0 Research theoretical Elsie Lee-Robbins and Eytan Adar are with the
University of Michigan. E-mail: {elsielee<EMAIL_ADDRESS>Lee-Robbins and
Adar: Affective Learning Objectives for Communicative Visualizations

# Affective Learning Objectives for Communicative Visualizations

Elsie Lee-Robbins and Eytan Adar

###### Abstract

When designing communicative visualizations, we often focus on goals that seek
to convey patterns, relations, or comparisons (cognitive learning objectives).
We pay less attention to affective intents–those that seek to influence or
leverage the audience’s opinions, attitudes, or values in some way. Affective
objectives may range in outcomes from making the viewer care about the
subject, strengthening a stance on an opinion, or leading them to take further
action. Because such goals are often considered a violation of perceived
‘neutrality’ or are ‘political,’ designers may resist or be unable to describe
these intents, let alone formalize them as learning objectives. While there
are notable exceptions–such as advocacy visualizations or persuasive
cartography–we find that visualization designers rarely acknowledge or
formalize affective objectives. Through interviews with visualization
designers, we expand on prior work on using learning objectives as a framework
for describing and assessing communicative intent. Specifically, we extend and
revise the framework to include a set of affective learning objectives. This
structured taxonomy can help designers identify and declare their goals and
compare and assess designs in a more principled way. Additionally, the
taxonomy can enable external critique and analysis of visualizations. We
illustrate the use of the taxonomy with a critical analysis of an affective
visualization.

###### keywords:

Affective visualization, communicative visualization, learning objectives.

##  1 Introduction

Data visualization designers often emphasize their goal of conveying facts,
insights, comparisons, and patterns to their audience through communicative
visualizations. Goals of this type are commonly viewed as ‘cognitive
objectives’ (e.g., recall that group X’s unemployment is greater than group
Y’s). By modeling the designer as ‘teacher’ and viewer as ‘student’ it is
possible to state intents as learning objectives [1]. Using a learning
objectives framework, a designer can explicitly state their objective (e.g.,
“the viewer will analyze the impact of different policy ‘bundles’ on global
temperature”) and assess whether a visualization successfully supports this
outcome. Most attention in the data visualization field—both from researchers
and practitioners—focuses on the cognitive domain. However, data visualization
practitioners also have goals that go beyond the cognitive domain—they want
their audience to have a reaction or a response to their visualization. For
example, a designer may want their viewer to consider that Obamacare is a bad
system if they show a chaotic diagram [37] (or a good one if they show an
organized one [61]). The cognitive intent becomes limited in these
visualizations; the designer doesn’t need the viewer to remember how Obamacare
works or critique its particular features. Rather, the designer may want their
audience to agree with an appraisal (e.g., Obamacare is bad), accept an
attitude (e.g., hospitals should be for-profit), or believe in a value (e.g.,
small government). These goals are affective intents, and cognitive learning
objectives do not cover them.

Affective intents are most obvious in data visualizations that are created for
advocacy reasons. With these, designers are clearly trying to raise awareness
or have a call to action. Advocates for a cause are not trying to hide the
fact that they are taking these positions and that they want you to care about
their cause too. The visualization “U.S. Gun Deaths” created by Periscopic is
a very clear example of an affective visualization [64]. Among other features,
the visualization animates a tally of ‘stolen years’ due to gun violence. The
cognitive aspect of the visualization—how many people were killed—is
important, but not the main takeaway of the visualization. Periscopic co-
founder Kim Rees reflects, “We need people to react. We need people to sort of
get riled up about things, get excited about things, and want to make change
in the world” ([72], 5:30). At a minimum, the goal of “U.S. Gun Deaths” is to
create interest in the topic. Ideally, the visualization will evoke empathy
within the audience for the victims. The affective response, not the cognitive
component, is the main intent of the visualization.

However, even in domains other than advocacy and social justice, designers
have affective goals. They want the viewer to care about the topic, strengthen
an attitude, or take further action. A visualization of a family tree might
have you consider your own family ties. A visualization about sleep patterns
might lead you to value sleep. A visualization about blood donations might
inspire you to donate blood. Unfortunately, because of the lack of attention
in this area, designers may not even realize or consider that they have
affective goals. Given the focus on ‘neutrality’ (or related concepts),
persuasive data visualizations are often controversial, which might make
designers try to hide the fact that they have affective goals. Even if a
designer acknowledges that they have affective goals, they may not have the
vocabulary or framework to articulate their goals.

In this paper, we extend our previous research on cognitive learning
objectives to the affective domain, adapting the affective taxonomy from the
education realm to work for data visualizations. We conceptualize affective
intents as goals regarding an audience’s reaction or response to appraisals,
attitudes, or values. We begin by adapting the (so-called) Bloom’s affective
taxonomy[43] to provide a framework for communicative data visualization
intent. We conducted an interview study to learn more about data visualization
practitioners’ affective goals, how they conceptualize their intent, and how
they could use learning objectives. Based on this study, we revised the
affective taxonomy to better align with visualization intents. A language for
describing affective intents will not only benefit designers, but will also
enable new kinds of critique. We also draw a distinction between affective
rhetorical techniques (i.e., ‘pathos’) in contrast to affective intents.
Specifically, we contribute: an affective learning objectives taxonomy for
data visualization, a qualitative analysis of an interview study of 12
designers, and a critical analysis of an affective visualization with
associated learning objectives to illustrate the taxonomy’s use. Data
visualization designers will be able to apply this framework to their own work
to consider their affective intents.

##  2 Background

Data visualizations are not neutral, even though designers and viewers might
want them to be. To create a data visualization, designers must make choices
that will shape how the audience will interpret the data. Intentionally or
unintentionally, designers have biases, backgrounds, and personal opinions and
preferences that will influence their design decisions. Strategically,
designers employ logical (logos), emotional (pathos), and credibility (ethos)
elements that are designed to evoke emotion in the audience (e.g., visual
imagery). In many cases, these pathos-based techniques, such as humanizing
data, coincide with trying to achieve an affective goal. Though our ultimate
goal is to create a language for describing affective intents, we begin by
reviewing affective persuasion strategies.

Note also that we adopt the language of designer and viewer in this work (per
[1]). The designer here does not necessarily mean the person who created it
but may indicate an editor who has chosen it. We also broadly use the idea of
a visualization as not only the image but also the context (text, captions,
etc.) with which it is associated.

###  2.1 Data Visualizations are Rhetorical Objects

Communicative visualizations are rhetorical devices—they are a way to convince
an audience of an idea. Narrative visualizations, a form of communicative
visualizations, “tell a story” and use rhetorical techniques to make an
argument [33]. Enrico Bertini argues, “We do not visualize data, we visualize
ideas based on data” [9] (emphasis in original). With visualizations, the most
apparent argument is a logical one—the data is a fact that supports a claim.
However, data visualizations are not solely logical arguments. In fact,
visualizations can also have an appeal to emotion [19]. Both of these modes
work together to persuade the audience to believe the data.

Data visualizations can be effective at persuasion [62], but are not always
successful [39]. Cartographic visualizations, in particular, have a history of
being used for propaganda, political pressure, and persuasion [78]. Designers
will choose how a three(+)-dimensional reality will be distorted through two-
dimensional maps. Because of their potential to influence people,
visualizations are used in advocacy [75]. Historically, visualizations have be
utilized to bring attention to, and provoke action for, many issues. Examples
include Florence Nightingale’s military medicine and Charles Minard’s
Napoleon’s march on Russia [28]. Paired with a call to action, visualizations
can galvanize people to act [44]. These examples reflect the subtle
interaction between rhetorical tools to persuade and the ultimate intent of
the visualization (see Figure 1).

###  2.2 Data Visualizations are Not Neutral

A key motivation for our attempt to characterize affective intents is to
challenge the idea the neutrality is desirable, or even possible, in
visualization. Data is biased throughout the entire process, from collection,
to cleaning, to encoding and communicating with an audience [25, 22, 77]. The
designer of a visualization has to make editorial choices that will influence
how a viewer perceives the message of the graph [3]. There are many choices
that a designer makes that could affect interpretation, including what
variables to encode, what colors to use [5], whether to highlight and
annotate, whether to add visual embellishments [6], what context to compare
the data to, how to frame the title of the visualization [39, 40], and what
type of chart to use [80]. Whether or not the designer is aware of it, their
intents, prior experience, and own personal preferences influence their design
choices of the visualization.

Even though there is subjectivity in data visualization, there can be an
illusion of objectivity for both designers and the audience. Designers can
intentionally (or unintentionally) use conventions, such as minimalist
aesthetics or the inclusion of data sources, to make data visualizations seem
objective, factual, and transparent [36]. This perception of neutrality could
also result from an association with scientific methods, which are perceived
as objective [17]. Designing a visualization as if the audience can see
everything from a neutral perspective is what Donna Haraway calls “The God
Trick” [30]. As opposed to text, which has a more apparent narrator
perspective, “images and statistics do not seem to imply an inherent point-of-
view” [67]. Thus, data visualizations are often incorrectly perceived by the
audience as neutral and objective [39, 40, 66, 79]. Although the audience
might not realize it, data visualizations can be created for malicious intents
[23], deceive or mislead [18], be intentionally slanted [39], and be used to
support both sides of an argument [47]. For visualization designers who have
acknowledged affective intents, an appropriate language for describing these
can support better design and critique.

###  2.3 Neutrality-Adjacent Goals

Although we have argued that data visualizations are not neutral, some
practitioners will still contend that their goal is to represent data in an
unbiased way as possible. Some may view their job as to inform their audience
in a neutral way, without any bias or persuasion. For these unacknowledged
affective goals, we propose that an affective learning objective language will
better enable self-critique and analysis (even an ability to express what the
designer doesn’t want).

Members of the journalism field are strong proponents of what we term
neutrality-adjacent goals. These come from the underlying idea of representing
all sides of a situation fairly and the pursuit of truth. Impartiality is a
common goal for journalism [59, 58]. An objective for an election
visualization could be giving equal representation to all parties. This could
mean choosing colors that are of equal salience, or to frame the title to
label the visualization’s topic rather than pointing out one candidate or the
other. A modified guideline, due objectivity [7, 60], also takes into
consideration context, harm, and potential outcomes, giving each side the
platform that they deserve. This may result in only presenting one side. In
the case of climate change, for example, some news organizations no longer
give climate-deniers a platform to present their opinions.

In some highly polarized political debates, a designer may want to strip all
emotion out of the presentation with an objective to present the data with
neutral affect. Sandra Steingraber, an author and biologist, noted that,
“there are times, actually, when communities are so divided emotionally over
an issue that I actually feel my task is simply to speak plainly about science
$\ldots$ In those cases I actually keep affect out” [73]. This goal is not
saying that the visualization itself is neutral, rather that the audience will
not have an emotional reaction to the visualization. In a similar vein, a
designer could have a goal of appearing non-political. For a politicized
topic, they may try to remove indicators that bring awareness to the political
nature of the topic.

A designer’s goal could be to represent the data as accurately and truthfully
as possible, intending an objectivity of method [42]. This goal acknowledges
that designers are subjective beings, but that a scientific method will bring
a level of objectivity to the content that a data visualization designer
communicates (or a journalist, in the original context of [42]). In this way,
objectivity is not a state, but rather a process of collecting the data in the
most accurate and faithful way possible, representing it to avoid
misperceptions of the data.

Neutrality (and neutrality-adjacent) goals connect to affective intents–we are
often trying to convince someone to believe something. We contend that many of
these neutrality-adjacent goals (many of which lead to non-neutral effects)
would benefit from a clear language to describe affective intents.

###  2.4 Emotion in Data Visualizations

In this work, we focus on affective intents, defined as goals relating to a
viewer’s appraisals, attitudes, values, or value system. The term affective in
research literature could alternatively refer to mood, feelings, and emotions.
Emotion is commonly found in affective data visualizations, but is not
necessary nor sufficient on its own. Emotions may play a role in how designers
achieve their goal, but our conceptualization of affective intents is on the
resulting change in appraisals, attitudes or values. While we discuss
emotions, we do not include them in our taxonomy.

Empathy in particular has been a controversial target in discussions of
whether data visualizations should convey or evoke emotion [81, 17, 51, 25].
Nonetheless, designers often use pathos techniques in data visualization as a
way to achieve their affective intent. We view evoking emotion as a technique,
whereas the resulting attitude change is the goal. We illustrate the ways that
cognitive and affective intents differ from logos, pathos, and ethos
strategies in Figure 1. While there has not been as much attention focusing on
affective intents, there is more research about pathos techniques. We briefly
summarize this literature, but note that a comprehensive review of all
techniques is out of scope for this paper. Additionally, we review ways that
emotions and attitudes have been measured in the context of data
visualization.

Figure 1: Examples of persuasion techniques (logos, pathos, ethos) used to
achieve intents (cognitive, affective).

####  2.4.1 Pathos Techniques in Data Visualizations

A subset of affective visualizations can be classified as anthropographics,
which are “visualizations that represent data about people in a way that is
intended to promote prosocial feelings (e.g., compassion or empathy) or
prosocial behavior (e.g., donating or helping)” [55]. Some design techniques
in anthropographics to humanize the data are to emphasize specific individuals
[31, 69, 14, 55], make marks more realistic [31, 69, 13, 14, 55], show each
individual [69, 13, 55], or only focus on a subset of the data [55]. The
effectiveness of anthropographics has been researched, with results suggesting
at most, a small effect of these design techniques increasing monetary
donations [13, 56].

Pathos techniques can also focus on evoking an emotional response to engage
the audience [19]. These design elements can grab a viewer’s attention, evoke
emotion, and create a more engaging experience. Some techniques that can be
used for engagement are color or mood [41, 5, 69, 46], visual imagery
embellishments or art [6, 54, 41, 46], narratives or stories [41, 2, 46],
visceralization or sensory experiences (e.g., audio, physicalizations) [41,
25, 27], immersion or virtual reality [34, 68], audience participation or
interactivity [41, 65, 2], animation [41, 45], novelty [41, 46], and
personalization or proximity [41, 20].

####  2.4.2 Measuring Emotions, Attitudes, and Behaviors

Emotion is clearly important for achieving affective intents and various
studies have sought to measure the interaction between emotions and
visualizations. Study participants in these experiments self-report how
strongly they felt emotions [13, 20, 57, 66], label the visualization with
affective words [46], or report their awareness of affective aspects [46].
Alternative measurements include asking participants to report their attitudes
on a Likert scale (strongly agree to strongly disagree) [20], how likely they
are to donate money [13], or to allocate hypothetical donations [13, 57].
Additionally, researchers can use standard measurements that are validated for
measuring specific attitudes (e.g., attitudes toward immigration [49]).
Researchers can also use qualitative methods, such as focus groups [35],
interviews [63, 79], or open-ended responses [66] to gain an understanding of
emotional reactions and attitudes.

####  2.4.3 Cognition and Decision Making

Emotion is historically perceived to be at odds with rationality. Paul Bloom
argues that relying on empathy is a bad basis for decision making [11]. In any
case, emotions influence decision making and mediate behavioral responses
[50]. Emotion (from pathos techniques) can help achieve cognitive intents (see
Figure 1 for examples). Visual imagery embellishments, also known as ‘chart
junk’, have been found to increase memorability of data visualizations [6,
12].

###  2.5 Learning Objectives

The idea of utilizing cognitive learning objectives to describe designer
intents was introduced in earlier work [1]. This taxonomy enables designers to
structure their intents in simple The viewer will [verb] [noun] statements.
Both the verbs and nouns are hierarchically organized111see
visualobjectives.net. For example, verbs range from remember, to understand,
to apply, and so on. Nouns range from factual to meta-cognitive. Learning
objectives can help visualization designers choose more effective designs
[48]. Learning objectives have been used in data visualization to evaluate
user engagement [52], visual analytic processes [74], specific chart types
(e.g., pictographs [16], 3D visualizations [70]), and multiple levels of
understanding [15].

However, the cognitive learning objectives framework is insufficient for
affective communicative intents. For example, visualization designers can also
intend for their viewer react or respond to an appraisal, attitude, or value.
Designers can also have goals such as prompting action or starting a
conversation about the data [38]. Thus, we extend this prior literature to
include affective learning objectives for data visualization communicative
intents.

##  3 An Affective Taxonomy for Education

The authoritative handbooks on learning objectives are widely accepted to be
Bloom’s taxonomy [10, 43]. In Bloom’s broader framework, learning is separated
into three domains: cognitive (i.e., acquiring and using knowledge), affective
(i.e., attitudes and appreciation), and psychomotor (i.e., physical movements
of the body). The cognitive domain is the most straightforward and common—the
teacher wants the student to be able to know or do something after the class.
Because it is the most used, the cognitive taxonomy has been evolved and
refined significantly [4]. Thus, it is easier to adapt this taxonomy to model
cognitive intents in communicative visualizations [1, 48].

Less frequently considered, yet still important, is the affective domain [32,
43]. Instructors have goals for their students to have an appreciation for the
course subject, be motivated or interested in learning, or develop a favorable
attitude towards the subject. These are different than cognitive learning
objectives, as they focus on moods, feelings, or attitudes. Affective learning
objectives have received less attention for several reasons. First, affective
learning is hard to measure. A multiple-choice question to gauge interest or
appreciation would likely be easy for the student to lie about. Second,
affective learning takes a long time. Developing a positive association for a
subject could take years, or a whole lifetime of learning, making it again
difficult to measure. Finally, affective goals can have a negative stigma; in
education, teachers could be accused of indoctrination. This combination of
factors leads to a less refined taxonomy, but a viable starting point. In our
past work [1], we made a brief proposal for this model1.

Figure 2: Bloom’s Affective Taxonomy (adapted from [32])

###  3.1 Affective Verbs

In the original taxonomy, the authors conceptualized the main dimension of
learning along the concept of internalization [43]. At the very minimal end of
the goals, the viewer will receive (or perceive in [32]) the content that the
visualization is presenting (e.g., the viewer will perceive the harm of
plastic pollution). At this point, the act of attending to is the bare
minimum, with interest being a common lower-level goal. Perceiving is a
necessary prerequisite for deeper levels of engagement. As the viewer is
attending to the visualization, the next level would be for them to respond. A
response could be a willingness to comply or starting to experience enjoyment
(e.g., the viewer will enjoy recycling). A deeper level of internalization is
for the viewer to value. An example could be agreeing with an opinion or
accepting a value (e.g., the viewer will agree that reducing one’s carbon
footprint is essential). The next step is organization (this level is believes
in [32]), which is to organize values into a value system, rank values and
evaluate which ones are more important (e.g., the viewer will value
sustainability over convenience). At the maximal level of affective learning,
there is characterization (called behaves in [32]), where the viewer will
fully internalize the values. They will behave in agreement with these values
and see these values as part of their identity (e.g., the viewer will identify
as an advocate for sustainability). We have adopted this main dimension for
the verbs of this taxonomy.

###  3.2 Affective Nouns

The cognitive taxonomy was extended to explicitly account for a hierarchy of
cognition-related nouns (e.g., facts, meta-knowledge, etc. [4]). However,
neither the original affective taxonomy nor any extensions attempt to
categorize the learning objectives by their “noun types.” As an initial
starting point, we conceptualized the noun dimension as consisting of opinion,
attitude, value, and value orientation. Opinions are weakly held beliefs,
which may or may not be backed up with evidence (e.g., there is too much
plastic pollution in the ocean). Opinions are the easiest to change, and may
be the focus of most affective visualizations. With attitude we find an
evaluative statement of an object that has an affective component (e.g.,
recycling is good). Although there is no universally held description of an
attitude, we use Eagly and Chaiken’s definition, where an attitude is “a
psychological tendency that is expressed by evaluating a particular entity
with some degree of favor or disfavor”[26]. Values represent a broader notion
which guides a more significant set of actions and is applicable across many
situations (e.g., sustainability). Bergman describes values as “relatively
stable beliefs $\ldots$ major life goals and general modes of conduct” [8].
Values enable decision making around what is ‘good’ or ‘bad’. Value system is
the final category. A particular value system (e.g., the Green Party’s 10 Key
Values [76]) suggests a larger set of held values that represent a group of
people or a community. A value system is described as “the specific
architecture, character, and driving force of a group” [8]. As with cognitive
objectives [1], this taxonomy is used to construct statements of the form: The
viewer will [verb] [noun]. Note that while the verbs can be used directly
(e.g., ‘value’), the nouns are replaced with specific opinions, attitudes,
values or value systems (e.g., ‘Obamacare is beneficial to the American
public’ or ‘progressive values’).

##  4 Interview Study

To understand if our proposed taxonomy fits actual designer intents, we
initiated a semi-structured interview study. The interview was comprised of
three main parts: background on the designer and their visualization, our
explanation of learning objectives, and the designer creating their own
learning objectives for their visualization.

###  4.1 Participants

We recruited participants from the Data Visualization Society’s Slack
workspace, from the channels #share-showcase, #share-critique, and #topic-
data-art. From these channels, we reached out to people who had posted their
own data visualization. Additionally, we only reached out to those that
potentially had an affective intent, as indicated by the description, topic,
or design of the visualization. Because there are far fewer visibly affective
communicative visualizations relative to obviously cognitive ones, we also
reached out to an additional 3 people on Twitter who posted visualization
examples. As an opening introduction, we direct messaged 51 people to ask what
their goals were for the visualization. We received 34 responses to our
initial message. We used their response to assess again whether there was an
affective intent. From there, we invited 17 people to participate in an
interview, of which 12 accepted.

Most of our participants create visualizations as part of their full time
jobs. Our participants ranged from 2 years of experience as a hobby to more
than 10 years of experience as their main career. Out of the 12 participants,
6 were journalists, 4 had data science backgrounds, 3 had science backgrounds,
3 had graphic design backgrounds, 8 had another type of background
(participants could have more than 1 type of background). Half of the
participants noted that they had formal education in data visualization,
meaning that they mentioned a masters degree, bootcamp, or some other
structured learning specifically about data visualization. Since the data
visualization society is a global organization, we had participants from New
Zealand, India, Finland, The Netherlands, Spain, Singapore, and the United
States.

###  4.2 Methods

The interviews were 60 minutes long, and were conducted over Zoom by the first
author. After gaining consent for participation, participants were presented
with a slide desk to follow along throughout the interview. First, the
participant was asked to describe their background and experience in data
visualization and their goals for the specific visualization they had posted.
Then, we gave a brief explanation of cognitive learning objectives and
affective learning objectives. We used an example visualization to illustrate
what learning objectives are and how they could be used in data visualization.
Finally, the participant was asked to create cognitive learning objectives and
affective learning objectives for their own visualization. We asked follow up
questions to better understand their experience creating these learning
objectives. Participants were offered a $30 gift card for their time. The
slide deck and the set of starting questions we asked are included in
Supplementary Materials.

This semi-structured interview design is modified from our previous interview
study on cognitive objectives [1]. In that work, we reached out to people with
learning objectives that we had created for their visualization and had them
evaluate if those actually fit their intent. Because we found that some
designers are resistant to the idea that they have affective intents, we used
a more general initial question this time: “In as much detail as possible,
what were your goals for this visualization? In other words, what effect on
your audience were you hoping to achieve with your visualization?” We made
this change because we were interested in how designers would frame their
intent in their own words. In contrast to our past study design where
participants selected and created objectives at the survey stage, we asked
participants to create learning objectives during the interview. We added this
section to get a better understanding of how designers would actually create
learning objectives. Our expectation was that this would reveal what questions
participants have about learning objectives, how difficult it is to create
them, and if they would use learning objectives in the future.

The interviews were recorded and transcribed. We used established qualitative
approaches for data analysis[24]. The initial code book was pre-populated with
codes from our previous study on cognitive learning objectives [1]. We only
used codes that applied to 2 or more participants from the previous study,
starting us with 49 codes. Additional codes were added using open coding by
the first author during close readings of the interview transcripts. After all
of the interviews were coded, the first author revised the code book,
combining similar codes and removing codes that were either too broad or too
specific. The interviews were recoded again according to the revised code
book. In total, the final code book contains 172 codes.

We conducted a thematic analysis using inductive and deductive approaches.
Patterns and themes were explored by discussion between both authors
throughout the coding process and after creating the code book. We considered
the participants’ responses to assess the fit between the designer intent and
the taxonomy. Arising from the data, we found four types of affective goals
that did not fit the taxonomy. Additionally, we studied the interviews to
summarize how learning objectives would be helpful for participants. We
anticipated that designers would find learning objectives difficult and we
found several themes that gave us a deeper understanding of this. Finally, and
most importantly, differing views arose on how some designers thought about
affective intents.

In the interest of protecting the anonymity of our participants, we have
redacted and abstracted details from their interviews, replacing details with
more general terms in [brackets] that still keep the essential meaning of the
quote.

###  4.3 Thematic Analysis

During the interview, we asked participants to create learning objectives for
their visualization. All participants created at least one affective learning
objective (max = 7, average = 3.7, median = 3). All but one also created a
cognitive learning objective (max = 6, average = 2.9, median = 3). The fact
that everyone reported an affective objective is unsurprising due to our
recruitment strategy, but is certainly not representative of the population of
communicative visualizations. There was a wide range of learning objectives
throughout the taxonomy, from a low-level “the viewer will perceive the
importance of the lockdowns” to a high-level “the viewer will evaluate,
question, and modify their own value-orientation system in comparison to the
value-orientation system of democracy.”

There were some common themes across the topics of affective visualizations.
Many of the visualizations were in the area of, or adjacent to, social justice
or advocacy, covering topics that were political, controversial, or emotional.
Given the sampling period, the most common topics were climate change and
COVID-19. Other topics included refugees, racial justice, and policy changes.

####  4.3.1 Intent vs. Taxonomy

One of our main areas of inquiry was to see if the adapted framework of
Bloom’s affective learning objectives is a good fit for designers’ intent. We
asked our participants to self-report if they felt like the learning
objectives fit their intent: most (9) participants responded yes, one
responded no, one responded that only the affective objectives fit, and one
responded that only the cognitive objectives fit. We explore the cases where
the designers’ intents are not translatable to affective learning objectives
to explore the limits of the taxonomy and to get a better idea of the entire
affective space.

One area where intent was hard to map to the taxonomy was in the case of data
art. Some of our participants that identify their work this way indicated that
the objectives didn’t fit their intent. They described looking to provoke a
reaction or evoke an emotion from their audience and were not necessarily
interested in influencing their attitudes or values. One participant described
this as, “it’s only about an emotional response. You’re trying to evoke
something$\ldots$ regardless of what that emotion is.” With a focus only on
emotion, with no higher-level goal, we find that the taxonomy is not a good
fit for solely ‘data art intents.’

The participant that responded that neither the cognitive nor the affective
learning objectives fit her intent had created her data visualization as a
personal project. Her intent was focused on goals for herself, not for sharing
out to an audience. She described it as “when I make my own personal projects,
it’s more about me going through the process. It’s for me.” The participant
wanted to learn about her own personal data and she created the piece as a way
to push the limits of data visualization in an innovative way. Although the
data visualization does evoke emotion and can influence the audience’s
appraisals, attitudes, and values, this was not the driving motivation for
creating it.

A few of our participants had, amongst other affective goals, an intent for
the visualization to be an object in and of itself. These visualizations were
described as “a tribute” or “a commemoration.” Conceptualizing the
visualization as such an object may lead to affective goals—the viewer will
consider [the tragedy]—but the object itself did not directly represent a
learning goal.

Finally, we found that act of creating the visualization was valuable as a
process. One participant described his design process as “I try to express in
some form or the other. So poetry is one form. Data visualization, I think, is
similar.” The act of data visualization can be an emotional process for the
designer. Knitting a scarf of COVID deaths [71] or temperature data [71] can
be a way to express grief and process emotions. Lupi described a goal of a
collaborative project as: “Through my work, I thought, I could try to help her
process and communicate her emotions” [51]. In these cases, it’s about the
journey, not the destination.

####  4.3.2 Learning Objectives are Helpful

In the semi-structured interview, we asked the question, “In what future
situations would you use learning objectives?” In response, we got a variety
of answers about potential ways learning objectives would be useful for
creating data visualizations. In general, most participants (9) indicated that
learning objectives would be helpful in some way. The responses focused on two
things: what the learning objectives would be helpful for and when in the
design process they would be helpful.

Helpful for what?  We found that some participants would only find learning
objectives helpful for big or novel projects, such as “larger projects” that
they want to “show in a different way how it was never shown before” or
projects that are “more time consuming that$\ldots$ was a result of many
weeks, a few months or so. It’s more special to do something different than
bar charts.” They commented that learning objectives wouldn’t be helpful for a
simple bar chart that is straightforward and commonly used. About half of our
participants create data visualizations in a collaborative setting, involving
communication amongst multiple people. Some of our participants mentioned that
learning objectives would be helpful for communicating with clients or a team.
One participant described them being useful “to ask my colleagues about, okay,
well, what do we want the readers to get?” When working with a client,
learning objectives could be integrated into a client brief. The purpose of a
client brief is to outline the specifications, scope, and goals for the
project. These are important to create clear expectations for client
wants/needs and designer deliverables. One participant said, “I could 100% see
this integrated in a creative brief.”

Helpful when? Participants indicated that learning objectives would be helpful
throughout the entire design process. The most common response was that
participants were likely to use learning objectives in their initial planning
stages. Participants indicated that creating learning objectives would be
helpful right at the beginning of the design process. Participants mentioned,
“it’s very good to have these learning goals upfront”, “I think it’s very
useful, right at the beginning of every project”, and “I think these are
highly useful in planning something new.” In defining learning objectives at
the beginning, they could refer to their goals while they are creating the
visualization. Learning objectives would provide clarity, and turn a messy
design process into clear goals. Some participants mentioned that learning
objectives would help with choosing amongst visualization designs. This
observation complements our past work showing that learning objectives can
help designers choose a more effective design [48]. One participant indicated
his design process as “random work trial and error” and that “this will really
make it easier, not as many trials before I reached the close. I think these
are helpful.” Although he mentioned that learning objectives are hard to
create, he anticipated that they would be worth it because they would make the
design process easier. One participant noted that they could use learning
objectives to evaluate their visualization once it’s completed, “you can test
against these goals, you can test if they, if they still hold up.” However,
many participants seemed to be unsure of how to assess if their visualization
was successful in their affective goals. After the participant created
learning objectives, we asked “Looking back at your visualization, do you
think that it succeeds in these learning objectives?” Some participants didn’t
even know where to start with this, replying with “I’m not sure how to gauge
that”, “I don’t know, that’s something that I wouldn’t be able to measure,”
and “Um, I think so. But um, yeah. I don’t know, how could I be sure?”
Evaluation is an area where learning objectives could be helpful. Learning
objectives could be translated into assessments, which could be used to
evaluate if the desired response was achieved in the audience. We discuss a
few options for assessments in Section 7.

####  4.3.3 Learning Objectives are Difficult

As we first noticed in our study of cognitive objectives, most participants
found learning objectives difficult to create [1]. Anticipating this, we
specifically asked our participants how hard it was to create learning
objectives. Almost all participants (11) reported that they found some
difficulty in creating the learning objectives. For most, this was the first
time they had encountered the idea of learning objectives, let alone
attempting to create them for their visualizations. Generally, participants
reported that the affective domain was more difficult than the cognitive
domain, with only one reporting the opposite.

One reason that the learning objectives, especially the affective ones, are
hard to create, is that designers haven’t given them much thought previously.
Some participants indicated that they always do think about their intent,
generally in other terms, or in the form of a client brief. However, others
indicated that they are not aware of their affective intent. They mentioned
having an affective intent, but they do not explicitly think about it. One
participant describes this as “the affective learning objectives $\ldots$ is
very interesting, because we use them, but$\ldots$ We don’t speak about it
consciously.” Furthermore, even designers who are aware of their affective
intent feel a stigma against explicitly labeling or talking about it.

> “In journalism $\ldots$ it’s sort of frowned upon to think about $\ldots$ or
> I guess to talk openly about the idea that$\ldots$ we’re, like, trying to
> influence how people think or feel about a certain thing or $\ldots$ what
> actions we want them to take.”

Several people mentioned that some of the verbs were difficult to use.
Participants commented, “it was quite hard to, like, use the verbs. Like, I
really want to use the verbs, but I have trouble trying to form a sentence
that fit to the ideas I have in my head.” Because the taxonomy is based on
educational goals, the verbs were often phrased in the positive direction—“the
viewer will enjoy [the topic]”. These types of goals are more suited for the
classroom, where the teacher wants the students to gain an appreciation or a
passion for their subject. However, many of the affective visualizations that
we’ve seen are about negative topics, such as climate change, deaths, and
injustices. This incongruence between verbs and topic made it harder for one
of our participants to use the verbs. Instead of thinking of her objective as
“the viewer will accept [that we need to treat people better]”, it was more
natural to frame it as “the viewer will reject [the terrible treatment of
people]”. The visualization story was about a tragedy, and the tone was
negative and dark. The positive verbs were not a good match for her intent and
the way she was designing the visualization.

When creating learning objectives, there was an ambiguity between the
cognitive and affective domains. After the explanation of learning objectives,
some participants mentioned “I may$\ldots$ have difficulties to separate the
cognitive ones from the affective ones.” and “[t]hey go hand in hand, I would
say I find it hard to separate them very, very easily.” While creating
cognitive learning objectives, goals arose that were better suited for the
affective domain, and vice versa.

####  4.3.4 Differing Views on Affective Intent

Previously, we discussed how the design choices that a designer makes can have
an influence on how the audience will interpret and react to the message of
the visualization. Throughout our interviews, participants had varying
viewpoints on how they viewed the responsibility of designer influence. The
viewpoints ranged from designers attributing full autonomy to the audience,
the designers having an intent but acknowledging the variability within the
audience, and designers taking full responsibility for the communication to
the audience. Most participants fell in the middle of this range, but we also
had some who were more towards one side than another.

Some designers disagreed with the idea that their visualization should affect
their audience’s values or behaviors. These designers talked about the
audience as “it’s everybody’s own kind of decision to make up their mind” or
“I’m not forcing anyone with my visualization that you should modify your
[behavior]. I am not the person responsible for that.” These designers thought
about their role as only creating the visualization. After that, it’s up to
the viewer to engage with it. These designers felt like the affective learning
objectives didn’t fit their intent as well as the cognitive learning
objectives did. One designer frames his intent as informing and “just
show[ing] the facts” clearly. Another designer frames his intent as “the
viewer will consider [the phenomenon].” Even though these designers do not
take responsibility for the audiences’ outcome, they do agree that their
visualization can and does influence a person’s attitudes. One designer noted
that he received audience feedback indicating exactly that—that his
visualization influenced their attitude. This designer agreed that his
visualization does achieve the affective learning objectives, even though he
disagrees that influencing their attitude was his intent. Another designer
agrees that the ideal response that he would love to see in an audience would
be for them to change their behavior. In fact, he mentioned specific design
decisions that he made to increase the chance that the audience would change
their behavior. However, he disagrees that changing the audience’s behavior
was his intent.

Most designers fell in the middle of the range, where they agreed that they
had goals to influence their audience, but also acknowledged the individual
differences within the audience would moderate each person’s reaction to their
visualization. These participants mentioned that not everyone is going to
react in the same way, or that the audience has some agency in how they engage
with the visualization. The participants said, “It’s really more like, here’s
an information that I think is worth knowing and sharing, and you know, engage
with it the way you’d like to engage with it”, “Viewers will find this
situation unacceptable, but that’s really quite like more personal. I guess
not everyone would find a situation unacceptable”, and one compares his role
to a radio host—“your audience is in control of the volume button, and they
can put you on silent every time. So you have only the indirect control, you
have to assume what your audience likes, and how you can convince them.” In a
slightly different vein, some designers see a difference between outcomes that
occur directly from information in their visualization and outcomes that occur
beyond that. These outcomes might arise as a consequence of their
visualization, but are not directly related.

> “[T]he viewer will understand the [cognitive information]$\ldots$ As a
> consequence, they will [take an action]. But the project is not explaining
> why they have to [take an action]. [It’s] just that is coming out of being
> exposed to the numbers and to, to kind of hopefully have the visualization
> having an impact in them. [It’s] not a learning, [it’s] a consequence, I
> would say.”

At the far end of the range, we had some participants describe the
responsibility that they have on influencing the audience. This viewpoint is
that the designer is responsible for communicating the message. If the
audience doesn’t get the message, then the fault lies with the designer, not
the audience. One participant described this as “How do you make sure they
understand and you own that message failure? If that message fails, it’s on
you as the author.” Another participant described this responsibility as not
just providing information, but doing it in a way that is easily understood
and not overwhelming.

> “Because one of the responsibilities that we have, as visual journalists, is
> not just to like put all the information out there. It’s also to direct the
> viewers eye, and direct the viewers attention$\ldots$ I take very seriously
> the responsibility like not to confuse people and not to overwhelm people.”

These two participants did not specifically discuss affective intents in the
context of this theme. They were either speaking more broadly about
communication as a whole or at least more in the realm of cognitive
efficiency. Most designers acknowledge that individual differences within the
audience can cause a range of outcomes. Some use that as an excuse to absolve
oneself from the responsibility of conveying a message. Others see it as their
responsibility to communicate to everyone in the audience regardless of their
background.

##  5 Revised Taxonomy

In response to our interviews, we refined the original learning-focused
affective taxonomy to better fit affective visualization objectives.

Figure 3: The revised affective verb taxonomy based on our interviews

###  5.1 Affective Verbs

In our revised taxonomy, we have revised the verbs to be: observe, position,
strengthen, connect, and behave. The affective intents start at observe the
range of beliefs, then to position oneself within that range of beliefs, then
to strengthen one’s belief, then to connect and compare several beliefs with
each other, and finally to behave consistent with that belief (see Figure 3).

In observe, the intent is simply to make the viewer aware of the range of
beliefs. At this beginning stage, the designer is not trying to influence the
viewer to take one side or another. This is similar to perceive, the lowest
level of Bloom’s taxonomy. When the viewer is simply observing the belief,
there is minimal to no emotional response. For example, our goal may be to
make the audience aware that there is a range of attitudes from “pollution is
the biggest health problem in the US” versus “pollution does not cause health
problems.” More realistically, this kind of objective may make a few ‘points’
in the attitude space known to the viewer (e.g., a more restrained ‘point’:
“pollution is a significant health problem in the US”). Additional verbs
within this level would be perceive, see, consider, watch, and attend to.

The next level, position, is when the designer intends to point out a specific
place along the range of beliefs where they would like the viewer to be. At
this stage, the viewer advances from observing the attitude to placing
themselves onto the range of attitudes. This could be that the designer wants
the viewer to self-reflect on their own prior attitude of where they land
within the range. Alternatively, the designer could have a goal to position
the viewer in a specific area of the range of attitudes. With ‘position,’
there is no strong conviction, just a weakly held belief. An example of a
learning objective could be “the viewer will agree that pollution is a major
cause of negative health outcomes,” placing them on one side of the attitude
spectrum. Additional verbs within this level would be agree, accept, prefer,
endorse, and adopt.

The third stage, strengthen, is when the designer tries to increase the
strength of an attitude. Here, the designer doesn’t want the audience to only
agree with the belief, but to deeply support it. If the viewer already holds
the specific belief, the goal would be to increase the intensity of the
conviction. This level is similar to Bloom’s level of believe, where the
student is not only accepting a value, but holding it more deeply and
ingrained. As the strength of the conviction increases, so does the emotional
response of the viewer. An example of a learning objective could be “the
viewer will trust that pollution is a major cause of negative health
outcomes.” Additional verbs within this level would be intensify, support,
believe, trust, internalize, and commit to. The distinction between position
and strengthen is not a ‘clean’ separation. Often, we will do both things
nearly simultaneously (form an attitude and internalize it). However, the
distinction allows us to acknowledge that at this level the intent of a
visualization may be to weaken a particular attitude and strengthen another
(i.e., shift the viewer from one position to another).

The fourth stage, connect, is when the designer wants the audience to compare
two of their beliefs. In this stage, the intent is for the viewer to compare,
contrast, and rank multiple beliefs. The viewer is making judgements about
which beliefs are more important to them, similar to Bloom’s level of
Organization. An example of a learning objective could be “the viewer will
compare their attitude toward sustainable eco-friendly options to their
attitude toward convenient plastic options.” Additional verbs within this
level would be compare, contrast, rank, connect, sort, rate, and evaluate.

The final stage, behave, is when the designer wants the audience to act in
ways consistent with the desired belief. Designers frequently have this goal,
as demonstrated in “calls to action” often listed at the end of a
visualization or story. Behaviors are the most visible indication of an
affective outcome, yet these could be the most difficult to achieve. An
example of a learning objective could be “the viewer will buy less plastic.”
Additional verbs within this level could be demonstrate, practice, act, or a
specific action verb related to the topic of the visualization.

The audience’s prior knowledge, beliefs, and backgrounds will naturally
influence what type of verbs will be effective. When communicating to an
audience that holds an opposite belief, the designer may be more successful
with an observe or position goal, rather than having an outcome of a behavior.
On the other hand, when communicating with an audience that already agrees
with a belief, it will be more likely for a designer to strengthen that belief
or inspire action for it.

###  5.2 Affective Nouns

In the revised taxonomy, we redefined the lower levels of the nouns as
appraisal and attitude, while maintaining the upper levels of value and value
system. We found that the difference between opinion and attitude (and belief)
were ambiguous and confusing to participants. There are many different
accepted definitions for these terms in the literature, so we will more
specifically describe how we are operationalizing the nouns for our revised
taxonomy.

The lowest level of the noun taxonomy is an appraisal. This is the level
closest to the data and most straightforward to convey to the audience. An
appraisal starts with a cognitive insight. For example: “the United States is
historically the largest contributor of carbon emissions.” This is an
unemotional fact as represented in the data. The lowest level of an affective
response would be to associate the insight with a “value judgement” (i.e.,
that the data observed means something good or something bad). For example, an
appraisal of this data could be “the United States is historically the worst
contributor of carbon emissions.” The change from largest to worst moves this
simple fact to an affective evaluation.

The next level would be attitude, or a subjective disposition towards an
object or phenomenon. An attitude is an inference that is one step away from
the data, meaning that the data does not directly represent the attitude. This
level is hierarchical, in that an attitude can be based on multiple
appraisals, although there could be just one portrayed in the visualization.
An example of this could be “the United States is responsible to address
climate change” or “carbon emissions are a major problem for the world.”

The upper two levels of the revised taxonomy are the same as the previous
version: value and value system. Values are a deeply held belief that are hard
to change, are persistent over a long time, and are applicable across many
situations. An example of a value could be “restorative justice” or “equity.”
Value systems are a set of beliefs held by a group of people. An example of a
value system could be “democratic” or “abolitionist”. There are various value
system frameworks that may be used (e.g., Moral Foundation Theory [29]).

As with the verbs, the background and prior knowledge of the audience will
impact what belief levels the designer would be likely to influence. A
person’s value system will moderate their acceptance of values, attitudes, and
appraisals. If their value system is incongruent with the visualization, then
the viewer may ignore, reject, or oppose the data. If the value system is in
line with the visualization framework, then the viewer may be more willing to
update their beliefs.

##  6 Affective Analysis: Juneteenth

In this section, we conduct a critical analysis of an affective visualization
to illustrate the use of the affective learning objectives taxonomy for a
range of objectives. A critical analysis could be conducted with an external
gaze. A critique of a visualization could cite design decisions and context
clues for what learning objectives the visualization could potentially be
pursuing, as well as where the visualization succeeds or fails. For this
section, one of the designers of this visualization was a participant in our
interview study. Thus, we have confirmation that the objectives stated were
actually applicable. However, as the interview focused on framing intent in
Bloom’s taxonomy, we have translated the goals to the revised taxonomy, but
have stayed faithful to the original intent. In our interview, we only talked
to one of the authors, and the quotes below largely reflect her viewpoint on
creating this visualization. We supplement this with quotes from a published
conversation between the two authors that detail their process and goals for
this visualization [21]. Finally, we chose this example because of its range
of learning objectives, but have included more examples at
visualobjectives.net.

Figure 4: An affective data visualization showing the timeline to freedom for
black people in America [53]. A larger version is included in our
Supplementary Materials.

This visualization, titled “Juneteenth: An Inflection Point in the Struggle
for Freedom,” communicates the realities and misconceptions of Juneteenth and
the end of slavery in America in a visually provocative way (Fig. 4). During
the American civil war, US President Abraham Lincoln issued the Emancipation
Proclamation in 1863, outlawing slavery in the confederate states. But, the
news didn’t reach enslaved people in Texas until June 19, 1865, also known as
Juneteenth. This visualization was created for affective intents. Our
participant stated, “it’s not really a cognitive problem. It’s an emotional
problem.”

At a lower level, one of the goals of the visualization was to have the viewer
observe the unjust and slow delay of the timeline to freedom for black people.
This was represented in the intentional design features of the curved timeline
which starts at 1619 (the start of slavery in America) and ends at 1866 (the
end of slavery in America). Almost all of the timeline is empty, except for
two events in the middle. One of the designers summarized the design choices
and connected it to their intent, “The white space on the timeline was
intentional because I wanted the reader to visually feel the time pass slowly
between the onset of slavery and its abolition” [21]. Additionally, the length
of one of the turns on the timeline is representative of a generation (25
years). This was an intentional choice to make the connection of the years
more relatable to the human scale.

One of the goals of the visualization was, “challenging of the mythology [of]
$\ldots$ Abraham Lincoln, the Great Emancipator.” The mainstream understanding
of the Emancipation Proclamation is that Lincoln freed the enslaved people for
a moral and good cause. This visualization presents the reality of the
situation—Lincoln issued the Emancipation Proclamation for financial,
logistical, and strategic reasons for the civil war. In fact, “Lincoln was not
an abolitionist, repeatedly made his position clear that he did not support
the social and political equality of the black race” [53]. For this
visualization, the authors wanted to challenge the audience’s prior beliefs
(Lincoln had moral pursuits), and accept the reality they put forth (Lincoln
used emancipation as a military strategy). Translated into a learning
objective, this goal could be described as “the viewer will accept the
narrative presented.” The visualization had several elements to support this
goal. The Tableau dashboard included text alongside the timeline to explain
this narrative. The language accurately describes the reality of black people
in slavery, using words like “brutal”, “horrors”, and “atrocity”. These words
evoke a negative affective response, making the viewer painfully aware of the
reality of slavery. Additional affective design elements include interactivity
to unfurl each section, visual imagery and metaphor, and the overall dark tone
of a black background with red colors.

There were several goals to move the needle slightly on values like equity and
value systems as a whole. The viewer will (1) observe or think about equity;
(2) agree with equity (in the long term) (3) evaluate their own value system
and compare it to their cultural group’s value system (in the very long term).
The designer acknowledged that these goals, especially shifting values and
value systems, are long term and not achievable with a single visualization. A
quote from our interview highlights this line of thought:

> “And that may not happen right away with looking at this. But if we start to
> shift people here, they start to question the role of slavery, then they can
> start looking at other parts of this outside much later on, and really begin
> to think about it. And that’s the whole goal of Juneteenth as a holiday, and
> that’s the whole goal of this visualization is to progressively feed that
> value adjustment.”

Finally, the authors mention a call to action in their published conversation,
inviting the audience to “contact your Congressional representatives and sign
the petition to make Juneteenth a federal holiday” [21]. This call to action
was not in the visualization. However, phrased as an affective learning
objective, this goal could be framed as “the viewer will take action in
support of Juneteenth.”

As mentioned previously, this visualization was a collaboration between two
designers. Our participant mentioned that the collaboration consisted of
conversations, dialogues, and an online whiteboard. Learning objectives could
add to this discussion as a social object that the designers can create
together, talk about, and refer to. Learning objectives might make it easier
to collaborate with others by supporting a shared understanding of the goals
of the project.

##  7 Discussion

In our interviews, we found that some designers disagreed that they had an
intent to influence their audience. It’s unclear if they were truly
uninterested in affective intents or if they were uncomfortable with the idea
of “biasing” their audience. In truth, this could vary from person to person.
In our interviews, we saw that the designer’s intent influences their design
choices. Additionally, designers were aware that their visualization
influenced (at least some of) the audience. Potentially, these designers
perceive that an intent to persuade or influence is not a socially acceptable
goal for data visualization. In the wider visualization community, there is a
stigma or controversy that data visualizations should be “objective.” This
perception of the community values might influence designers’ perception of
their own intent. However, we find that this value of “objectivity” is
shifting within the community, with increasing discussions of subjectivity,
bias, and affective intents.

We believe that it is important to acknowledge and model affective intents. In
some sense, all visualizations are political and thus all have some affective
intent. It is problematic that people try to avoid their own subjectivity and
deny affective intents when they have them. Accepting one’s affective intents
means that self-subjectivity is confronted. We see resistance to accepting
subjectivity in data visualization because it is uncomfortable. Yet, there is
value in accepting subjectivity. Once we acknowledge our own biases, we have
the opportunity to self-reflect, be more aware of how they affect design, and
lead us to create better visualizations. We agree that context is queen,
meaning that it’s not always favorable to include pathos techniques in
visualizations [25]. But recognizing affective intents within ourselves and
within others’ work will allow us to think more critically and deeply about
affective intents and their use in data visualization. Building a language for
affective objectives may better guide designers and critics. We hope that
designers will apply this framework to their own work and consider both their
cognitive and affective intents during their design process.

There is some ambiguity of what is a technique in data visualization compared
to what is a goal of data visualization. Our framework of affective learning
objectives centers around reacting and responding to appraisals, attitudes,
and values, and does not include a goal of simply evoking emotion. We consider
evoking emotion as a pathos technique used in data visualization in order to
achieve a higher level goal, such as “the viewer will strengthen a value”. For
example, we considered humanizing the data to be a technique in order to
achieve a higher level goal, such as “the viewer will support the refugees”.
In the framework of rhetoric, an emotional appeal is part of making an
argument. It does not make much sense for a designer to want the audience to
feel sad, and then end there. There is an underlying reason that the designer
wants the audience to feel an emotion. Maybe this is to consider the injustice
of a situation, or to accept and internalize a value, or to act in response to
the situation. In any case, the designer is not only trying to make the
audience sad. This is why we do not include emotion as a layer of the
taxonomy. In fact, emotion could be thought of as a byproduct of a learning
outcome. At lower levels of the taxonomy, there is not much of an emotional
reaction (e.g., “the viewer will consider the situation”). At higher levels of
the taxonomy, there will be much more of an emotional response (e.g., “the
viewer will protest on behalf of human rights”). If an advocacy group is
trying to get their audience fired up and emotionally charged, it’s because
they want them to take an action. If a journalist is trying to evoke emotion,
it’s because they want them to care about their topic.

###  7.1 Future Directions

We explored the ways in which designers create learning objectives during an
interview study. However, this study only recruited designers after they have
created their visualizations. Therefore, we have explored learning objectives
in a reflective context, by having designers think about a previous
visualization they have already created. There may be different considerations
and insights that arise when designers use learning objectives during their
actual design process. Future work should explore affective objectives as
designers actively use them within their design process. We hypothesize that
learning objectives would help designers create more effective affective
visualizations. Additionally, our interview study was focused on only the
Bloom’s version of affective learning objectives; we have not asked designers
about their opinions on the wording of our revised taxonomy. Future work
should specifically evaluate this version.

While there is some overlap between affective visualizations and data art, we
originally concluded that artistic goals are outside the scope of the
taxonomy. In this paper, we mainly focused on affective intents. However, the
role of emotion in affective visualizations should be discussed more. The
larger area of interest of how emotions are evoked in data visualization and
what techniques in data visualization achieve that is an important field. A
deeper investigation into data art may illuminate the connection between
artistic endeavors, emotions, and reactions from an audience. We have thus far
focused on intentional objectives (hence our focus on design intents).
However, we believe it is worth considering when affective learning is
achieved un-intentionally.

We found that creating learning objectives is difficult, especially affective
ones. More materials should be produced to help designers traverse this area
and clearly define their intent. Teachers have supporting materials for
creating learning objectives, visualization design should also have supporting
materials. An hour-long interview was a quick explanation, but more accessible
materials should be created.

Finally, we have not included ways to assess whether, and to what extent, an
affective visualization was successful in its goals. Part of the appeal of
explicit definitions of learning objectives is they naturally lead to
assessments. The main difficulties with evaluating affective intents is that
they: shift on long-term timelines; are hard to accurately assess with typical
multiple-choice questions; and outcomes can be behaviors. For assessments of
position goals, a yes/no response would suffice (e.g., Q: Do you agree that we
should defund the police? A: yes/no). For assessments of strength goals, a
Likert scale could measure how strong the belief is (e.g., Q: To what extent
do you agree that we should defund the police? A: strongly dis/agree, slightly
dis/agree, neither agree nor disagree). For assessments of connect or compare
values, ranking may be used (e.g., How much money would you allocate to each
category given a fixed city budget?). For the final level, behaves, a designer
could ask for intentions to carry out behaviors (e.g., How likely are you to
donate money to this cause?), measure indications of behaviors (e.g., track
how many viewers clicked on a link to donate), or ask people later if they
actually took action (e.g., Q: What actions have you taken in support of this
cause this year? A: Donating; Signing petitions; Sharing materials; Contacting
government representatives).

A good measure will isolate the effect that the visualization has on the
audience, rather than measuring the audience’s prior beliefs. One way to do
this is to ask pre- and post- questions. By contrasting a viewer’s answer
about their attitude before seeing the visualization and after, one could
evaluate the success of the visualization. This method, and all multiple-
choice methods, need to be designed carefully to ensure that they are
measuring the intended phenomenon (effect of the visualization) and not
something else (demand characteristics). Qualitative measures such as open
ended responses, interviews, conversations, and observations can also give
designers insight to how their visualization may influence their audience.
Future work should expand on the idea of measuring and evaluating the success
of affective visualizations.

Learning objectives allow designers to explicitly identify their target goal.
This can lead to thinking about what the result would look like if that goal
was achieved. By considering their intent, designers can consider their
audience’s perception and reaction, even during design iterations. Even
without the perfect assessment, getting feedback on designs in a casual way
can give the designer valuable information how well they met their goal.

##  8 Conclusion

If all visualizations are political, all visualizations are affective. To
enable designers to design and talk about their affective goals, we develop a
taxonomy for describing affective learning objectives for communicative
visualizations. This work expands upon the idea that communicative
visualizations are essentially a learning problem where the designer/viewer
correspond to the teacher/student pairs. Our past work has identified ways of
describing cognitive intents through a cognitive taxonomy. However, many
visualizations–perhaps all–enable some affective learning. We begin with an
existing taxonomy which we test against the intents of real designers. We
update the taxonomy to better support visualization intents. Through a
language of learning objectives, we offer a pathway for designers to think
about their visualizations from both affective and cognitive lenses, contrast
alternatives relative to specific articulation of their goals, and assess
their work.

###### Acknowledgements.

We are grateful to the NSF for their support of this work through NSF
IIS-1815760. We would also like to thank our interview participants for
sharing their work and thoughts with us. Finally, we thank our reviewers for
their helpful feedback.

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