weakness: they answer a question in one place and time, in a way that science done elsewhere cannot, but the answer cannot easily be generalized. Generalization only comes from many studies performed in many situations, and thus from weighing the evidence from each individual study against existing knowledge and other similar studies. Embracing the limits and specificity of knowledge is a better approach than excessive generalization.

## *Equity and Inclusion*

For data that arise from the direct human experience of the world, there may always be aspects that cannot be simplified or summarized (e.g., the feeling of meeting an animal for the first time). Be careful to also value the work of students whose identities place a higher value on direct human experience than numbers, and make space for conclusions and presentations that support this type of description.

#### *Implementation*

For descriptive projects, encourage students to describe any discoveries they made both qualitatively and quantitatively. Qualitative descriptions may include observations of new animals or plants or behaviors, or other phenomena, like interactions between weather and animal behavior. Many qualitative observations can lead to new questions or new hypotheses for later projects and represent an important step for scientific discovery. Quantitative descriptions may include tables or graphs.

Both quantitative and qualitative descriptions are informative. If the question is "How many ants visit the schoolyard?," a good descriptive answer might be "More than we could count, and we also found some spiders we did not know about," or "Approximately 300 ants each day."

For hypothesis-driven projects, have students refamiliarize themselves with their anchor chart. As noted earlier, the chart should identify the necessary graph types already, so students should be able to make them. You may want to divide students into subgroups to make different graphs.

After undertaking this process, have students discuss which hypotheses are unlikely given the data and need to be rejected and which are not. The outcome of this discussion is the conclusion.

Students may be tempted to argue for a particular hypothesis or answer for reasons beyond the data. Limit discussion to what the data themselves indicate. You can ask students, "Is that idea supported by your data, or is it your opinion?" Opinions and speculation are also valuable but are better integrated at the reflection stage. Focus first on the facts; then you can expand to your opinions about these facts.

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

Use simpler graph types that use observations as directly as possible, with little intervening calculations. Skip statistical tests. It is reasonable to reject hypotheses based on the visual interpretation of graphs. The concept of quantifying uncertainty will be difficult. But understanding measurement variability (even for "the same thing") is important and possible with repeated direct experience and illustration of such variability (e.g., graphing all measurements and not only graphing averages).

#### **Middle (6–8)**

More complex graphs and relationships between measurements can be used. Skip statistical tests. Include variation as well as statistical summaries in graphs (e.g., a box-and-whisker plot in addition to plotting individual measurements).

#### **High (9–12)**

Consider introducing statistical tests. The results of students' projects may or may not agree with what a textbook says about the same question. You can discuss such discrepancies, but do not automatically dismiss the students' result as wrong: their results just may not generalize broadly.

#### *Examples*

*Figure 10.1* Concluding that a hypothesis is supported (a study of insect diversity, fourth grade)

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*Figure 10.2* Concluding that a hypothesis is rejected but other descriptive findings were interesting (a study of fire impacts on forests)

*Figure 10.3* Concluding that a hypothesis is supported but confidence is limited (a study of tree regeneration)

*Figure 10.4* Concluding that a hypothesis is rejected (a study of gopher dietary preferences)

*Figure 10.5* Concluding a hypothesis is supported (a study of plant pigmentation)

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# **10.2 Visually Interpreting Results**

#### *Concepts*

Graphing data makes numbers easier to interpret. Graphing each measurement as a data point visualizes the range of variation in the data and the amount of overlap between groups of measurements. This information helps determine your level of confidence in a conclusion.

Descriptive statistics can be used to make graphs summarizing data via averages, medians, standard deviations (a measure of variation), or other metrics. They are useful but always represent a loss of information. If you do not have a large number of values, you may be better off just focusing on visually interpreting the original numbers instead. In a descriptive project, interpretation of the graph is sufficient to come to a conclusion.

In a hypothesis-driven project, you are comparing the measurements against at least two qualitatively different, previously formulated possibilities. You will also already have decided on a graph type in your study design (Chapter 7). You can interpret each graph by deciding whether it "looks different from prediction A" (rejects the hypothesis), "looks like prediction A" (does not reject the hypothesis), or you are "not sure" (unclear conclusion) (see Section 10.2). There is no exact rule for making this decision, though statistical tests (see Section 10.3) can help. Interpreting available evidence always requires human judgment.

Do not be afraid of concluding that you are not sure; this is likely with small numbers of measurements. Using statistical tests will give you a clear decision criterion but still often lead to the same conclusion that there is not sufficient information to reject a hypothesis. In these cases, either more data or a different study are needed to answer the question.

## *Equity and Inclusion*

Students who are colorblind may not be able to interpret graphs that differentiate data with certain colors. Encourage students to use alternate color pairings, even if you are not aware of any colorblind students, to support sharing data with broader communities. Replacing red-green palettes with blue-yellow palettes will help the majority of people.

Students who are visually impaired may have difficulty creating or interpreting graphs on paper or on the computer. Students also can interpret data tactually as well as visually. If using paper, consider making graphs that have raised surfaces to indicate axes and data points (e.g., corkboard with pinned-on cardboard, buttons, pipe cleaners, or 3D-printed surfaces). Accessible graphs also can be created using tactile graph sheets/kits or graphic aids (Maguvhe, 2015; Moon et al., 2012). Both are commercially available.

When sharing computer graphs, visual elements should also be alt-tagged with appropriate text descriptions. If using computers, ensure students have access to screen reader software. Additionally, consider obtaining access to data sonification programs that convert numbers and graphs to sounds or music and permit audio interpretation of data. Free options for both can be found via an Internet search.

#### *Implementation*

Consider having students draw graphs on paper by hand. Doing so gives students—especially at younger grade levels—the chance to grapple with numbers and all the parts of a graph and to practice numerical and algebraic skills in manipulating numbers. Drawing graphs by hand also provides extra opportunities to practice fine motor skills and mark out measured intervals on axes. Letting each student add their measurements to a group graph can help engage everyone in the graphing process. Some graphs will take a few iterations to get right.

Alternatively, you can use computers and other digital tools to assist with graphing. Computer graphs free up student time to think about other aspects of the project and build digital literacy. However, if less than one computer or device per student is available, making graphs by hand may enable more students to engage with the process. If you decide to have students share a computer to make graphs, try to assign different graphs or parts of the analysis to different students, or encourage a process where one student types or uses the mouse and another student directs, to enable all students to actively engage and contribute to the software use. Spreadsheet software can generate bar plots and scatter plots; however, it is harder to find software that produces box plots or strip plots. More advanced free graphing programs can be found via an Internet search.

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

Strip plots or bar plots are most intuitive. You may have to help students translate measurements to positions on graph axes. If you can, introduce the same graph type on smaller datasets before embarking on a longer inquiry project (e.g., small surveys in the classroom, such as, "What is your favorite ice cream flavor?"). You can also prepare a blank graph for students to add data/labels to.

#### **Middle (6–8)**

Two-dimensional scatter plots can be used. Summary statistics can be introduced, allowing you to graph in box plots; individual measurements also can be graphed on such plots. Pie plots can be used to practice fractions.

#### **High (9–12)**

Highlight variation in measurements (e.g., by including all data points in a strip plot or by using a box plot). If you are also including statistical tests (see Section 10.3), encourage discussion about the pros and cons of each approach.

# *Examples*

*Figure 10.6* Visually interpreting a frequency plot to conclude that cricket behavior does vary among individuals; see Figure 7.8

*Figure 10.7* Visually inspecting a strip plot/bar plot with medians to conclude that watering does increase seedling growth; see Figure 7.7

**Plant height variation:** Elementary students wanted to know why plants in their schoolyard varied in height and tested the hypotheses that "Plants grow better with regular water availability" and "Water availability plays no role in plant growth." The students used a randomized controlled trial study design. Ten seeds from a schoolyard plant were placed in ten small pots and either watered once at the beginning of the study or every day for a week. On Day 7, students measured the length of seedlings with a ruler. One pot was accidentally destroyed. The students graphed "Watering scheme" (X) and "Growth of seedlings" (Y). They graphed each data point (seedling) as well as the median (bar height). The median for the daily watered plants is much higher, but the spread of the data points overlaps. They could potentially reject the hypothesis that watering plays no role (based on the higher median), but also admit that there is substantial uncertainty in this conclusion.

**Fuchsia flowers:** Middle school students were curious about the showy flowers on California fuchsia bushes growing in their schoolyard. They tested the hypotheses that (1) more sunlight would lead to more reproduction through more investment in flower parts and (2) that the response would be stronger for female flowers because their eventual fruits are dispersed better if they are visible. They selected male and female flowers in sites with differing light intensities, then measured flower part masses using a cross-sectional study design. They graphed flower mass (Y) and light level (X), coloring data by flower sex, on a scatter plot. They concluded that the female part of the flower (carpel) was heavier but also that it responded more to increased

*Figure 10.8* Visually interpreting a scatter plot to support the hypotheses that flower mass increases with sunlight and that females respond more strongly than males

sunlight (steeper slope of the line) than the male part of the flower (stamen). Because the data are close to the line, it is unlikely that there is truly no relationship between the X and Y variables. The students concluded that both hypotheses were supported, compared to the implicit alternatives that sunlight did not affect investment in flower parts or that both sexes would respond equally.

**Fire effects:** High school students investigated the effects of fire on different species of trees in Arizona's Catalina Mountains. They first performed a descriptive study, counting the number of trees of different species in a defined area of burned and unburned areas. Using a pie plot, they discovered that primarily ponderosa pine and Douglas fir seemed to have survived in burned areas. The students then asked two separate questions, using a cross-sectional study design, where they visited burned and unburned areas and measured tree properties in each. First, they asked if the thickness of bark would determine whether trees can survive a forest fire, hypothesizing that thicker bark would help (with the alternative that it would not, because other traits determine fire survival). This led to the predictions that ponderosa pine and Douglas fir would have thicker bark than other trees (or not). Measuring bark thickness for all species in the unburned area and plotting it on a bar plot (average bark thickness [Y] and species [X]), they found taller bars for the two species that survived best in burned areas. However, one other species (aspen) also had relatively thick bark. The students concluded that there was support for the first hypothesis, but they could not accept it with high confidence. Second, they asked whether bigger trees might be able to better survive forest fires. They hypothesized that trees in burnt areas should have larger sizes, or alternatively, that they would not because other traits determine fire survival. They measured circumference for individual trees of the two focal species that survived best in the burned area. They plotted tree circumference (Y) and area (X) using a box plot. Using visual interpretation, they found trees actually were bigger in unburned areas. They therefore rejected the first hypothesis with high confidence. They speculated about fire recovery as an alternative explanation.

*Figure 10.9* Describing variation in forest composition (top left), visually interpreting bar plots to support a hypothesis about bark thickness among species (top right), and visually interpreting box plots to reject a hypothesis about fire impacts on size (bottom)

*Figure 10.10* Visual interpretation yields an unclear conclusion about animal hole size on and off a path (averages are similar but ranges are large)

*Figure 10.11* Tactile interpretation of a bar plot using sticky tape for data and embossing (from a braille machine) for axes

# **10.3 Statistical Tests**

#### *Concepts*

If you want to go further in the analysis of your results than visual interpretation allows, you can introduce students to inferential statistics. Inferential statistics help to compare data to predictions by quantifying the uncertainty associated with different conclusions. This process formalizes the graph interpretation process by giving objective criteria for determining when a hypothesis should be rejected.

In a statistical test, you declare a default or "null" hypothesis and decide that you will accept this null hypothesis in all cases except when there is strong evidence against it. Most tests calculate a p-value, which is the probability that the measurements look as they did (or more extreme) if the null hypothesis is true. Standard practice is to accept the null hypothesis unless p < 5% (i.e., p < 0.05); in other words, you want to ensure that you have no more than a 5% chance of error of accepting the alternative hypothesis when this is in fact not true (i.e., you want no more than a 5% chance of false-positive errors; see the table below).

A t-test or analysis of variance (ANOVA) can be used to compare the difference in measurements between groups. These tests calculate a p-value for the null hypothesis that the difference between the means of two groups is zero (t-test) or that the difference in means among three or more groups is zero (ANOVA). A linear regression can be used to determine if a relationship between two continuous variables exists or not. Regressions estimate a line of best fit (with a slope and intercept). They typically calculate a p-value for a null hypothesis that there is no relationship (the slope is zero).

These tests do not tell you how likely it is that the null hypothesis, or any other hypothesis, is true. "Accepting the null hypothesis" here means simply that you act as if the null hypothesis is true and the alternative hypothesis is false; you may realize that there is considerable uncertainty associated with this inference but consider the available evidence insufficient to accept the alternative hypothesis.

No test or dataset is perfect, and statistical error may occur. Different error types are shown in the above table. In a real study, you only know your conclusion (whether you are in the first or second row); you do not know whether or not you are making an error (whether you are in the first or second column). In any decision, changing your decision strategy can only decrease one error type by increasing the other (e.g., decrease the probability of false positives by increasing the probability of false negatives, or vice versa). However, increasing the sample size (number of measurements) is a straightforward way to reduce the chance of false negatives.

You should also consider not only whether something happens but how often or how large its effect is. It only makes sense to talk about effect size if you are reasonably convinced that there is an effect (i.e., you have visually or statistically concluded that the treatments are different). For comparison between categories (e.g., bar or strip plots with t-tests), the difference in means (height of bars) is often used as a metric of effect size. For continuous variables (e.g., a scatter plot with a linear regression), the R2 metric is commonly used.

Statistical tests are often misunderstood. You can skip statistical tests unless you feel comfortable with the arguments presented earlier. If you do not have a lot of data, applying statistical tests may not add much insight for students and may make the conclusions harder to understand.

# *Equity and Inclusion*

Consider access if you are asking students to produce graphs on a computer. You should not ask students to complete complex or novel tasks outside of school time. You cannot assume either the ready availability of computing devices or Internet access in all students' homes. Computing tasks are best performed at school, on devices that have been provided, and where you can ensure that each student or small group gets an equitable share of time to use available devices.

# *Implementation*

If students have access to a computer, spreadsheet software will allow simple graphing of data and basic statistical tests. Alternatively, if you have Internet access, many websites carry out these tests directly after you type or paste in data (e.g., search for t-test calculator).

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

Focus on visual interpretation of results. Descriptive statistics can be introduced.

#### **Middle (6–8)**

Confidence and error types/consequences can be introduced. Visual interpretation of results is still preferred.

#### **High (9–12)**

Statistical tests can be introduced.

# *Examples*

**Plant height variation:** In Figure 10.2, the null hypothesis could be that watering does not affect plant growth, meaning that the two treatments (watered vs. not watered) should have similar measurements. A t-test applied to these data shows that the probability of getting an observed difference in means as big or bigger if the null hypothesis were true is p = 8%. Thus, the null hypothesis (that there is no difference in plant growth between the watered and unwatered treatments) would not be rejected (because p > 5%). Even though the observed data may appear to differ among treatments, the t-test indicates that you cannot be sure that there was an effect of treatment.

**Cactus spines:** Barrel cacti are common in the Sonoran Desert. They have clusters of spines on all sides that shield them from herbivores and also shade their photosynthetic surfaces. Students were curious about whether cacti grew spines at different densities depending on sun intensity and shading. They used a cross-sectional study design. They selected several cacti, measured the amount of shade they experienced during a day, and also measured the number of spine clusters. They made a scatter plot (X, number of spine clusters; Y, % shade). They used a statistics program to do a linear regression, which calculated that p = 0.08, R2 = 0.19, and y = 0.04x + 8.8. Finding p > 0.05 means that they cannot reject the null hypothesis, namely that the number of spine clusters does not change with the amount of shade. Even if the p-value were smaller, the factor 0.04 before x indicates that the slope of the regression line is low, that is, the best estimate would be that only one spine cluster is added for every 25% more shade (1 = 0.04 × 25). The R2 value is also somewhat low, indicating that if shade affects spine clusters at all, it only explains about 19% of the variation in spine cluster number.

*Figure 10.12* Failing to reject a hypothesis that spine cluster counts and shade level are not associated, using linear regression, even though the slope appears positive - i.e., the conclusion is no effect of shade

*Figure 10.13* Failing to reject a hypothesis that ant food retrieval speed is the same among site pairs, using t-tests, even though the group medians appear to differ. An ANOVA could also be used. Thus the conclusion is that retrieval speed is the same across sites

*Figure 10.14* Rejecting the hypothesis that there is no difference in the number of bird species seen or heard in the forest, using a t-test (bottom right); thus concluding that there is a difference in bird species seen and heard

# **Reflecting and Recognizing Success**

# **11.1 Reflection**

#### *Concepts*

**11**

Each project contributes toward broader knowledge and sits within broader societal values and structures. After completing their projects, students should reflect on their findings and consider the relevance to their places, communities, and futures. Additionally, students should develop explanations for their results that enable sense-making and connection to broader scientific ideas (Braaten & Windschitl, 2011).

Reflection around findings is important. Findings should be connected to the Disciplinary Core Ideas and Crosscutting Concepts relevant to the overall curriculum (Schwarz et al., 2017; Windschitl et al., 2018). Students should explain their findings and contextualize them relative to their current knowledge, helping them connect their projects to classroom learning. What students discover may be surprising, interesting, or challenging to prior beliefs/knowledge, thus helping them develop their identity as learners and scientists.

Reflection around the scientific inquiry process is important. Findings should be connected to the Science and Engineering Practices to help students explicitly recognize the skills they are developing and the tools that they are using to answer their questions (e.g., engaging in argument from evidence). Process reflection helps students recognize how many subjective decisions they had to make in the design/implementation of their project and how hard it is to obtain trustworthy data.

#### 218 DOI: 10.4324/9781003367192-11 This chapter has been made available under a CC-BY license.

Reflection around identity and peers is important. Students can come to see themselves or their peers in new ways. They can celebrate how they have worked collaboratively, learned new things about each other, and handled conflict when it arose.

Reflection around place relationships is important. Students can come to see familiar places in new ways and gain greater humility and empathy for the communities of people and organisms that use these places. Students may have discovered the uniqueness and value of their place for the first time and be able to contrast this perspective with potentially negative ones they were exposed to by other people, in books, and in media.

Reflection around justice is also important. Students can synthesize the conclusions they have made, and the place connections they have experienced, to consider whether these connections are fair and how these connections might change. They can also reflect on their own roles and responsibilities in the community and the place.

# *Equity and Inclusion*

Reflection is a key time to encourage students to think critically about place relationships and decolonization. You can open conversations around why you and your students were able to do their projects in the place and who should inhabit or take care of this land (if not the current inhabitants). You can also discuss what it means to own land and what the differences are between owning, renting, and caring for land (Kimmerer, 2013; Zinn, 2015). Holding these conversations requires you to have some level of background knowledge (e.g., readings like the references provided in Chapter 2).

## *Implementation*

Allow students to reflect individually, then share one by one in a team setting. Students may come to different reflections. You can have students write thoughts down first, and then allow each student to share to avoid having responses dominated by whoever speaks first; or have all students write something on the whiteboard (more or less simultaneously), and then review what is written there as a group. Enumerating all reflections and then allowing students time to discuss is a fairer process and produces a broader set of thoughtful points.

The following questions can be used to structure reflection. Some questions focus on subjective experiences, and others can be used to support students in aligning their thinking to Next Generation Science Standards (NGSS) principles.

#### **Reflection around findings:**

#### **Reflection around process:**

#### **Reflection around self/peers:**

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#### **Reflection around place relationships:**

#### **Reflection around justice:**

Reflection provides an opportunity to use student responses to recognize student success, plan further inquiry projects, or develop community or activism-focused work.

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

Reflection can become too abstract and removed from the action or event you are reflecting on. It may be more useful to insert short reflection sessions throughout the inquiry project and its presentation to others. It may be most effective to pick one of the Crosscutting Concepts to focus on throughout the project and to reconnect student thinking to this concept repeatedly and at the end. For example, you can integrate discussions of scale throughout, from planning and executing measurements, to graphing axes and effect sizes; in the reflection phase, you may then encourage students to discuss how changing the scale of measurements or the scale of effects would have changed the project and its outcomes.

#### **Middle (6–8)**

Students are able to start reflecting on the overall project and the impact of their own actions and the scientific process. Encourage discussion on your preferred aspect of reflection and direct their attention to the role of Crosscutting Concepts in this topic. For example, if you choose to focus on place relationships, you can connect these to systems thinking by discussing the multiple ways in which humans, landscapes, and ecosystems interact and how this was reflected in your specific project(s).

#### **High (9–12)**

You can spend more time on reflection and include more self-awareness (i.e., reflection around process, relationships, and justice). You may also be able to include more actions as a result (e.g., follow-up projects or activism). Students may be able to reflect more deeply and independently on several Crosscutting Concepts and their role in the execution and interpretation of their project.

#### *Examples*

*Figure 11.1* Sitting outside in a large circle supports sharing reflections

# **11.2 Recognition**

# *Concepts*

Recognition of success and growth helps to close out a project. Recognition helps reinforce the idea that science is conducted in a community of people who participate in and gain from the process in different, and often complementary, ways. Recognition also helps students understand how their success has been supported by the place they studied, further building place relationships.

# *Equity and Inclusion*

Set the expectation that every student gets recognized at least once. Implicit student biases may otherwise cause peers in marginalized groups to be less likely to receive positive feedback. Avoid structures that can let some students get skipped.

# *Implementation*

Recognizing success can follow, or be integrated with, reflection. It differs by focusing on celebration. Within small groups, have each student identify:

You can also have students synthesize their experience by asking them to share:

Recognitions can be shared within small groups. In circles, students can go in order or "popcorn"—each student shares, then chooses another student until everyone has been chosen. Alternatively students can make their recognitions individually (e.g., on paper plates that can be given as "awards" to peers, on sticky notes that can be put on a shared poster, or on a shared digital platform).

You can also share your own recognitions with students as long as you share equally with all groups and/or students. Consider answering one of the earlier questions for each group in a whole-group or whole-classroom setting.

## *Grade-Level Differentiation*

#### **Elementary (3–5)**

Keep questions simpler and recognition time shorter. Structure the activity to ensure that it is focused on celebration (e.g., ask about their favorite part of the project). Students may benefit from receiving a physical object symbolizing their recognition.

#### **Middle (6–8)**

Students may worry about favoritism, so focus on celebration and do not give selective praise.

#### **High (9–12)**

Longer reflection and individually directed feedback are possible.

#### *Examples*

*Figure 11.2* Students celebrating their successful project

# **Sharing Outcomes**

# **12.1 How and Why to Share Projects**

#### *Concepts*

**12**

Sharing projects helps students recall and synthesize information, builds communication skills, and helps them feel that their efforts are valuable and recognized. Sharing also builds relationships between students, teachers, and the community. Sharing benefits the community by disseminating knowledge and generating or reinforcing a shared sense of place. Sharing can also benefit the place and the broader community by building awareness and calling for action.

Sharing typically occurs via student presentations, which may be individual, group, or whole-class products. Presentations are not only posters or slide presentations; any format that reaches its audience is appropriate. Depending on the interests and availability of the audience and the presenters, presentations can be short and include only major conclusions or implications of the project, or long, with a detailed report of how the inquiry project was performed.

Physical artifacts that relate to the project can enhance presentations. For example, a bar plot summarizing the number of leaves found in different places could be made of leaves glued onto paper. A team that studied rock hardness could bring some cracked-open rocks to pass out to peers. Students could create models or dioramas, possibly with natural materials. Similarly, recorded sounds or video (e.g., recorded via smartphone) could be used to share study sites, methods, or stories about team members.

# *Equity and Inclusion*

Be mindful of what you expect students to accomplish when sharing their project outside the classroom, especially if it may involve special equipment (e.g., computers, phones), infrastructure (e.g., Internet access), or parent/ guardian support (e.g., purchasing poster boards). Supplies, equipment, and infrastructure should be provided at school. Select modes of sharing that require minimal investment if resources are lacking at school.

Presenting at science fairs allows students to showcase their work widely. Place-based projects are not common at science fairs, so event organizers may not provide templates or entry categories that match your projects. You should still encourage students to present their work in their preferred format. You may need to contact organizers in advance. Like all events taking place outside of the school building and hours, reduce barriers (e.g., transportation costs) for students as much as possible and leave participation optional.

#### *Implementation*

Set clear expectations that sharing outcomes is a key part of the inquiry process. Decide (potentially with your students) who the projects should be shared with. At a minimum, projects should be shared with your classroom among multiple student teams. Sharing with the school community and parents should also be possible at low or no cost and will bring increased benefits for the students, yourself, and your community. It may also be possible to share with broader community members, or with landowners/rightsholders and other partners. You should also ask your students if there are particular groups of people they feel it would be important to share their projects with.

Decide with your students what format for sharing would be effective and logistically feasible for your selected audience. Using a diversity of presentation formats can provide students multiple ways of demonstrating their engagement with the learning process and also be more interesting for other students. Possible presentation modes include:

Plan to archive the presentation in some way (e.g., by keeping physical artifacts or a video recording). You can share recordings later on and can give future access to students examples of past student work, to provide examples of high-quality projects that can be achieved

#### *Grade-Level Differentiation*

#### **Elementary (3–5)**

Integrate sharing throughout the project. Letters to parents/guardians and show-and-tell are effective. Simple posters summarizing methods and results are possible.

#### **Middle (6–8)**

Students can use more freedom to choose among presentation modes.

#### **High (9–12)**

If time allows, students can develop multiple forms of presentations targeted to different audiences and purposes.

228

# *Examples*

*Figure 12.1* Sharing poster presentations in the classroom

*Figure 12.2* Dramatizing a project through a skit; here, a scientist (center) observing a squirrel (right) approaching a tree (left)

*Figure 12.3* Drawing comics to show project methods

*Figure 12.4* Sharing a project at a regional science fair

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*Figure 12.5* Recording video of student presentations for distribution to family

*Figure 12.6* Writing a blog post for Internet distribution

# **12.2 Supporting Presentation Development**

#### *Concepts*

Making a presentation is like telling a story. A good story creates a connection with its audience; has a clear beginning, middle, and end; and creates a tension that is then resolved. That is, a good presentation is about emotion and place connection as much as it is about facts.

One way to tell a story is via an hourglass form. Hourglass presentations start "wide," drawing the audience in with the motivation for their inquiry: a pressing problem in the community, a notable observation, or a personal experience that motivated curiosity. The presentation "narrows" as it focuses on the students' particular experimental design: a specific inquiry question (and hypotheses if present), methods, and quantitative results. The presentation ends "wide" via a general conclusion based on strong inference, a reflection on the implications of their results for the place and the community, and/or their own personal growth.

You can modify this form or adopt others that are more culturally relevant to your students. For example, students may make an infographic to publish in a school newsletter, or they may audio-record each other's recollections to share with another classroom.

 Keep focus on the students as much as on the end product. All students should participate in some way in the presentation, though the modality of their contribution may vary within a team (e.g., one student may make art while another may speak). The method that is most accessible for a student to demonstrate understanding is the one that should be encouraged. You should help students feel valued and respected, not create uncertainty and anxiety.

## *Implementation*

Start with the audience in mind. Ask students to visualize specific people as an audience (e.g., a friend in another classroom, a grandparent). Ask students to imagine that they are helping that person understand what their inquiry project was about. Thinking of specific people also helps maintain motivation at the end of the project. Ask students: What would you say to [this person] about why you were interested in this question? What different answers were you trying to decide between? What would [this person] find funny, or interesting, or relevant to their life about your project or results? How were you able to tell which answer was the right one? Who is affected by the answer, and who should care? Answering these questions via a brainstorming session similar to the question generation/winnowing process can be useful.

Once students have a story to tell and they have decided on a presentation mode, they can work together to assign individual responsibility for different components of the presentation. Components may include not only writing different sections, drawing illustrations, and preparing media or recording equipment, but also planning a live event, inviting an audience, or managing dissemination of the product. Be prepared for conflict and/or confusion to arise at this stage. Investing further time in team building can be helpful. You can also float between groups or require that teams check in with you to share their plan and foreseen obstacles before proceeding.

If possible, allocate time for iteration. Drafting and then improving work is often initially difficult for students. Explain to students that presentations should reflect their best work and that work can always be improved with feedback. Practicing presentations to peer groups can yield supportive and constructive feedback.

Provide a rubric and examples of what makes a good presentation (or, alternatively, what makes a bad presentation). This will allow students to understand the level of work and detail that is expected of their presentation, which is especially important if you are allowing students to freely choose a presentation mode. Some modes may require more effort than others; some may be more or less suitable for conveying key information. All presentations should be equally effective at engaging an audience and communicating the work that has been accomplished. Sample rubrics are provided in Appendix 3.

Posters or digital slideshows are valuable, especially if students are presenting at a science fair. In this case, having one student in charge of poster/slideshow layout while others take charge of writing text, drawing graphs, or making illustrations can be helpful. Plan to use more space and less text than you and your students originally think is needed. Consider using large illustrations and/or photos. Avoid large blocks of small text.

For physical posters, students should use pencils to sketch a layout before committing to markers, glue, or other permanent materials.

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

It is often difficult for students to imagine others not knowing what they know, making presentation development difficult. You can replace a final presentation with more frequent mini-reports throughout the project. Writing and drawing skills will also vary widely, so it is important to find a form of project/results sharing that plays to each student's strength. Consider simply allowing spontaneous storytelling (and recording via audio) or having students combine writing and drawing as they prefer.

#### **Middle (6–8)**

Students can plan their own presentations. Explaining conclusions based on data can be difficult, so you may want to have students practice their explanations with you first. Provide time for students to allocate roles for presentation development.

#### **High (9–12)**

As noted earlier.

#### *Examples*

*Figure 12.7* Students dividing the work of poster development. The teacher (far right) does not need to actively support this well-functioning team

*Figure 12.8* Peers provide input on a whiteboard draft of a poster

*Figure 12.9* Natural objects (here, seeds) are included on a poster

*Figure 12.10* Detailed illustrations of plant anatomy provided by an artistically inclined student

*Figure 12.11* A digitally prepared presentation

*Figure 12.12* Photos of data collection steps for inclusion in a digital presentation

# **12.3 Holding a Community Event**

#### *Concepts*

Holding a community event provides an opportunity for students to share their projects with a community. The community can be defined as narrowly as other students within the classroom, or more broadly to include other students at the school, school staff (e.g., administrative and janitorial staff, other teachers), community groups, city representatives, faith community leaders, or other landowners, rightsholders, and partners. People are part of the community if they are interested in your students or have a relationship to the focal place.

Community events celebrate the achievement of each project and each student, share knowledge and values, and provide a forum for encouraging future action or inquiry. In this space, learning should be possible for both the community and the students. That is, the event should not be a oneway flow of information from students to the community; rather, students should also be able to listen and learn from the community. Implicitly, the focal place is also in conversation with both the community and the students, as it has a central role in framing the event.

#### *Equity and Inclusion*

Be mindful of who is and who is not invited to a community event. The people you invite implicitly teach your students about who is valuable in the community and who might consider their inquiry projects valuable (e.g., if you invite an all-white panel of parks staff, or use a local park as a focal place but do not invite the neighborhood community). If you are advertising the event, be intentional about the audiences that you select.

Reduce barriers for community participation in the event. Hold it at an easy-to-reach location (e.g., your school) and during dates and times that make it possible for a diverse set of people to join. If possible, improve event accessibility by providing seating, amplified sound, and interpretation using school resources. Consider also providing a remote or asynchronous participation option.

#### *Implementation*

Decide on the community you hope to engage through the event. Possible communities include:

include more detailed information on the structure of projects and their learning outcomes.

• **Broader community**—Brainstorm a list of people who may have a relationship with the place. Consider organizations that engage with the community (e.g., local media outlets, libraries, senior centers) or existing events that are seeking groups to share information (e.g., community health events). If hosting your own event is the only or best option, send invitations (perhaps with photos of students outside) early enough to promote participation.

If possible, hold the event in or near the location where your inquiry took place to help the community recognize and build place connections. Begin the event by recognizing connections to land (Chapter 5) and the importance of the community. The event can take a range of forms depending on your goals and the students' presentation modes. Possibilities include but are not limited to:

All students in each group should participate in some way in the presentation or feedback. Set expectations for students and community members that they should engage actively and respectfully with their peers when others are presenting (e.g., by being quiet while others speak and asking questions or leaving comments afterward). The event should enable bidirectional learning and reflection.

Students may be nervous about participating and presenting. Emphasize that the event should be a conversation and celebration. Make sure to recognize at least one positive thing about each team's project.

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

Sharing with parents and peers is easiest. You will need to actively structure and plan presentations for students. Students may feel shy, so offer both emotional and structural support.

#### **Middle (6–8)**

Students better understand the expectations and interests of different audiences, so you can challenge them to target their presentations accordingly. Practice and improvement with feedback are encouraged.

#### **High (9–12)**

Students can likely maintain focus for multiple practice sessions, if needed.

#### *Examples*

*Figure 12.13* An evening event for family and other community members to learn about student projects

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*Figure 12.14* Sharing projects at a weekend community fair held at a school

*Figure 12.15* A student shares her team's assessment of stream health with local officials at a town government meeting

*Figure 12.16* Sharing project outcomes at an educational event held at a recreation area visitor center

# **Assessing Learning 13**

# **13.1 Why (or Why Not) to Assess**

# *Concepts*

Assessing inquiry projects can sometimes be valuable. Formative assessments can help guide students' projects and build their confidence and motivation. Summative assessments can help you determine what learning has occurred and motivate engagement. Dimensions of assessment can include:

#### **Next Generation Science Standards** *(***NGSS):**

#### **Classroom:**

#### **Psychosocial:**

#### DOI: 10.4324/9781003367192-13 243

This chapter has been made available under a CC-BY license.

#### **Human development:**

However, not all of these dimensions are easily or fairly assessed, even if they represent important axes of learning. The experience of building a relationship in a place or seeing an organism for the first time may help shift a student's long-term choices and identity in positive ways you cannot easily assess in the short term. Assessment of inquiry projects is also complicated by the open-ended nature of projects; outcomes can be unknown and surprising, and multiple pathways to excellence exist. Assess what can be measured and be humble about what cannot.

# *Equity and Inclusion*

Grades can inadvertently reinforce power hierarchies in the classroom. Identity-related biases (e.g., around race, class, gender, beliefs, or disability) can determine how you perceive student participation and contribution, even if you think you are bias-free (Quinn, 2017; Sprietsma, 2013; Tenenbaum & Ruck, 2007). This can cause students to learn whether or not they are valued and, in turn, impact identities and future choices. Teacher-led summative assessments, especially grades, should be used cautiously in inquiry-based projects, even for apparently "objective" axes like presentation quality. Teacher-led assessment often drives motivation through fear of failure and can reduce student engagement or form a feigned, expectation-driven engagement.

Place-based inquiry projects fundamentally can and should shift power from the teacher to the students. Teacher-led assessments take some of that power away. Peer assessments come closer to supporting the power shift. Ultimately, assessment arising from students' opinions of themselves, their work, their relation to the work, or the community's opinions of the students may be most motivating and supportive of these altered power hierarchies. Deeper motivation should come from within as students seek to develop their relationships with the place.

## *Implementation*

Decide whether you are comfortable focusing on formative assessment or also need summative assessments; if the latter, decide if you are comfortable shifting power toward student-centered approaches (implementation details in the next section). Then determine which axes of learning are most important to you, noting that some axes map most closely onto the NGSS. Focusing on a few (or no) axes for assessment can be a more effective use of your time, recognizing that additional unmeasured student growth and learning also may be occurring.

Communicate your assessment plans, and your rationale for them, before inquiry projects begin. Leave some room for feedback and revision on this process, given student input.

# *Grade-Level Differentiation*

#### **Elementary (3–5)**

Strongly consider not assigning grades. Small and frequent formative assessments, progress checks, and student self-assessment are more valuable.

#### **Middle (6–8)**

As noted earlier.

#### **High (9–12)**

Provide more time for self-reflection. Have students assess their progress at least during and after the project.

# *Examples*

*Figure 13.1* Friendships and cultures of care develop through intensive teamwork

*Figure 13.2* Time spent outside helps develop a sense of wonder and belonging

# **13.2 Student-Centered Reflection and Feedback**

# *Concepts*

"Ungrading" (Blum, 2020; Feldman, 2018) can shift power back to students in both formative and summative assessment. You still provide clear expectations and standards to students. However, students make progress toward these targets primarily through reflective and peer-based mechanisms, then use this process to propose their own grades. Key features include:

# *Equity and Inclusion*

Adjusting students' proposed grades is subject to the same biases as traditional grading approaches. Similarly, students of some identities may be more likely to give themselves high performance ratings. If you can avoid grades, skip them but keep the rubrics and reflection. Focus on identifying moments of growth and next steps as opposed to completion of tasks.

# *Implementation*

Set clear expectations early on about the assessment structure and how students will participate in it. In general, rubrics and reflection should be used together to generate final grades (if needed) through a student-led and teacher-supported conversation.

Review rubrics (examples in Appendix 3) with students in advance of projects. Students can also co-create rubrics. If you have prior student work or completed rubrics or reflections available, have your current students review these to calibrate their expectations. When using prior student work, be clear that students do not need to replicate prior project to be successful.