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### **Biotechnology Virtual Labs: Facilitating Laboratory Access Anytime-Anywhere for Classroom Education**

Shyam Diwakar, Krishnashree Achuthan, Prema Nedungadi and Bipin Nair *Amrita Vishwa Vidyapeetham (Amrita University) India*

#### **1. Introduction**

Biotechnology is becoming more popular and well identified as a mainline industry. Students have shown greater interest in learning the techniques. As a discipline, biotechnology has led to new advancements in many areas. Criminal investigation has changed dramatically thanks to DNA fingerprinting. Significant advances in forensic medicine, anthropology and wildlife management have been noticed in the last few years. Biotechnology has brought out hundreds of medical diagnostic tests that keep the blood safe from infectious diseases such as HIV and also aid detection of other conditions early enough to be successfully treated. Medical kits for diabetes, blood cholesterol and home pregnancy tests are also biotechnology diagnostic products. Industrial biotech applications have led to cleaner processes that produce less waste and use less energy and water in such industrial sectors as chemicals, pulp and paper, textiles, food, energy, and metals and minerals. Laundry detergents produced in many countries contain biotechnology-based enzymes making them nature friendly and safer. Agricultural biotechnology benefits farmers, consumers and the environment by increasing yields and farm income, decreasing pesticide applications and improving soil and water quality, and providing healthful foods for consumers. Biotechnology has created more than 200 new therapies and vaccines, including products to treat cancer, diabetes, HIV/ AIDS and autoimmune disorders.

This rise in application has led to an increased rise in the number of students undertaking University-level biotechnology courses. However, biotechnology education requires an eclectic approach of combining various sub-disciplines. Biology courses and chemistry courses in biotechnology have diversified the approach of the topic. Most common courses that biotechnology degree programs focus at the University level in India consist of cell biology, molecular biology, microbiology, immunology, ecology, statistics and biophysics.

A brief description of the courses will be sketched so a better picture can be understood on the university-level curriculum at most places in India and abroad. Cell biology is a course that focuses on theoretical fundamentals behind the structure, function, and biosynthesis of cellular membranes and organelles; cell growth and oncogenic transformation; transport, receptors, and cell signaling; the cytoskeleton, the extracellular matrix, and cell movements; chromatin structure and RNA synthesis. Molecular biology course covers a detailed analysis of the biochemical mechanisms that control the maintenance, expression, and evolution of prokaryotic and eukaryotic genomes. The topics also include gene regulation, DNA replication, genetic recombination, and mRNA translation. In particular, the logic of experimental design and data analysis is emphasized. Microbiology course introduces students to the principles of infectious agents. Fundamental techniques in microbiological researches, such as sterilization, isolation, morphological observation, and cultivation are usually covered. Immunology courses focus on the mechanisms which govern the immune response. This will usually include the cells, organs and molecules that mediate the innate and adaptive aspects of the immune system as they apply to infection, tumor recognition, autoimmune diseases, immunodeficiency, cancer and hypersensitivity. Population ecology courses introduce students to major concepts in population ecology including topics such as mathematical models of population growth, population viability analysis, habitat fragmentation and meta-populations, dispersal, population harvesting, predation and population cycles, competition, and estimation of population parameters in the field. Biochemistry course explores the roles of essential biological molecules focusing on protein chemistry, while covering lipids and carbohydrates. It provides a systematic and methodical application of general and organic chemistry principles. Students examine the structure of proteins, their function, their binding to other molecules and the methodologies for the purification and characterization of proteins. Enzymes and their kinetics and mechanisms are covered in detail. Metabolic pathways are examined from thermodynamic and regulatory perspectives. A typical course in biochemistry provides the linkage between the inanimate world of chemistry and the living world of biology. Biophysics is a course that usually links to the study of underlying physical phenomenon in biology and their function. Biophysics course usually cover techniques, methods and applications besides molecular structure and function. Topics in biophysics covered will include an introduction to cell and molecular biology, biorheology, Brownian motion, molecular interactions in macromolecules, protein and nucleic acid structure, physics of biopolymers, chemical kinetics, mechanical and adhesive properties of biomolecules, molecular manipulation techniques, cell membrane structure, membrane channels and pumps, molecular motors, neuronal biophysics and related biophysical mechanisms. A very significant yet seemingly unrelated course is biostatistics. A single introductory course in biostatistics involves an emphasis on principles of statistical reasoning, underlying assumptions, and careful interpretation of results. Topics covered include descriptive statistics, graphical displays of data, introduction to probability, expectations and variance of random variables, confidence intervals and tests for means, differences of means, proportions, differences of proportions, chi-square tests for categorical variables, regression and multiple regressions, an introduction to analysis of variance.

Using software technologies for education has become a new trend. Computer-based technologies developed by academic institutions as well as industries worldwide are revolutionizing the educational system. A new field involving the use of virtual reality techniques is becoming the training environment. Through virtual labs, a new interdisciplinary field of science brings together biologists and physicists to tackle this grand challenge through quantitative experiments and models. Using several pro-learning even

transformation; transport, receptors, and cell signaling; the cytoskeleton, the extracellular matrix, and cell movements; chromatin structure and RNA synthesis. Molecular biology course covers a detailed analysis of the biochemical mechanisms that control the maintenance, expression, and evolution of prokaryotic and eukaryotic genomes. The topics also include gene regulation, DNA replication, genetic recombination, and mRNA translation. In particular, the logic of experimental design and data analysis is emphasized. Microbiology course introduces students to the principles of infectious agents. Fundamental techniques in microbiological researches, such as sterilization, isolation, morphological observation, and cultivation are usually covered. Immunology courses focus on the mechanisms which govern the immune response. This will usually include the cells, organs and molecules that mediate the innate and adaptive aspects of the immune system as they apply to infection, tumor recognition, autoimmune diseases, immunodeficiency, cancer and hypersensitivity. Population ecology courses introduce students to major concepts in population ecology including topics such as mathematical models of population growth, population viability analysis, habitat fragmentation and meta-populations, dispersal, population harvesting, predation and population cycles, competition, and estimation of population parameters in the field. Biochemistry course explores the roles of essential biological molecules focusing on protein chemistry, while covering lipids and carbohydrates. It provides a systematic and methodical application of general and organic chemistry principles. Students examine the structure of proteins, their function, their binding to other molecules and the methodologies for the purification and characterization of proteins. Enzymes and their kinetics and mechanisms are covered in detail. Metabolic pathways are examined from thermodynamic and regulatory perspectives. A typical course in biochemistry provides the linkage between the inanimate world of chemistry and the living world of biology. Biophysics is a course that usually links to the study of underlying physical phenomenon in biology and their function. Biophysics course usually cover techniques, methods and applications besides molecular structure and function. Topics in biophysics covered will include an introduction to cell and molecular biology, biorheology, Brownian motion, molecular interactions in macromolecules, protein and nucleic acid structure, physics of biopolymers, chemical kinetics, mechanical and adhesive properties of biomolecules, molecular manipulation techniques, cell membrane structure, membrane channels and pumps, molecular motors, neuronal biophysics and related biophysical mechanisms. A very significant yet seemingly unrelated course is biostatistics. A single introductory course in biostatistics involves an emphasis on principles of statistical reasoning, underlying assumptions, and careful interpretation of results. Topics covered include descriptive statistics, graphical displays of data, introduction to probability, expectations and variance of random variables, confidence intervals and tests for means, differences of means, proportions, differences of proportions, chi-square tests for categorical variables, regression and

multiple regressions, an introduction to analysis of variance.

Using software technologies for education has become a new trend. Computer-based technologies developed by academic institutions as well as industries worldwide are revolutionizing the educational system. A new field involving the use of virtual reality techniques is becoming the training environment. Through virtual labs, a new interdisciplinary field of science brings together biologists and physicists to tackle this grand challenge through quantitative experiments and models. Using several pro-learning even distance education courses have started using virtual laboratories to enable students to access equipment since they are independent from opening hours and the work schedule of the staff. In many engineering courses within India, simulation is the most effective tool in training students in the use of sophisticated as well as complicated instruments that are routinely employed in modern biological and chemical laboratories. For the life sciences, this also circumvents the use of expensive and hazardous biological and chemical agents which toxic to the experimentalists as well as to the environment. Above all, the virtual lab technology is cheap as well as cost effective.

Education in many universities and research institutes include their own virtual laboratories on the web, which are accessible to people around the world. Although some laboratory practice requires getting one's hands 'dirty', it has already been established that the Virtual Lab enables the students to understand the underlying principles and the theory behind laboratory experiments. E-learning plays and will play an important role in diverse regions such as India where the traditional lab facilities at Universities are not very well localized to suit requirements of all sub-regions. With multi-campus scenarios as in some Universities such as ours, offering cross-disciplinary courses needs to exploit the use of extensive elearning facilities (Bijlani et al., 2008).

Biotechnology lab courses richly rely upon new up-to-date content and various techniques that require a new synergy of knowledge and experimental implementation. Hence a new kind of experimental science that can be brought as a virtual simulation based laboratory is necessary. The developments of the virtual labs include mathematical techniques in biology to study, to hypothesize and to demonstrate complex biological functions. However virtual labs in heavy engineering topics such as analyzing nanomaterials with high-power microscopes and lab courses in biotechnology or biology will also have to exploit multiple techniques besides simulators alone as many scenarios cannot be reproduced mathematically while retaining the "real" lab-like feel.

In this chapter, we focus on the development and use on the virtual biotechnology laboratory courses through a combination of techniques to try completing the learning experience as that of a regular University laboratory.

#### **2. Why virtual labs?**

There are many main reasons to focus on creating virtual labs for University education (Auer et al., 2003). Among the primary reasons include the cost and lack of sufficient skillset for facing the current growth in biotechnology sector. The setup cost of laboratories puts a large overhead on the educators. The Universities also need to setup laboratories to educate sufficient target group with the details of common biotechnological techniques and protocols (O'donoghue et al., 2001).

Another new motivation is the need to introduce and focus well-explored potential virtual lab areas which use computational methods, mathematical modeling and biophysics, computational biology and computational neuroscience. Computational biology and biophysics are upcoming areas and most techniques derive basis from real laboratory experiments. Another intention of using virtual labs via a computational approach is to train young scientists in the field of the mathematical thinking for life sciences and related environments. Main goals of cross-disciplinary sciences include the need to ensure that the students will be able to integrate different exhaustive models into a larger framework, i.e. in the perspective of comprehensive biological systems such as cells and biological networks. Such a role will also give an overview of the modeling approaches that are most appropriate to describe life-science processes. For the everyday biologist, the major use of virtual labs will also be in the learning perspective of advanced but common-to-use simulation tools.

Virtual labs and use of virtual tools should lead to an increase the awareness of a crucial need for standard model descriptions. Most simulators and common-use tools require various formats and schema and with the explosion of data, the use of virtual labs across the country or across multiple countries is also intended to unite educators to work towards common model descriptions and standardization of their data.

For the biotechnology sector, a highly favoring motivation for the shift to the virtual lab paradigm is the explosion of data-rich information sets, due to the genomics revolution, which are difficult to understand without the use of analytical tools. Also, recent development of mathematical tools such as chaos theory to help understand complex, nonlinear mechanisms in biology seems to push the need for information-rich virtual labs in simulation domain.

To aid further, an increase in computing power which enables calculations and simulations to be performed that were not previously possible, have set a new trend in the concept and use of computing. Simulations in the past that needed more intensive computers now can plainly be run through long battery-life laptops (Aycock et al., 2008), given that in many cases laptops today even host servers.

A slightly different reason that also pushes the concept of virtual labs for undergraduate and master level education at the Universities also seems to be an increasing interest in in silico experimentation due to ethical considerations, risk, unreliability and other complications involved in human and animal research.

Given all above reasons and motivation, virtual labs are today's experimental approach towards a newer trend in future education. However the virtual lab environments are still under severe testing and newer models seems to switch to more intelligent and adaptive platforms that can yield efficient knowledge dissipation. One such common model is the adaptive learning system (ALS) currently employed by many e-learning applications strewed on the internet.

#### **3. Other virtual labs and online courses in biosciences**

Very little work has been actually done in the biology sector. There are online "dissections" of frog tutorials by Mable Kinzie developed in 1994 and an improved version of the same was hosted in 2002 (http://curry.edschool.Virginia.EDU/go/frog/menu.html). Quick "movies": http://www.bio.unc.edu/faculty/goldstein/lab/movies.html Virtual "experiments": Biology Labs On-Line (BLOL) is a collaboration of the California State University system Center for Distributed Learning and Addison Wesley Longman, with partial funding provided by the National Science Foundation (http://biologylab.awlonline.com). A project titled "BIOTECH Project" developed by University of Arizona, with aim of supporting Arizona teachers to conduct molecular genetics (DNA science) experiments with their students and assists teachers in developing new activities for their classroom (http://biotech.bio5.org/home). "Protein Lab" by A.J. Booth, is a computer simulation of protein purification. These labs are extremely helpful for beginners in the art of protein purification. It gives them a chance to get beyond the details of individual techniques and get a sense of the overall process of a protein purification strategy. (http://www.booth1.demon.co.uk/archive).To enhance education, there is a great need for individualized courseware to provide educational content that fits to the learner's learning style and knowledge base. University of Utah's genetic science learning center has its very animated genetics labs at http://learn.genetics.utah.edu/gslc. The labs were developed with the mission in making science easy for everyone to understand. Similar projects at Howard Hughes http://www.hhmi.org/biointeractive/vlabs and at Pearson's http://www.phschool.com/science/biology\_place/labbench have been useful as virtual education websites.

Online biotechnology courses are available through several leading universities around the world, including the Massachusetts Institute of Technology (MIT), Osaka University and the Open University. OpenCourseWare (OCW) from TUFTS and MIT offer courses on the Web that containing all or some of the materials from the university's original oncampus classrooms. Many biotechnology courses on OCW make use of several different learning materials available online or by download, lab notes, assignments, lectures on scientific communications and study materials. Online biotechnology courses are known to be very helpful for students to study/prepare for the positions as lab technicians, research assistants and quality assurance analysts in such fields as agriculture, pharmaceuticals and manufacturing.

#### **4. Amrita VL**

382 Innovations in Biotechnology

the students will be able to integrate different exhaustive models into a larger framework, i.e. in the perspective of comprehensive biological systems such as cells and biological networks. Such a role will also give an overview of the modeling approaches that are most appropriate to describe life-science processes. For the everyday biologist, the major use of virtual labs will also be in the learning perspective of advanced but common-to-use

Virtual labs and use of virtual tools should lead to an increase the awareness of a crucial need for standard model descriptions. Most simulators and common-use tools require various formats and schema and with the explosion of data, the use of virtual labs across the country or across multiple countries is also intended to unite educators to work towards

For the biotechnology sector, a highly favoring motivation for the shift to the virtual lab paradigm is the explosion of data-rich information sets, due to the genomics revolution, which are difficult to understand without the use of analytical tools. Also, recent development of mathematical tools such as chaos theory to help understand complex, nonlinear mechanisms in biology seems to push the need for information-rich virtual labs in

To aid further, an increase in computing power which enables calculations and simulations to be performed that were not previously possible, have set a new trend in the concept and use of computing. Simulations in the past that needed more intensive computers now can plainly be run through long battery-life laptops (Aycock et al., 2008), given that in many

A slightly different reason that also pushes the concept of virtual labs for undergraduate and master level education at the Universities also seems to be an increasing interest in in silico experimentation due to ethical considerations, risk, unreliability and other

Given all above reasons and motivation, virtual labs are today's experimental approach towards a newer trend in future education. However the virtual lab environments are still under severe testing and newer models seems to switch to more intelligent and adaptive platforms that can yield efficient knowledge dissipation. One such common model is the adaptive learning system (ALS) currently employed by many e-learning applications

Very little work has been actually done in the biology sector. There are online "dissections" of frog tutorials by Mable Kinzie developed in 1994 and an improved version of the same was hosted in 2002 (http://curry.edschool.Virginia.EDU/go/frog/menu.html). Quick "movies": http://www.bio.unc.edu/faculty/goldstein/lab/movies.html Virtual "experiments": Biology Labs On-Line (BLOL) is a collaboration of the California State University system Center for Distributed Learning and Addison Wesley Longman, with partial funding provided by the National Science Foundation (http://biologylab.awlonline.com). A project titled "BIOTECH Project" developed by University of Arizona, with aim of supporting Arizona teachers to conduct molecular genetics (DNA science) experiments with their students and assists teachers

common model descriptions and standardization of their data.

simulation tools.

simulation domain.

strewed on the internet.

cases laptops today even host servers.

complications involved in human and animal research.

**3. Other virtual labs and online courses in biosciences**

Amrita University's Virtual and Accessible Laboratories Universalizing Education (VALUE) initiative was initially targeted towards making biotechnology, physics and chemistry courses virtually accessible for undergraduate and postgraduate education. The project led to the development of 14 labs in biotechnology and 13 labs in physical and chemical sciences. The schema of virtual labs was based on one of our studies.

An average survey of the VL framework software was performed and the tests were shown (see Table 1 in Diwakar et al., 2011). The developed virtual labs are available for public use (See http://amrita.edu/virtuallabs). Any user may login with an open-id or Google's gmail account and access the authentication-compulsory regions such as the remote-panel, simulator and animations. The website uses the name and email address that provider gives only to set up an user account.

#### **5. Techniques – Animation, simulations and remote-triggered experiments**

The key learning component in many biological laboratories is the complexity of the procedure and details of the step-by-step protocol carried out in the laboratory. Although some of these biological processes can be replaced by mathematical equations modeling the system, most of the "feel" is in performing the detailed procedure which is not derived from sets of equations. Graphical animations deliver a high degree of the reality to the virtual labs through their seeming closeness to the appearance and feel of the lab. Graphical animations also cut out the complexity of the modeling process by increasing the "feel" of experiment. Like the proverb goes, "a picture is worth a thousand words", animations reveal better information that cannot be easily conveyed via text alone or static illustration.

In our biotechnology virtual labs, the animation type of experiments include the use of 2D flash based animations for illustrating detailed procedures such as wet lab protocols and heavy engineering techniques that are out of scope for simulation due to various reasons like complicated equations, numerical issues in simulation, lack of modeling data etc. Besides animation, another common technique in our virtual labs included engineeringbased approaches such as remote-triggered experiments or remote-controlled experiments.

The very common and research-inspiring approach is the use of mathematical simulators to model biological and biotechnological processes or sub-processes. Although mathematics has long been intertwined with the biological sciences, an explosive synergy between biology and mathematics seems poised to enrich and extend both fields greatly in the coming decades. Among the various scenarios to study biology and disseminate information effectively and efficiently, includes the use of e-learning as a medium to offer courses.

Applying mathematics to biotechnology for virtual lab creation has recently turned into an explosion of interest in the field. The NASA virtual laboratory or the HHMI virtual labs at Howard Hughes Medical Institute or the Utah genetics virtual laboratory are some examples.

For our labs, a combination of user-interactive animation, mathematical simulations, remote-trigger of actual equipment and the use of augmented perception haptic devices are used to deploy effectively the real laboratory feel of a biotech lab online.

#### **6. Models in biology – As virtual labs**

Design of simulation labs requires basic mathematical models. Some models that were used to develop the virtual labs are listed below.

#### **6.1 Neurophysiology and neuronal biophysics**

In order to understand neuronal biophysics and simulations on voltage clamp and current clamp in detail, we modeled a section of excitable neuronal membrane using the Hodgkin-Huxley equations (Hodgkin and Huxley, 1952) that can be accessed a graphical web-based simulator. Various experiments using this simulator deal with the several parameters of Hodgkin-Huxley equations and will model resting and action potentials, voltage and current clamp, pharmacological effects of drugs that block specific channels etc. This lab complements some of the exercises in the Virtual Neurophysiology lab.

#### **6.2 Population ecology**

As part of population ecology virtual labs, we developed a set of mathematical ecology models to understand the basic dynamics and behavior of population in various aspects. Some models include:

the "feel" of experiment. Like the proverb goes, "a picture is worth a thousand words", animations reveal better information that cannot be easily conveyed via text alone or static

In our biotechnology virtual labs, the animation type of experiments include the use of 2D flash based animations for illustrating detailed procedures such as wet lab protocols and heavy engineering techniques that are out of scope for simulation due to various reasons like complicated equations, numerical issues in simulation, lack of modeling data etc. Besides animation, another common technique in our virtual labs included engineeringbased approaches such as remote-triggered experiments or remote-controlled

The very common and research-inspiring approach is the use of mathematical simulators to model biological and biotechnological processes or sub-processes. Although mathematics has long been intertwined with the biological sciences, an explosive synergy between biology and mathematics seems poised to enrich and extend both fields greatly in the coming decades. Among the various scenarios to study biology and disseminate information effectively and efficiently, includes the use of e-learning as a medium to offer

Applying mathematics to biotechnology for virtual lab creation has recently turned into an explosion of interest in the field. The NASA virtual laboratory or the HHMI virtual labs at Howard Hughes Medical Institute or the Utah genetics virtual laboratory are some

For our labs, a combination of user-interactive animation, mathematical simulations, remote-trigger of actual equipment and the use of augmented perception haptic devices are

Design of simulation labs requires basic mathematical models. Some models that were used

In order to understand neuronal biophysics and simulations on voltage clamp and current clamp in detail, we modeled a section of excitable neuronal membrane using the Hodgkin-Huxley equations (Hodgkin and Huxley, 1952) that can be accessed a graphical web-based simulator. Various experiments using this simulator deal with the several parameters of Hodgkin-Huxley equations and will model resting and action potentials, voltage and current clamp, pharmacological effects of drugs that block specific channels etc. This lab

As part of population ecology virtual labs, we developed a set of mathematical ecology models to understand the basic dynamics and behavior of population in various aspects.

used to deploy effectively the real laboratory feel of a biotech lab online.

complements some of the exercises in the Virtual Neurophysiology lab.

**6. Models in biology – As virtual labs**

to develop the virtual labs are listed below.

**6.2 Population ecology**

Some models include:

**6.1 Neurophysiology and neuronal biophysics**

illustration.

experiments.

courses.

examples.

#### **6.3 Biochemistry, cell biology, microbiology, immunology and molecular biology**

Simple linear equations were used to understand molecular mass flow in AGE, PAGE exercises. No differential equations were used in biology oriented virtual labs where the focus was on the look and feel. In many cases, animation played a major role in these areas rather than mathematical simulations. As in the case of realistically animating experiments there are a lot of advantages; although it cannot be considered as a complete replacement of real labs due to its limitations. One solution was to provide the necessary details of the instruments we were using for the lab. Per say, if we use cooling centrifuges for an experiment in the virtual lab, one may not fully show all details corresponding to the operating methods of the centrifuge. But in the case of a real laboratory the student gets an opportunity to have a hands-on experience on the equipment while doing the experiment. Also, many of the experiments require instrumentation facilities. Also instruments from different companies have slight differences in design and operating mechanisms, which may not be shown in the virtual labs. Thus even though virtual lab meets the major target, it shadows the minor details of the experiments. Not all parameters such as changes in temperature during an experiment especially (where small changes do not matter) may not be included in the virtual lab for the sake of simplicity. In a real lab, curious students can perform these kinds of interesting experiments but to do the same in virtualized experiments is difficult.

#### **7. Major challenges**

Setting and developing AMRITA virtual labs (see Fig. 2) as a complete learning experience has not been an easy task. Amongst the major challenges we faced included usage/design scalability, deliverability efficiency, network connectivity issues, security and speed of adaptability to incorporate and update changes into existing experiments.

Owing to the scientific domain, biotechnology lends the following challenges to establishing virtual labs:

In order to address some of these issues and to overcome restrictions, we deployed virtual lab experiments as web-client based animations or simulators besides remote triggered experiments. The virtual lab was based on a website that was designed for favorable use within intranets and internets. However, efficiency depended on the internet bandwidth and connectivity. Our target was any campus with a download link of 256kbps should suffice. To retain this compatibility the animations had to be size-delimited. To overcome the problem, longer experiments had to be sliced to smaller portions, each loading in sequence. This was possible as we maintained the virtual lab experiments as flash animations (Adobe, USA). Having labs in flash environments allowed the scalability and access although flash based action script programming needed additional programmers and training.

Other e-learning issues such as student-teacher collaboration via chat, video interfacing etc. were overcome via AVIEW-like environment (Bijlani et al, 2008). The intention of the virtual labs was also to extend the facility to develop an applied computational laboratory.

#### **8. Methodology**

Amongst others, the focus of having and designing virtual labs was also based on John Keller's ARCS model of motivation. Design of courses, simulations and models for computational approaches in biology will be the highlight. A lot of attention was on courses whose content will be applicable to the existing P.G. programs.

perform these kinds of interesting experiments but to do the same in virtualized

Setting and developing AMRITA virtual labs (see Fig. 2) as a complete learning experience has not been an easy task. Amongst the major challenges we faced included usage/design scalability, deliverability efficiency, network connectivity issues, security and speed of

Owing to the scientific domain, biotechnology lends the following challenges to establishing

• The development of analytical solutions in the arena is limited as biological processes are typically non-linear and are coupled systems of differential equations in various

• The mathematics behind models is hidden by their complexity and appears refined

• Most simulation platforms need direct hands-on experience between teachers and

• Besides, such courses also need simultaneous theoretical explanations which may need classroom-like scenarios with video presentations, white-board and other tools. We could overcome the issue here using a collaborative suit, AVIEW (Bijlani et al., 2008).

In order to address some of these issues and to overcome restrictions, we deployed virtual lab experiments as web-client based animations or simulators besides remote triggered experiments. The virtual lab was based on a website that was designed for favorable use within intranets and internets. However, efficiency depended on the internet bandwidth and connectivity. Our target was any campus with a download link of 256kbps should suffice. To retain this compatibility the animations had to be size-delimited. To overcome the problem, longer experiments had to be sliced to smaller portions, each loading in sequence. This was possible as we maintained the virtual lab experiments as flash animations (Adobe, USA). Having labs in flash environments allowed the scalability and access although flash based action script programming needed additional programmers and

Other e-learning issues such as student-teacher collaboration via chat, video interfacing etc. were overcome via AVIEW-like environment (Bijlani et al, 2008). The intention of the virtual

Amongst others, the focus of having and designing virtual labs was also based on John Keller's ARCS model of motivation. Design of courses, simulations and models for computational approaches in biology will be the highlight. A lot of attention was on courses

labs was also to extend the facility to develop an applied computational laboratory.

whose content will be applicable to the existing P.G. programs.

• The number of students that can be catered at any given time is restricted.

• There are not many courses in India developed for this scenario.

adaptability to incorporate and update changes into existing experiments.

experiments is difficult.

**7. Major challenges**

virtual labs:

forms.

students.

training.

**8. Methodology**

through simulation platforms.

Fig. 1. Sakshat Amrita virtual labs. Accessible at http://amrita.edu/virtuallabs

For all biotech virtual labs, we had set the following lab-level objectives as general guidelines.

The implementation of animation and simulation based virtual labs was mainly done in Action Script 3 in Adobe flash in order to bring better definition to 2-D graphics. Action script allowed flash swf files as output thereby allowing both a better look-and-feel and an enhanced interactivity with the software. The physics simulator tools worked reasonably well. We did not use java as a programming medium in our learning tool to make sure we have complete cross-OS, cross-browser compatibility, to reduce initial loading time and also to consider support for the commercial operating systems such as Microsoft's Windows platform that support flash better than Java plug-in.

We used a new VLCOP platform (Nedungadi et al., 2011) in its full functionality for the virtual labs. The minor intention was to deploy preliminary platform with a learning environment and later render the environment adaptive and intelligent as per the user-audience. The main reason to precursor with such a test was cost-efficiency. Cost-efficiency of e-learning programs has been increasingly important because some institutions have failed due to the lack of wellthought out financial plans (Wentling et al, 2002; Morgan, 2000).

Virtual Labs use self-assessment based on questionnaire to evaluate user's experience. Although not implemented, an advanced form of the lab is being planned to include teacher's assessment, peer-assessment and collaborative assessment. Teacher assessment will actually have a "real" instructor on the deployment site to evaluate the lab user/student. Peer-assessment will include any student or teacher to assess another. Collaborative assessment will include both the instructor and the student to perform assessment on the completion of an experiment.

For our installation and deployment, we focused to reduce internet downtime. A 2004 study indicated that overall downtime costs companies an average of 3.6% of annual revenue (internet sources, see www.sentinelbussiness.it) indicating leading causes for downtime being software failure and human error. Through our studies, we managed to reduce unnecessary events and maintain downtime to less than 27 minutes for 6 months (not as in Amrita Learning software, see Table I in Diwakar et al., 2011). However, this could be because of our lack of full incorporation of the complex adaptive learning system as it was done for the schools where it was tested. However a test on real-time upgrade to such a model based on our previous experiences (data not shown) with Amrita learning (Nedungadi and Raman, 2010) indicated that overall loss of virtual lab in terms of downtime will be significantly less.

#### **9. Feedback and assessment**

Feedback is usually not used as an evaluator but an assessment tool for student quality. With that in mind, the virtual lab evealuation criterion was focussed on measuring and estimating the student's involvement in the particular experiment of a particular lab. A way to increment the quantity and timing of feedback is to provide enough detail. Through animation, we have also increased evaluatory criterion and details in the virtual environments. It was noted that in more than 95 experiments performed by more than 30 people within a particular time-window there were more than 91% of appreciation (further statistics pending, data not shown) when two experiments, one with detail oriented interactive animation and other without interaction were delivered to assess the involvement of the students in terms of their self-assessment.

The implementation of animation and simulation based virtual labs was mainly done in Action Script 3 in Adobe flash in order to bring better definition to 2-D graphics. Action script allowed flash swf files as output thereby allowing both a better look-and-feel and an enhanced interactivity with the software. The physics simulator tools worked reasonably well. We did not use java as a programming medium in our learning tool to make sure we have complete cross-OS, cross-browser compatibility, to reduce initial loading time and also to consider support for the commercial operating systems such as Microsoft's Windows

We used a new VLCOP platform (Nedungadi et al., 2011) in its full functionality for the virtual labs. The minor intention was to deploy preliminary platform with a learning environment and later render the environment adaptive and intelligent as per the user-audience. The main reason to precursor with such a test was cost-efficiency. Cost-efficiency of e-learning programs has been increasingly important because some institutions have failed due to the lack of well-

Virtual Labs use self-assessment based on questionnaire to evaluate user's experience. Although not implemented, an advanced form of the lab is being planned to include teacher's assessment, peer-assessment and collaborative assessment. Teacher assessment will actually have a "real" instructor on the deployment site to evaluate the lab user/student. Peer-assessment will include any student or teacher to assess another. Collaborative assessment will include both the instructor and the student to perform

For our installation and deployment, we focused to reduce internet downtime. A 2004 study indicated that overall downtime costs companies an average of 3.6% of annual revenue (internet sources, see www.sentinelbussiness.it) indicating leading causes for downtime being software failure and human error. Through our studies, we managed to reduce unnecessary events and maintain downtime to less than 27 minutes for 6 months (not as in Amrita Learning software, see Table I in Diwakar et al., 2011). However, this could be because of our lack of full incorporation of the complex adaptive learning system as it was done for the schools where it was tested. However a test on real-time upgrade to such a model based on our previous experiences (data not shown) with Amrita learning (Nedungadi and Raman, 2010) indicated that overall loss of virtual lab in terms of downtime

Feedback is usually not used as an evaluator but an assessment tool for student quality. With that in mind, the virtual lab evealuation criterion was focussed on measuring and estimating the student's involvement in the particular experiment of a particular lab. A way to increment the quantity and timing of feedback is to provide enough detail. Through animation, we have also increased evaluatory criterion and details in the virtual environments. It was noted that in more than 95 experiments performed by more than 30 people within a particular time-window there were more than 91% of appreciation (further statistics pending, data not shown) when two experiments, one with detail oriented interactive animation and other without interaction were delivered to assess the

platform that support flash better than Java plug-in.

assessment on the completion of an experiment.

will be significantly less.

**9. Feedback and assessment**

thought out financial plans (Wentling et al, 2002; Morgan, 2000).

involvement of the students in terms of their self-assessment.

Fig. 2. Neuron simulator. The Neuron simulator lab uses Hodgkin-Huxley equations to study and analyze the action potential properties. The simulator allows some pharmacological studies and complements the neurophysiology virtual lab.

#### **10. Case study: Virtual neurophysiology laboratory**

Our preliminary studies in the biotech sector were on neurophysiology techniques. The virtual neurophysiology laboratory provides an opportunity for students to substitute classroom physiology course with detailed techniques and protocols of a real laboratory. Besides the material like chemicals, physiology demands extensive knowledge and experience from the instructor. For example, rat brain slicing protocol which is the first experiment (in the virtual lab) takes approximately 6-10 hours to complete training and about 2-3 weeks to train one student in a real laboratory.

With the focus on time (Rohrig et al., 1999) and learning know-how, we adapted the usual lab experimental protocols as user-interactive animations of the neurophysiology lab experience. The work involved both animators and programmers. For some experiments such as brain slice preparation, animations were sufficient whereas for some others such as Hodgkin-Huxley neuronal model (Hodgkin et al., 1952, see Fig. 3.) for demonstrating behavior of single neurons, we used Java based simulator. The same simulator was embedded into other experiments such as voltage clamp protocol and current clamp protocol to allow the student to see the corresponding behavior as seen in real neurons (Koch, 1999).

A new set of experiments developed included the use of electronic resistance-capacitance (RC) circuits that could be remotely triggered as mimicking the electrical dynamics of a passive neuronal membrane. Passive neuronal membranes are modeled as RC-circuits with high resistance and low capacitance (for more details see Koch, 1999). In the simulation lab that was developed to complement the exercises of the VL, a model detailed study was added. Some of the main objectives and experiments using a neuron simulator included:

Also as part of the labs, we follow a particular formatting for each experiment within the lab. The goal was to allow the student to study the theory, the approach and do a self-test before actually going into the simulator or the virtual experiment. Covering some explanations and incorporating the same theory into the actual "lab" part of the experiment has been one of the primary goals. Each experiment in the labs (especially in Biotechnology) opens by default with the textual theory, which can also be randomly accessed by clicking on the icon "theory".

All the control and experimental parameters are explained in the "manual". The instructor and the student are informed on how various parameters change in the experiment in the very context of the virtual experimental lab procedure. For those experiments that have both an animation learning component and simulator component, each of the user controls and the variable parameters are explained. Also included in the manual is a help that actually explains the usage of radio controls and icons covered by the experiment. The intention was to evaluate the basic info that once the student completes the familiarization process by going through the theory and manual sections, he/she can take a "self-evaluatory" quiz module that chooses to test the student on some questions based on the theory background of the experiment.

The "simulator" tab actually leads to the experiment workbench. "Protocol for brain slicing" that is actually a detailed lab process that would take 6-10 weeks for post-master's student to learn and about 3-10 hours per procedure. That experiment we have virtualized by means of an interactive action script based animation. The second neurophysiology experiment concerns the modeling of a neuronal cell. In this case we have used a Flash based learning component along with a HH-simulator of a biophysical neuron.

The "assignment" icon is the lab experiment question with which intention the student performs the experiment. An instructor version of the assignment will include a model

A new set of experiments developed included the use of electronic resistance-capacitance (RC) circuits that could be remotely triggered as mimicking the electrical dynamics of a passive neuronal membrane. Passive neuronal membranes are modeled as RC-circuits with high resistance and low capacitance (for more details see Koch, 1999). In the simulation lab that was developed to complement the exercises of the VL, a model detailed study was added. Some of the main objectives and experiments using a neuron simulator included:

Also as part of the labs, we follow a particular formatting for each experiment within the lab. The goal was to allow the student to study the theory, the approach and do a self-test before actually going into the simulator or the virtual experiment. Covering some explanations and incorporating the same theory into the actual "lab" part of the experiment has been one of the primary goals. Each experiment in the labs (especially in Biotechnology) opens by default with the textual theory, which can also be randomly

All the control and experimental parameters are explained in the "manual". The instructor and the student are informed on how various parameters change in the experiment in the very context of the virtual experimental lab procedure. For those experiments that have both an animation learning component and simulator component, each of the user controls and the variable parameters are explained. Also included in the manual is a help that actually explains the usage of radio controls and icons covered by the experiment. The intention was to evaluate the basic info that once the student completes the familiarization process by going through the theory and manual sections, he/she can take a "self-evaluatory" quiz module that chooses to test the student on some

The "simulator" tab actually leads to the experiment workbench. "Protocol for brain slicing" that is actually a detailed lab process that would take 6-10 weeks for post-master's student to learn and about 3-10 hours per procedure. That experiment we have virtualized by means of an interactive action script based animation. The second neurophysiology experiment concerns the modeling of a neuronal cell. In this case we have used a Flash based learning

The "assignment" icon is the lab experiment question with which intention the student performs the experiment. An instructor version of the assignment will include a model

• Modeling sodium ion channel and its effect on neural signaling

• Understanding pharmacological implications of ionic currents

questions based on the theory background of the experiment.

component along with a HH-simulator of a biophysical neuron.

• Modeling delayed rectifier potassium channels • Modeling passive membrane properties

• Capacitive transients using Voltage Clamp • Effect of temperature on neuronal dynamics

accessed by clicking on the icon "theory".

• Modeling action potential • Modeling resting potential

• Current clamp protocol • Voltage clamp protocol

• Plotting F-vs-I curve • Plotting V-vs-I curve solved question or key tips in case of a protocol-like experiment. Additional reading material and reference information and other details will be found in our "misc info" icon.

Among the various methodologies the lab covers simulation-based, animation-based and remote-triggered experiments. The simulator was that of a bio-realistic model cell and was combined with an interactive animation-based learning-tool made using Flash. Maldarelli et al. (2009) report the advantage of virtual lab demonstration as an effective lab tool. The remote-triggered experiments were based on real electronic circuits that mimic the phenomenon observed in neuronal cells. The basic behavior of Resistancecapacitance circuits that can be modified remotely by a user to study and imitate real neuronal circuits as he/she does in a neuronal biophysics laboratory on a patch of a neuronal membrane.

Fig. 3. Remote-triggered Experiment. Remote panel is also made with re-configurable panels and control options. This experiment emulates action potential generation using analog neurons.

We also tested the virtual lab via a questionnaire-based feedback for overall quality. Among the major questions, several virtualizations related questions were presented in the questionnaire. The general developer/designer related questions included in the lab were to rate the experiment that was most recently completed, extent of control on the interface, closeness to lab environment and feel, measurement and analysis of data, user-manual quality, adequacy of bibliography and references, results interpretation, whether any clear information was gained by using the virtual lab, any problems faced, how helpful the lab was and overall motivation.

Fig. 4. Tiger population study. Using exponential growth patterns to predict tiger population in India.

#### **11. Taking project tiger to the classroom: A virtual lab case study**

Using a virtual lab was not our only objective. We wanted to test a real scenario and see if the virtual lab could be used a as research tool. Tiger population study uses virtual labs to take India's Project tiger to the classroom. Half of the tiger population in the world is in India. Due to reduction in their population in large numbers, from 1969 onwards the 'tiger' was declared as an endangered species (by CITES). Educating about tiger populations is vital. Typically courses in population ecology deal about population variations. In this section, we suggest on the applicative use of population ecology simulators as classroom models to complete the learning experience for a population ecology laboratory course. This section also reports the analysis, interpretation and some preliminary predictions in variations of tiger population in India.

Here we used the exponential growth model experiment in population ecology lab 1 (at http://amrita.edu/virtuallabs). First step is to select an experiment followed by selecting a mathematical model which is described in the experiment from the set of experiments (see Fig. 4). Some existing data tested indicated the validation of the technique. (Fig 5B).

#### **11.1 Data collection**

392 Innovations in Biotechnology

quality, adequacy of bibliography and references, results interpretation, whether any clear information was gained by using the virtual lab, any problems faced, how helpful the lab

Fig. 4. Tiger population study. Using exponential growth patterns to predict tiger

**11. Taking project tiger to the classroom: A virtual lab case study**

Using a virtual lab was not our only objective. We wanted to test a real scenario and see if the virtual lab could be used a as research tool. Tiger population study uses virtual labs to take India's Project tiger to the classroom. Half of the tiger population in the world is in India. Due to reduction in their population in large numbers, from 1969 onwards the 'tiger' was declared as an endangered species (by CITES). Educating about tiger populations is vital. Typically courses in population ecology deal about population variations. In this section, we suggest on the applicative use of population ecology simulators as classroom models to complete the learning experience for a population ecology laboratory course. This section also reports the analysis, interpretation and some preliminary predictions in

Here we used the exponential growth model experiment in population ecology lab 1 (at http://amrita.edu/virtuallabs). First step is to select an experiment followed by selecting a

was and overall motivation.

population in India.

variations of tiger population in India.

Statistical data for this study was collected from Project Tiger which includes the tiger population from 1972 – 2002 of various tiger reserves (see Table 1). And the second data set was the crime reported for the numbers of tiger that have been killed in past few years were from WPSI's Wildlife Crime Database (14. WPSI's Tiger Poaching Statistics, http://www.wpsi-india.org/statistics/index.php).

Fig. 5. Virtual Lab model for tiger population study. Note that exponential model was chosen based on decline in populations. Predictions with other models such as Lotka-Volterra and Logistic growth were inappropriate or had errors.

Growth rate has been calculated by using the formula, Growth rate (t 1) (t 1) t ( ) <sup>N</sup> *g t* + + − <sup>=</sup> . where

N( 1) *<sup>t</sup>*<sup>+</sup> is the total number of individuals at t+1, t' represent the time in years.

#### **11.2 On-screen methods**

We have used an adaptive growth rate for different periods as shown in Table 1. Simulator's viewable window contain three main tabs, 1) Statistics button will show the growth of population while the simulation is running 2) Data plots button, will Population size Vs Time, 3) Worksheet button is an implementation of the model in excel.

#### **11.3 Assumptions with tiger populations and growth model**

Population ecology models include several assumptions, in order to realistically apply the model on data. Growth of prey population is exponential in absence of predators;

Although the assumptions make it difficult to actually call the simulation 'realistic', the validations showed a realistic trend and hence for this model, a simple exponential growth simulator was used.

Calculated average growth rate was = 0.1545

Table 1. Shows the statistical data for tiger population from 1972 – 2002 and extended (prediction column) the curve to 2012 using continuous growth model simulator (data collected from Tiger project India at http://projecttiger.nic.in/populationinstate.asp).

#### **11.4 Results**

Taking real data to class rooms have been very difficult with population ecology due to its high unpredictability and model-related unreliability. However, using a simple growth rate simulator and using patterns from a short period, the model shows promise.

The simulation showed predictability in the growth of tiger population in India for the years 2003- 2012 by extending the behavioral pattern of tiger population in India for last 30 years (see Fig.6). Predicted tiger population for the year 2007 was 1664. According to National Tiger Conservation Authority (on 2008), the total tiger population reaches 1,411 (i.e. ranging between 1,165 and 1,657). The difference in number of tigers from predicted to this statistical report may be because of some environmental factors, number of tigers that have been killed in past few years and the census by National Tiger Conservation Authority was only partially included West Bengal.

#### **11.5 Conclusion**

394 Innovations in Biotechnology

We have used an adaptive growth rate for different periods as shown in Table 1. Simulator's viewable window contain three main tabs, 1) Statistics button will show the growth of population while the simulation is running 2) Data plots button, will Population size Vs

Population ecology models include several assumptions, in order to realistically apply the

• During the process, the environment does not change in favour of one species and the

Although the assumptions make it difficult to actually call the simulation 'realistic', the validations showed a realistic trend and hence for this model, a simple exponential growth

> **Total number of tigers predicted by the model**

Table 1. Shows the statistical data for tiger population from 1972 – 2002 and extended (prediction column) the curve to 2012 using continuous growth model simulator (data collected from Tiger project India at http://projecttiger.nic.in/populationinstate.asp).

simulator and using patterns from a short period, the model shows promise.

Taking real data to class rooms have been very difficult with population ecology due to its high unpredictability and model-related unreliability. However, using a simple growth rate

The simulation showed predictability in the growth of tiger population in India for the years 2003- 2012 by extending the behavioral pattern of tiger population in India for last 30 years (see Fig.6). Predicted tiger population for the year 2007 was 1664. According to National

1972 268 - - 1979 711 - 0.6230 1984 1121 - 0.3657 1989 1327 - 0.1552 1993 1366 - 0.0285 1995 1333 - -0.0247 1997 1498 1521 0.1236 2002 1576 1586 0.0409 2007 - 1664 0.0468 2012 - 1718 0.0314

**Growth rate**

model on data. Growth of prey population is exponential in absence of predators;

• Tiger population grows/declines exponentially within a short duration (10 years)

Time, 3) Worksheet button is an implementation of the model in excel.

**11.3 Assumptions with tiger populations and growth model**

• The rate of change of tiger population is proportional to its size.

**Total number of tigers**

genetic adaptation is sufficiently slow.

**Year**

Calculated average growth rate was = 0.1545

**11.2 On-screen methods**

simulator was used.

**11.4 Results**

The predicted data (Table.1, third column) for tiger population in India showed standard deviation of 10% from real data. With some assumptions, it was possible to use simple models like exponential growth models for studying tiger populations. For a very short duration (such as in the data shown in Table 1), basic growth show a slowly saturating exponential and hence data matched the predictions (see Fig. 6). Online population ecology experiments developed on the basis of mathematical equations could help students to get a deeper understanding on model dynamics by exploring the parameter space provided by the model. Also it is always feasible for the user to supply the real data as input and observe the corresponding dynamics. The possibility to study such experiments has value. Biotechnology studies often include data collection and such models allow building simple hypothesis based on the dynamics. This new e-learning environment engaged and motivated the students to practice and explore the parametric space provided for the population ecology experiments.

Newer studies for analyzing fish populations and deer populations are being developed as part of the ongoing process. Such data will be made available as a virtual lab for continued use and study. We also noticed that the undergraduate and postgraduate students show an increased attention to details when we trained them on virtual labs instead of plainly explaining the theory. There was a 23% (metric not shown) improvement in interest to critically analyze population models among students who were introduced to population ecology studies directly virtual labs.

#### **12. Cost of virtual labs**

In order to estimate the true financial cost of our virtual lab project, we had to include both project development and delivery and maintenance costs. As indicated by Kruse (see http://www.e-learningguru.com/articles/art5\_2.htm), design of courseware needed more initial costs than instructor-led learning but delivery and maintenance is affordably cheaper. We estimate, based on Amrita learning software experience that there will be negligible costs for maintain web-based experiments. The main post-deployment costs included administration and maintenance. The administration and maintenance estimates included tracking of user-behavior, technical support, content updates and technology updates. Student material development, instructor costs and subject expert costs were included in the development expenses.

#### **13. Some evaluatory setbacks and associated feedback**

What we know from the Virtual Lab studies performed is that user-involvement in assessment is vital for improving the knowledge-experience for the user. Self-assessment hints preliminary results but are not comprehensive. Users tend to show implicit behavior patterns indicating favor of the tool rather than the experiment for their choice of vote. Interactional voting behavior is also dependent on age and other characteristic learner attitudes. In our studies, younger students mostly at the undergraduate level evaluated the tool using mid-range scores compared to the varying yet favorably high votes of the Master's and Graduate students in the feedback assessment. Although this may need further testing, we believe that scores from the higher age-experience level indicated statistically relevant reliability much more than undergraduates (data not shown due to pending experiments).

Overall, 27 Master's level students who helped in intensive evaluation of the Virtual lab platform as part of their regular class-room course, appeared predominantly positive about the value of virtual labs in e-learning, but anxieties were also expressed about the potential for e-labs to replace face-to-face teaching and labs in the economically challenged regions of the country apart an indication either of the value on the personal and face-to-face tutoring through an expressed preference for it. Students who were positive about their experiences of virtual labs indicated that they had received appropriate introductions and felt supported by staff, indicating the importance of sound inductions into the use of institutional systems and technologies.

Fig. 6. Growth of tigers with predictions. A. The plot shows the nature / pattern of statistical data for tiger population in India from 1972 – 2002 (blue line) and an extended prediction (red line) of the curve to 2012 using continuous growth model simulator. The model assumes a 10% standard deviation shown by the error bar. B. The plot shows the enlarged curve of predicted piece-wise continuous growth of tiger populations in India

#### **14. Conclusion and further remarks**

Education using VL has been the new venture to better education and provide extensible laboratory experience to University students. The virtual lab protocols for neurophysiology and related sciences have been a successful complement to the usual theoretical education that happens at our school of biotechnology at the level of masters and undergraduate education. Although the elements can be improved, our approach to virtualization has answered many key results in establishing the virtual lab features such as teacherindependent/teacher-friendly approach to e-learning.

patterns indicating favor of the tool rather than the experiment for their choice of vote. Interactional voting behavior is also dependent on age and other characteristic learner attitudes. In our studies, younger students mostly at the undergraduate level evaluated the tool using mid-range scores compared to the varying yet favorably high votes of the Master's and Graduate students in the feedback assessment. Although this may need further testing, we believe that scores from the higher age-experience level indicated statistically relevant reliability much more than undergraduates (data not shown due to

Overall, 27 Master's level students who helped in intensive evaluation of the Virtual lab platform as part of their regular class-room course, appeared predominantly positive about the value of virtual labs in e-learning, but anxieties were also expressed about the potential for e-labs to replace face-to-face teaching and labs in the economically challenged regions of the country apart an indication either of the value on the personal and face-to-face tutoring through an expressed preference for it. Students who were positive about their experiences of virtual labs indicated that they had received appropriate introductions and felt supported by staff, indicating the importance of sound

Fig. 6. Growth of tigers with predictions. A. The plot shows the nature / pattern of statistical data for tiger population in India from 1972 – 2002 (blue line) and an extended prediction (red line) of the curve to 2012 using continuous growth model simulator. The model assumes a 10% standard deviation shown by the error bar. B. The plot shows the enlarged

Education using VL has been the new venture to better education and provide extensible laboratory experience to University students. The virtual lab protocols for neurophysiology and related sciences have been a successful complement to the usual theoretical education that happens at our school of biotechnology at the level of masters and undergraduate education. Although the elements can be improved, our approach to virtualization has answered many key results in establishing the virtual lab features such as teacher-

curve of predicted piece-wise continuous growth of tiger populations in India

**14. Conclusion and further remarks**

independent/teacher-friendly approach to e-learning.

inductions into the use of institutional systems and technologies.

pending experiments).

We tried to avoid the most usual failures in e-learning labs (Romiszowski, 2004) by focusing to avoid the common failures. Our design issues were based on a successfully tested e-learning software environment (Nedungadi and Raman, 2010) and included a clear identification and analysis of the real problem associated with University laboratory courses. Each virtual lab included overall strategic design decision such as structure of the courses, technologies employed and mode of experiment. Each experiment and the lab was considered with instructional design and elements that were evaluated so to motivate the learner experience. Such elements included the choice of graphical front-ends and authoring tools. We had also previously estimated issues related to dissemination for rapid, efficient and cost-effective usability taking into consideration both pedagogical and infrastructural complicacy.

Large scale tests will be needed to analyse and provide the assessment. These tests will also require both learners and educators (lab faculty) to use the software platform. Some tests in biotechnology are already underway via the VALUE initiative (Diwakar et al. 2011).

Several users raised the issue of how to support learners using VL. In real-world labs, learners work in the same place at the same time so there is teacher or peer support available. This kind of support is not immediately available to remote learners.

From our experience, the most vital requirement for each virtual lab is that of technical coordinators and subject matter experts whose inputs improve the lab's knowledge bank and usability. The Virtual lab project is already online for public preview via http://amrita.edu/virtuallabs or the National mission site http://vlab.co.in

#### **15. Acknowledgment**

This project derives direction and ideas from the Chancellor of Amrita University, Sri Mata Amritanandamayi Devi. The authors would also like to acknowledge the contributions of Mr. Raghu Raman, Director, CREATE, Amrita University and CREATE team. This project is funded by NMEICT, MHRD, Government of India.

#### **16. References**

### **Gender, Knowledge, Scientific Expertise, and Attitudes Toward Biotechnology: Technological Salience and the Use of Knowledge to Generate Attitudes**

Richard M. Simon *Department of Sociology, Rice University USA*

#### **1. Introduction**

398 Innovations in Biotechnology

Rohrig C., Jochheim A., The Virtual Lab for controlling real experiments via Internet,

Nedungadi P and Raman R. Effectiveness of Adaptive Learning with Interactive Animations

Engineering and Applications (ICCEA 2010), Bali, Indonesia, March 2010. Wentling T and Park J. Cost Analysis of E-learning: A Case Study of A University Program,

Morgan BM. Is distance learning worth it? Helping to determine the costs of online courses.

Maldarelli GA, Hartmann EM, Cummings PJ, Horner RD, Obom KM, Shingles R and Pearlman RS. Journal of microbiology & biology education, pp. 51-57, May 2009. Romiszowski A. How's the E-learning Baby? Factors Leading to Success or Failure of an

Koch, C. Biophysics of Computation: Information Processing in Single Neurons,Stryker, M.

Wenger E. Artificial Intelligence and Tutoring Systems: Computational and Cognitive approaches to the communication of knowledge. Morgan Kaufman Ed., 1987. Diwakar S, Achuthan K, Nedungadi P and Nair B. Enhanced Facilitation of Biotechnology

Nedungadi P, Raman R, Achuthan K, Diwakar S. Collaborative & Accessibility Platform for

Wangersky P. Lotka-Volterra poulation models, Ann. Rev. Ecol Systems, 1978, 9:189-218.

University of Hartford, NY, USA, April 29-30, 2011.

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Education in Developing Nations via Virtual Labs: Analysis, Implementation and Case-studies, International Journal of Computer Theory and Engineering vol. 3, no.

Distributed Virtual Labs, Proceedings of 2011 IAJC-ASEE International Conference,

Since the advent of recombinant DNA techniques in the early 1970s, biotechnologies have received much attention both for their potential to help and to harm individuals and society. Through the development of gene therapies, stem cell technologies, reproductive technologies and genetically modified crops, biotechnologies have promised to help the sick, the barren, and the poor live more fulfilling lives. At the same time governments, the general public, and scientists themselves have recognized the risks involved with biotechnologies. Since the very beginning of modern biotechnological techniques, scientists have warned that there is "serious concern that some of these artificial recombinant DNA molecules could prove biologically hazardous" (Berg et al., 1974: 303). Risks include the unknown consequences of consuming organisms that have been genetically manipulated, and the release of novel genetic material into wild populations (Torgersen et al., 2002). But aside from the technical risks involved with biotechnologies, concerns have also arisen over the ethical implications of manipulating basic life processes. Critics have rebuked scientists for "taking a technological and reductionist perception of life itself" (Torgersen et al., 2002: 39), and the seemingly imminent development of human cloning and "designer babies" (McGee, 2000; Hughes, 2004) were added to the already controversial issues of embryonic stem cell research and genetically modified organisms. The potential to simultaneously do great help, and great harm, has made biotechnology one of the most important science policy issues of our time.

Often the views of scientists, industry, government, and the public have been at odds. Because of their enormous commercial potential, industry has largely downplayed the risks and emphasized the benefits of biotechnologies (see, for example, Rampton's and Stauber's (1998) exposé of Monsanto's attempts to suppress evidence that one of its products, recombinant bovine growth hormone, causes cancer). National governments, recognizing the importance of biotechnology as a key technology in the post-industrial marketplace, have simultaneously sought to protect industry from an overzealous public, yet appear responsive to public concerns. The general public has been especially critical of biotechnologies, though the level of skepticism varies by region and the particular issue at stake (Allum et al., 2002).

In the case of biotechnology, public concern has turned out to have significant influence. In the arenas of politics and economy, public concerns about biotechnology have translated into real transformations. Bauer and Gaskell observe that,

In contemporary times, public opinion is not merely a perspective "after the fact"; it is a crucial constraint, in the dual sense of the limitations and opportunities for governments and industries to exploit the new technology. Whereas the biotechnology industry assumed that regulatory processes were the sole hurdle prior to commercialisation, it is now apparent that a second hurdle, national and international public opinion, must be taken into account (2002: 1).

Both by voting with their dollars, and by making it difficult for biotech firms to bring products to market (Weber et al., 2009), "public opinion" has affected the development and distribution of biotechnologies (for a more extended discussion of the efficacy of public opinion see Page and Shapiro, 1992). Furthermore, governmental bodies at the national level, as well as the European Union, have heard concerns and adjusted policy accordingly (Torgersen et al., 2002). Clearly, if the future of biotechnology is to be grasped, public opinion, and the processes by which it is formed, must be understood.

One issue that is frequently referred to when considering the public's attitudes is knowledge of biotechnology. Despite initial hesitance, "an increasing number of scientists concluded that the risks had been exaggerated" (Torgersen et al., 2002: 34). Yet public attitudes have become more negative, and more ambivalent, over time (Shanahan et al., 2001). Despite some studies that suggest a link between genetically modified organisms and health problems (e.g., a recent study that found a connection between the consumption of genetically modified corn and organ failure in rats; Vendômois et al., 2009), scientists have largely dismissed public outcry over biotechnologies as reactionary and a consequence of a public that remains uninformed (see, for example, McHughen, 2007). This very basic hypothesis – that people will become more accepting of biotechnology once they better understand it – has received some support in studies of public opinion (Allum et al., 2008; Bak, 2001; Wright & Nerlich, 2006). However, not all people use knowledge in the same way. The *contexts* in which knowledge is called forth have been shown to play a crucial role in how it is used to formulate opinions toward science and technology. Studies that test for interaction effects show that the effect of knowledge on attitudes varies by region (Allum et al., 2002), levels of political knowledge (Sturgis & Allum, 2004), and by gender (von Roten, 2004; Simon, 2010, 2011).

Gender differences in the use of knowledge to formulate attitudes are particularly intriguing because of the persistent gender gap in support for science and technology. Whether asked about environmental issues (Stern et al., 1993; Hayes, 2001; McCright, 2010), nuclear power (Freudenburg & Davidson, 2007; Davidson & Freudenburg, 1996; Krannich & Albrecht, 1995), biotechnologies (Qin & Brown, 2007; Bryant & Pini, 2006; Simon 2010, 2011) including reproductive technologies (Napolitano & Ogunseitan, 1999), or science in general (Trankina, 1993; Barke et al., 1997; von Roten, 2004; Mallow et al., 2010; Breakwell & Robertson 2001; Hayes & Tariq 2000), women express more skepticism toward science and technology than men do. Gender is an important determinant of people's attitudes toward science and

biotechnologies, though the level of skepticism varies by region and the particular issue at

In the case of biotechnology, public concern has turned out to have significant influence. In the arenas of politics and economy, public concerns about biotechnology have translated

Both by voting with their dollars, and by making it difficult for biotech firms to bring products to market (Weber et al., 2009), "public opinion" has affected the development and distribution of biotechnologies (for a more extended discussion of the efficacy of public opinion see Page and Shapiro, 1992). Furthermore, governmental bodies at the national level, as well as the European Union, have heard concerns and adjusted policy accordingly (Torgersen et al., 2002). Clearly, if the future of biotechnology is to be grasped, public

One issue that is frequently referred to when considering the public's attitudes is knowledge of biotechnology. Despite initial hesitance, "an increasing number of scientists concluded that the risks had been exaggerated" (Torgersen et al., 2002: 34). Yet public attitudes have become more negative, and more ambivalent, over time (Shanahan et al., 2001). Despite some studies that suggest a link between genetically modified organisms and health problems (e.g., a recent study that found a connection between the consumption of genetically modified corn and organ failure in rats; Vendômois et al., 2009), scientists have largely dismissed public outcry over biotechnologies as reactionary and a consequence of a public that remains uninformed (see, for example, McHughen, 2007). This very basic hypothesis – that people will become more accepting of biotechnology once they better understand it – has received some support in studies of public opinion (Allum et al., 2008; Bak, 2001; Wright & Nerlich, 2006). However, not all people use knowledge in the same way. The *contexts* in which knowledge is called forth have been shown to play a crucial role in how it is used to formulate opinions toward science and technology. Studies that test for interaction effects show that the effect of knowledge on attitudes varies by region (Allum et al., 2002), levels of political knowledge (Sturgis & Allum, 2004), and by gender (von Roten,

Gender differences in the use of knowledge to formulate attitudes are particularly intriguing because of the persistent gender gap in support for science and technology. Whether asked about environmental issues (Stern et al., 1993; Hayes, 2001; McCright, 2010), nuclear power (Freudenburg & Davidson, 2007; Davidson & Freudenburg, 1996; Krannich & Albrecht, 1995), biotechnologies (Qin & Brown, 2007; Bryant & Pini, 2006; Simon 2010, 2011) including reproductive technologies (Napolitano & Ogunseitan, 1999), or science in general (Trankina, 1993; Barke et al., 1997; von Roten, 2004; Mallow et al., 2010; Breakwell & Robertson 2001; Hayes & Tariq 2000), women express more skepticism toward science and technology than men do. Gender is an important determinant of people's attitudes toward science and

In contemporary times, public opinion is not merely a perspective "after the fact"; it is a crucial constraint, in the dual sense of the limitations and opportunities for governments and industries to exploit the new technology. Whereas the biotechnology industry assumed that regulatory processes were the sole hurdle prior to commercialisation, it is now apparent that a second hurdle, national and international

stake (Allum et al., 2002).

2004; Simon, 2010, 2011).

into real transformations. Bauer and Gaskell observe that,

public opinion, must be taken into account (2002: 1).

opinion, and the processes by which it is formed, must be understood.

technology, and the fact that there is some evidence that indicates that men and women use knowledge differently to form attitudes suggests this is of critical importance for understanding public attitudes toward science and technology.

In a previous study of gender differences in attitudes toward biotechnology (Simon, 2010), I found that the more knowledge that men had on the subject, the less inclined they were to be pessimistic about its effects on society. The effect I found for men clearly supports the knowledge deficit hypothesis: the more men know, the more likely they are to support biotechnology. However, the effect of knowledge on attitudes proved to be the *opposite* for women: women became more *critical* of biotechnology with more knowledge. The results of that study suggest scientific knowledge persuades men to be supportive of biotechnology, but that same knowledge causes women to be critical of it.

What is causing men and women to use knowledge in such radically different ways? One hypothesis is that science and technology, and biotechnologies in particular, have radically different implications for men and women. Because of gender roles associated with childbearing and childrearing, women are more likely to be directly affected by some of the negative consequences of science and technology. As Dorothy Nelkin suggests regarding attitudes toward nuclear power, women are its "most active and outspoken critics" because of "the special effects radiation has on the health of women and on future generations" (1981: 15). This perspective is echoed by ecofeminists such as Mallory (2006) who argue for a parallel between the exploitation of nature characteristic of science and technology and the exploitation of women, and Bryant and Pini who theorize "a relationship between chemicals and women's reproductive bodies" (2006: 268). Biotechnologies may be especially relevant to women because the implications for bodily contact and harm are an integral element of the controversies surrounding biotechnology (DuPuis, 2000). With respect to gender differences in attitudes toward biotechnology, Napolotano and Ogunseitan remark, "many of the applications [of biotechnologies] towards human health issues will likely affect fetuses, mothers, and young children more than adult males and non-childbearing female members of society" (1999: 202).

The implication of these arguments is that biotechnology is more *salient* for women than it is for men. I submit that the gender difference in *technological salience* produces the circumstances that facilitate a unique set of evaluative criteria for women that are not utilized by those with more distance from biotechnology. The hypothesis is that, for women, biotechnology is *personal* in a way that it is not for men, and the more women understand about biotechnology the better they are able to grasp this.

The technological salience explanation of gender differences in attitudes toward science and technology (developed with my colleague Katherine Johnson; Johnson and Simon, n.d.) suggests certain hypotheses. The theory states that the criteria that people will use to form attitudes toward science and technology will be determined by how salient the particular issue is to their own lived experiences. The hypotheses I intend to explore have to do with how men and women use knowledge differently with respect to their views on the public's role in science policy. It is intuitive that when people think the public should decide science policy, their own knowledge of a topic should be an important determinant of their attitude toward it. However, when people think experts should decide science policy, it is unclear how knowledge might relate to attitudes toward biotechnology. If biotechnology is really a personal matter for women, a matter relevant to their bodies, values, and roles in a way it is not for men, then knowledge of it should be absolutely crucial for determining their attitudes under nearly all circumstances. Even when women are willing to abdicate responsibility to scientific experts or government to regulate other forms of science and technology, because biotechnology has an especial salience for women, they should be unlikely to disregard their own knowledge when evaluating it. On the other hand, I predict that men's use of knowledge to generate attitudes will be conditional upon their willingness to let scientific experts and government regulate science and technology. Because biotechnology is not especially salient for men compared to women, they should categorize it alongside other issues in science and technology that are not especially salient for them. When men abdicate responsibility for science policy to experts, their own knowledge of biotechnology should be less important in determining their attitudes compared to men who think science policy should be decided by the public.

In technical terms, I am hypothesizing a three-way interaction of gender, knowledge, and willingness to leave science policy to experts in determining attitudes toward biotechnology. For women, the effect of their knowledge on their attitudes should be the same no matter what their opinions on the role of experts are (Hypothesis 1), because even if they are generally willing to let experts decide science policy, the especial salience of biotechnology for women will make their understanding of it relevant under any circumstances. But because biotechnology is not especially salient for men, when the responsibility for determining a positive or negative attitude is externalized to experts, the importance of one's own knowledge about the topic should be less important because responsibility is transferred to an external source (Hypothesis 2). Because of the particular salience of biotechnology for women, the effect of their knowledge on their attitudes should be unconditional. Because biotechnology is less salient for men, the effect of their knowledge on their attitudes should be more likely to be affected by other circumstances; in particular, it should become less relevant when they believe the public should stay out of scientific affairs. Stated formally:

**H1**: Controlling for other relevant variables, the effect of knowledge on attitudes toward biotechnology will be *unconditional* upon the opinion that experts should decide science policy issues for females (i.e., the knowledge-experts interaction will *not* significantly predict attitudes for females).

**H2**: Controlling for other relevant variables, the effect of knowledge on attitudes toward biotechnology will be weaker when males hold the opinion that experts should decide science policy issues, and stronger when males hold the opinion that the public should decide science policy issues. (i.e., the knowledge-experts interaction *will* significantly predict attitudes for males, and it will be negative).

#### **2. Data and methods**

#### **2.1 Sample**

The Eurobarometer 63.1 is used to test these hypotheses because it features items on attitudes toward biotechnology, the role of experts in scientific decision-making, and a twelve-question quiz designed to measure scientific knowledge. The Eurobarometer 63.1 is a representative sample of thirty-two European nations and includes household respondents aged 15 and over. These nations include 25 European Union member countries: Austria, Belgium, Republic of Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, and the United Kingdom, plus the EU candidate countries of Bulgaria, Croatia, Romania, Turkey, as well as three European Free Trade Association countries: Iceland, Norway, and Switzerland (Papacostas, 2006).

Data for the Eurobarometer 63.1 were collected between the 3rd of January and the 15th of February 2005 by TNS Opinion and Social on behalf of the European Commission. It utilized a probabilistic, stratified sample design in which each nation was stratified first by region, then municipality. In each country, a number of sampling points were drawn with probability proportional to population size and to population density. Finally, households were chosen within each municipality. The survey was administered at the homes of the respondents in face-to-face interviews (Papacostas, 2006).

#### **2.2 Dependent variable**

402 Innovations in Biotechnology

not for men, then knowledge of it should be absolutely crucial for determining their attitudes under nearly all circumstances. Even when women are willing to abdicate responsibility to scientific experts or government to regulate other forms of science and technology, because biotechnology has an especial salience for women, they should be unlikely to disregard their own knowledge when evaluating it. On the other hand, I predict that men's use of knowledge to generate attitudes will be conditional upon their willingness to let scientific experts and government regulate science and technology. Because biotechnology is not especially salient for men compared to women, they should categorize it alongside other issues in science and technology that are not especially salient for them. When men abdicate responsibility for science policy to experts, their own knowledge of biotechnology should be less important in determining their attitudes compared to men

In technical terms, I am hypothesizing a three-way interaction of gender, knowledge, and willingness to leave science policy to experts in determining attitudes toward biotechnology. For women, the effect of their knowledge on their attitudes should be the same no matter what their opinions on the role of experts are (Hypothesis 1), because even if they are generally willing to let experts decide science policy, the especial salience of biotechnology for women will make their understanding of it relevant under any circumstances. But because biotechnology is not especially salient for men, when the responsibility for determining a positive or negative attitude is externalized to experts, the importance of one's own knowledge about the topic should be less important because responsibility is transferred to an external source (Hypothesis 2). Because of the particular salience of biotechnology for women, the effect of their knowledge on their attitudes should be unconditional. Because biotechnology is less salient for men, the effect of their knowledge on their attitudes should be more likely to be affected by other circumstances; in particular, it should become less relevant when they believe the public should stay out of scientific

**H1**: Controlling for other relevant variables, the effect of knowledge on attitudes toward biotechnology will be *unconditional* upon the opinion that experts should decide science policy issues for females (i.e., the knowledge-experts interaction will *not*

**H2**: Controlling for other relevant variables, the effect of knowledge on attitudes toward biotechnology will be weaker when males hold the opinion that experts should decide science policy issues, and stronger when males hold the opinion that the public should decide science policy issues. (i.e., the knowledge-experts interaction *will*

The Eurobarometer 63.1 is used to test these hypotheses because it features items on attitudes toward biotechnology, the role of experts in scientific decision-making, and a twelve-question quiz designed to measure scientific knowledge. The Eurobarometer 63.1 is a representative sample of thirty-two European nations and includes household respondents aged 15 and over. These nations include 25 European Union member countries: Austria, Belgium, Republic of Cyprus, Czech Republic, Denmark, Estonia,

who think science policy should be decided by the public.

significantly predict attitudes for females).

significantly predict attitudes for males, and it will be negative).

affairs. Stated formally:

**2. Data and methods**

**2.1 Sample**

The purpose of this study is to investigate gender differences in attitudes toward biotechnology. Hence, the dependent variable is derived from a question asking respondents whether they think biotechnology "will have a positive, negative, or no effect on our way of life over the next twenty years." This variable was recoded so that all respondents indicating that they think biotechnology will have a negative effect "on our way of life" were coded as 1 and all other respondents were coded as 0. The dependent variable is therefore a measure of *pessimism* about biotechnology.

#### **2.3 Independent variables**

Independent variables of interest are gender, scientific knowledge, and a variable derived from the question, "Which of the two following views is closest to your own? Decisions about science and technology should be based primarily on the advice of experts about risks and benefits involved, or on the general public's views of risks and benefits?" All respondents who indicated that decisions should be left to experts were coded as 1 and all respondents who indicated that the general public should make decisions were coded as 0.

To create the gender variable used in the analysis, all males were coded 1, and all females were coded 0.

The knowledge variable is a scale constructed from a twelve question factual test on scientific knowledge administered with the Eurobarometer. This test asked questions about science in general, not about biotechnology specifically. The items were recoded so that correct responses were equal to 1, and incorrect responses were equal to 0. Each respondent's score on the knowledge variable is equal to the proportion of correct answers they made on the knowledge test (e.g., someone who earned eight correct answers would be coded as .67). The knowledge scale obtained an alpha = .72.

Control variables included variables capturing political attitudes, age, education, and religiosity. The political attitudes variable was derived from responses to the question: "In political matters people talk of 'the left' and 'the right'. How would you place your views on this scale?" Respondents were asked to rate their political attitudes on a scale of 1 to 10, with lower values indicating that they are more "left", and higher values indicating that they are more to the "right". Respondents' age was coded in years. The Eurobarometer's education item does not directly measure respondents' educational attainment; instead respondents are asked to give the age at which they left school. This variable was recoded into dummies that roughly correspond to stages in the educational career: respondents who left school before high school age ("Edu. 0-14"), those of high school age ("Edu. 15-18"), those of college age but who have not reached the typical age of college graduation ("Edu. 19-21"), and those who left school after the age of 22 ("Edu. 22+"), assumedly college graduates. Another dummy was created for respondents who had not yet left school ("still studying"). This education measure is not ideal, but previous work (Simon, 2010, 2011) with the Eurobarometer's education measure has showed that it possesses criterion validity when predicting attitudes toward biotechnology. Respondents' religiosity was measured with an ordinal item asking respondents, "Apart from weddings or funerals, about how often do you attend religious services?", with responses ranging from "never" to "more than once a week". Higher values indicate more frequent attendance of religious services, and lower values indicate less frequent attendance.

#### **2.4 Analytic sample**

The total number of respondents included in the Eurobarometer 63.1 is 31,390. Missing values were addressed using pairwise deletion. After deleting missing values the sample was reduced to 26,621. Because of the complex sampling design, the data were weighted with a weight designed to accommodate analyses that make use of all the nations in the study. Weighting the data further reduced the analytic sample to 24,630. Splitting the sample by gender resulted in an *n* = 12,379 for males, and an *n* = 12,251 for females.

#### **2.5 Analytic approach**

Hypothesis 1 predicts that the experts-knowledge interaction will not be significant in the female model. Hypothesis 2 predicts that the experts-knowledge interaction will be significant and negative in the male model. To test the hypotheses, the sample was split by gender, and separate logistic regression models predicting attitudes toward biotechnology (with "it will have a negative effect on our way of life" coded as "1") were run for males and females, including a test of the "experts-knowledge" interaction in both models.

#### **3. Results**

Table 1 shows means (or proportions) and standard deviations for all variables by gender. A few items are of note. First, while slightly less than 20 percent of males thought biotechnology will have a negative effect "on our way of life over the next twenty years", slightly more than 22 percent of females had the same opinion. While the gender difference in attitudes is slight, it is consistent with previous research that has shown females to be more skeptical of biotechnology than males. Also consistent with previous research, females possess less scientific knowledge than males. (averaging .59 on the knowledge test vs. .66, respectively, a difference of nearly one entire correct answer). Approximately three-quarters of both males and females believe that experts, not the general public, should decide science policy issues.

Males: *n* = 12,379

404 Innovations in Biotechnology

lower values indicating that they are more "left", and higher values indicating that they are more to the "right". Respondents' age was coded in years. The Eurobarometer's education item does not directly measure respondents' educational attainment; instead respondents are asked to give the age at which they left school. This variable was recoded into dummies that roughly correspond to stages in the educational career: respondents who left school before high school age ("Edu. 0-14"), those of high school age ("Edu. 15-18"), those of college age but who have not reached the typical age of college graduation ("Edu. 19-21"), and those who left school after the age of 22 ("Edu. 22+"), assumedly college graduates. Another dummy was created for respondents who had not yet left school ("still studying"). This education measure is not ideal, but previous work (Simon, 2010, 2011) with the Eurobarometer's education measure has showed that it possesses criterion validity when predicting attitudes toward biotechnology. Respondents' religiosity was measured with an ordinal item asking respondents, "Apart from weddings or funerals, about how often do you attend religious services?", with responses ranging from "never" to "more than once a week". Higher values indicate more frequent attendance of religious services, and lower

The total number of respondents included in the Eurobarometer 63.1 is 31,390. Missing values were addressed using pairwise deletion. After deleting missing values the sample was reduced to 26,621. Because of the complex sampling design, the data were weighted with a weight designed to accommodate analyses that make use of all the nations in the study. Weighting the data further reduced the analytic sample to 24,630. Splitting the

Hypothesis 1 predicts that the experts-knowledge interaction will not be significant in the female model. Hypothesis 2 predicts that the experts-knowledge interaction will be significant and negative in the male model. To test the hypotheses, the sample was split by gender, and separate logistic regression models predicting attitudes toward biotechnology (with "it will have a negative effect on our way of life" coded as "1") were run for males and

Table 1 shows means (or proportions) and standard deviations for all variables by gender. A few items are of note. First, while slightly less than 20 percent of males thought biotechnology will have a negative effect "on our way of life over the next twenty years", slightly more than 22 percent of females had the same opinion. While the gender difference in attitudes is slight, it is consistent with previous research that has shown females to be more skeptical of biotechnology than males. Also consistent with previous research, females possess less scientific knowledge than males. (averaging .59 on the knowledge test vs. .66, respectively, a difference of nearly one entire correct answer). Approximately three-quarters of both males and females believe that experts, not the

sample by gender resulted in an *n* = 12,379 for males, and an *n* = 12,251 for females.

females, including a test of the "experts-knowledge" interaction in both models.

general public, should decide science policy issues.

values indicate less frequent attendance.

**2.4 Analytic sample**

**2.5 Analytic approach**

**3. Results**

Females: *n* = 12,251

Table 1. Mean or proportion and standard deviation by gender.

Table 2 features results of a series of logistic regressions predicting attitude toward biotechnology for females only. Odds ratios are shown. Model 1 features only the knowledge scale as an independent variable; it is positive and significant. Without controlling for any other variables, each additional correct answer on the knowledge test increases the likelihood of being pessimistic about biotechnology by about 33 percent. Model 2 adds the experts variable as a predictor; it is negative and significant. Controlling for level of scientific knowledge, feeling that experts and not the public should be responsible for science policy decreases the odds of being pessimistic toward biotechnology by about 31 percent. Model 3 retains the knowledge and experts variables as predictors, but adds the knowledge-experts interaction term. Recall that Hypothesis 1 predicted that, for females, the effect of knowledge on attitudes toward biotechnology would be about the same regardless of the respondent's opinion about the role experts should play in science policy issues. Model 3 reveals that the effect of knowledge on attitude is not affected by opinion about the role of experts in science policy. Model 4 shows that this is true even when controlling for political attitude, age, religiosity, and education, confirming Hypothesis 1.

Table 3 shows logistic regression results for the male sample only. Model 1 uses only the knowledge scale to predict pessimism toward biotechnology. Without accounting for any other variables, each additional correct answer on the knowledge test results a reduction in the odds of being pessimistic about biotechnology of about 20 percent for males. However, adding the experts variable reduces the effect of knowledge to insignificance. Like their female counterparts, when males leave policy decisions up to experts, they are much less likely to oppose biotechnology. Model 3 adds the interaction term. The effect is strong, negative, and highly significant. The correlation between knowledge and attitude is much weaker for males who think science policy should be decided by experts compared to males who think it should be decided by the public. Model 4 includes the control variables. The experts-knowledge interaction effect on attitude is slightly reduced when controlling for political ideology, age, religiosity, and education, though it is still strong, negative, and significant, confirming Hypothesis 2. This is a much different scenario than for females, for which the experts-knowledge interaction did not predict biotechnology attitude.

To further illustrate how opinion on the role of experts in science policy tempers the effect of knowledge on biotechnology attitude for males but not for females, each male and female sample was further split by whether the respondent thought science policy decisions should be made by experts or by the public. Table 4 shows results of logistic regressions predicting biotechnology attitude by gender and science policy opinion. Model 1 displays the coefficients only for females who believe that science policy decisions should be left to experts. With all of the control variables in the equation, each additional correct answer on the knowledge test results in a 53 percent increase in the odds of being pessimistic about biotechnology. Model 2 shows results only for females who think the public should decide science policy issues. For these women, greater knowledge also leads greater odds of rejecting biotechnology. Model 3 features the same regression model, but this time only for males who put their faith in experts. Controlling for political views, age, religiosity, and education, the effect of knowledge on attitude is strong and negative: with each additional correct answer on the knowledge test, the odds of being pessimistic about biotechnology are reduced by about 47 percent.

\*sig. at p.<.05

\*\*sig. at p.<.01

\*\*\*sig. at p.<.001

However, the effect of knowledge on attitude is drastically different for males who think science policy should be the domain of the public. For these males, the effect of knowledge is strong and *positive*, nearly doubling the odds of being pessimistic about biotechnology with each additional correct answer on the knowledge test. For females, more knowledge leads to a greater likelihood of rejecting biotechnology no matter who they think should be making science policy decisions, and in both cases the effect of knowledge on attitude is about the same. For males, the effect of knowledge on attitude cannot be predicted unless the respondent's opinion on science policy decisions is known.

\*sig. at p.<.05 \*\*sig. at p.<.01

\*\*\*sig. at p.<.001

Table 3. Logistic regressions predicting whether or not biotechnology will make things worse, male sample only

#### **4. Discussion**

406 Innovations in Biotechnology

is much weaker for males who think science policy should be decided by experts compared to males who think it should be decided by the public. Model 4 includes the control variables. The experts-knowledge interaction effect on attitude is slightly reduced when controlling for political ideology, age, religiosity, and education, though it is still strong, negative, and significant, confirming Hypothesis 2. This is a much different scenario than for females, for which the experts-knowledge interaction did not predict

To further illustrate how opinion on the role of experts in science policy tempers the effect of knowledge on biotechnology attitude for males but not for females, each male and female sample was further split by whether the respondent thought science policy decisions should be made by experts or by the public. Table 4 shows results of logistic regressions predicting biotechnology attitude by gender and science policy opinion. Model 1 displays the coefficients only for females who believe that science policy decisions should be left to experts. With all of the control variables in the equation, each additional correct answer on the knowledge test results in a 53 percent increase in the odds of being pessimistic about biotechnology. Model 2 shows results only for females who think the public should decide science policy issues. For these women, greater knowledge also leads greater odds of rejecting biotechnology. Model 3 features the same regression model, but this time only for males who put their faith in experts. Controlling for political views, age, religiosity, and education, the effect of knowledge on attitude is strong and negative: with each additional correct answer on the knowledge test, the odds of being pessimistic about biotechnology are

> Knowledge 1.325\*\* 1.539\*\*\* 1.354 1.421\* Experts .686\*\*\* .609\*\* .698 KnowXExperts 1.208 1.029 Politics .964\*\* Age 1.003 Religiosity 1.017 Edu 0-14 .956 Edu 15-18 1.134 Edu 19-21 1.068 Edu Still Studying .753\*

Table 2. Logistic regressions predicting whether or not biotechnology will make things

Model 1 Model 2 Model 3 Model 4

biotechnology attitude.

reduced by about 47 percent.

(Edu 22+ ref cat)

\*sig. at p.<.05 \*\*sig. at p.<.01 \*\*\*sig. at p.<.001

worse, female sample only.

This paper made two predictions about how males and females use knowledge differently to generate attitudes toward biotechnology based on the idea of *technological salience*. It was predicted that because biotechnologies are more salient to females compared to males, the effect of their knowledge on their attitudes should hold no matter what their opinions on the role of experts are, because even if they are generally willing to let experts decide science policy, the especial salience of biotechnology for women will make their understanding of it relevant under a more robust set of circumstances.

But because biotechnology is not especially salient for men, when the responsibility for determining a positive or negative attitude is externalized to experts, the importance of one's own knowledge about the topic should be less important because responsibility is transferred to an external source. Because of the particular salience of biotechnology for women, the effect of their knowledge on their attitudes should be unconditional. Because biotechnology is less salient for men, the effect of their knowledge on their attitudes should be more likely to be affected by other circumstances; in particular, it should become less relevant when they believe the public should stay out of scientific affairs. Hypothesis 1 predicted that the experts-knowledge interaction would not significantly predict biotechnology attitude for females; that is, the effect of knowledge on attitude should be the same regardless of their opinion about who should make science policy decisions. Table 2 showed that, indeed, the experts-knowledge interaction was not significant for females. Hypothesis 2 predicted that the effect of knowledge on attitudes toward biotechnology should be weaker when males hold the opinion that experts should decide science policy issues, and stronger when males hold the opinion that the public should decide science policy issues; that is, the knowledge-experts interaction should be *negative* and significant for males. The evidence in Table 3 supports this hypothesis. When males do not trust the general public to make good policy decisions, their own knowledge is less important in determining their attitude toward biotechnology. Females, however, seem equally insistent upon using the knowledge they have under any circumstances.

\*sig. at p.<.05

(Edu 22+ ref cat)

\*\*sig. at p.<.01

\*\*\*sig. at p.<.001

Table 4. Logistic regressions predicting whether or not biotechnology will make things worse, by gender and opinion on expertise.

But because biotechnology is not especially salient for men, when the responsibility for determining a positive or negative attitude is externalized to experts, the importance of one's own knowledge about the topic should be less important because responsibility is transferred to an external source. Because of the particular salience of biotechnology for women, the effect of their knowledge on their attitudes should be unconditional. Because biotechnology is less salient for men, the effect of their knowledge on their attitudes should be more likely to be affected by other circumstances; in particular, it should become less relevant when they believe the public should stay out of scientific affairs. Hypothesis 1 predicted that the experts-knowledge interaction would not significantly predict biotechnology attitude for females; that is, the effect of knowledge on attitude should be the same regardless of their opinion about who should make science policy decisions. Table 2 showed that, indeed, the experts-knowledge interaction was not significant for females. Hypothesis 2 predicted that the effect of knowledge on attitudes toward biotechnology should be weaker when males hold the opinion that experts should decide science policy issues, and stronger when males hold the opinion that the public should decide science policy issues; that is, the knowledge-experts interaction should be *negative* and significant for males. The evidence in Table 3 supports this hypothesis. When males do not trust the general public to make good policy decisions, their own knowledge is less important in determining their attitude toward biotechnology. Females, however, seem equally insistent

> Females Males Experts Public Experts Public

Knowledge 1.563\* 1.171\* .526\*\*\* 1.925\* Politics .967\* .961 .952\*\*\* .950\* Age 1.000 1.012\*\*\* .998 .996 Religiosity 1.027 .988 .981 1.021 Edu 0-14 1.091 .702 1.290\* 1.590\* Edu 15-18 1.155 1.071 1.178 1.166 Edu 19-21 1.105 1.000 1.343\*\* 1.257 Edu Still Studying .606\*\*\* 1.314 1.477\*\* .784

Table 4. Logistic regressions predicting whether or not biotechnology will make things

upon using the knowledge they have under any circumstances.

(Edu 22+ ref cat)

\*sig. at p.<.05 \*\*sig. at p.<.01 \*\*\*sig. at p.<.001

worse, by gender and opinion on expertise.

Table 4 lends further support to the theory of technological salience, and complicates the way we understand how men's and women's knowledge differently affects their attitudes toward biotechnology. Consistent with previous research on how males and females use knowledge to generate attitudes (Simon, 2010), this study found that women become more likely to reject biotechnology with greater knowledge of it, and this relationship holds regardless of their opinion about who should make decisions about science in society. It is intuitive that when people think science should be controlled by the public, their own knowledge of a topic should be an important determinant of their attitude toward it. However, for women, even when they believe that science is best run by the experts, their knowledge is just as important for determining their attitude toward biotechnology, and in both cases they become more skeptical of it the more they understand. Table 4 also shows the dramatic difference in the way males who trust experts use knowledge compared to males who believe science policy decisions should be a public affair. Though Hypothesis 2 – that knowledge would be less important in determining attitude for males who abdicate to experts compared to males who think the public should be responsible for science policy decisions – was supported in by the results displayed in Table 3, Table 4 revealed that knowledge still significantly predicted attitude for males who defer to experts. While the effect of knowledge on attitude is stronger for males who think science policy should be decided by the public, Table 4 shows another telling gender difference. Males who trust experts become *less* likely to reject biotechnology with increasing levels of knowledge; males who insist that science should be controlled by the public become *more* likely to reject biotechnology with increasing levels of knowledge.

I propose that the explanation that best accommodates these findings is the differential salience of biotechnologies for women compared to men. Previous research has suggested that because of gender roles associated with childbearing and childrearing, women are more likely to be directly affected by some of the negative consequences of biotechnologies (Napolitano & Ogunseitan 1999; Bryant & Pini 2006). The more women understand biotechnology, the more they see the personal implications, and the more skeptical they become. When a technology is particularly salient to one's lived experiences, it is likely to be viewed from a much more personal perspective, one in which opinions about the role of experts in policy decisions become of secondary importance compared to how the technology is likely to affect one's own life.

Because biotechnologies are not as salient to males, they use knowledge to generate their attitudes in a different way. This study has suggested that when males award experts responsibility for making important decisions about science in society, the way they use knowledge largely conforms to the deficit model (Allum et al., 2008; Bak, 2001; Wright & Nerlich, 2006): the more they know, the more supportive they become. This seems to typify the kind of male frequently described in the literature on gender and science, one that has much invested in hegemony of scientific institutions, and the one described so eloquently by Hayes:

Because men have historically commanded the technoscientific components of society, they have not only acquired the necessary scientific and technological knowledge to dominate nature but have also been socialised to unecological attitudes toward the environment. Denied access to the technoscientific realm, not only have women been traditionally prevented from acquiring this knowledge but they have also been socialised to the more ecologically benign roles of mother and nurturer as reflected in their reproductive and greater child-rearing responsibilities within society (2001: 658).

However, not all males fit this stereotype. Although only about 27 percent of males in this study thought that science policy should be decided by the public, those who did became more critical of biotechnology with increasing knowledge of it, an effect of knowledge typical of females.

This study has several implications for the future of biotechnology and the study of its relations with society. If public opinion has been as instrumental in the development of biotechnologies as Bauer and Glaskel (2002) insist that it has been, then it seems that gendered experiences of biotechnologies have been instrumental in equal measure. In Bauer and Glaskel's edited volume on public perceptions of biotechnology – the most comprehensive and influential single text on this subject to date – gender is hardly mentioned at all, and when it is it is utilized as a control variable, not a focus of sustained analysis. If the future of biotechnology depends on its public reception, then it is also dependent upon gender roles and experiences, and how they relate to biotechnologies.

What do these results tell us about the relationship between understanding biotechnology and its acceptance? As Priest remarks, "It remains tempting for the scientific community and those who speak in public on its behalf to assume that dissent generally represents ignorance, and that it therefore can be reduced or eliminated by education" (2001: 98). But charges by scientific experts that resistance to biotechnologies is a consequence of an illinformed public are only half true. It seems that, rather than providing the general public with accurate information, advocates of biotechnologies would have an interest in keeping women ignorant. Proponents of the "public understanding of science" have largely missed the point when it comes to women because they tend to see science and technology in terms of an objective cost-benefit analysis (e.g., Wertz et al., 1986) rather than as objects of lived experience. Embryonic stem cell techniques may sound innocuous to men, who may view the experimental material simply as a collection of cells. But when you have had a fetus in your own body, or even simply have the potential to carry a child, then embryonic stem cell techniques become more about powerful social institutions doing things to your body, and less about the greater good for humanity.

While this study is suggestive, it is also limited in some respects. The argument rests on claims about women's knowledge of biotechnology, though the knowledge variable used in the analysis measures general scientific knowledge, not knowledge about biotechnology specifically. It is assumed that general scientific knowledge can be taken as a faithful proxy for knowledge of biotechnology. There are surely arguments that could be summoned to challenge this assumption. However, it should be noted that the gender-knowledge interaction found here is similar to that found in a previous study (Simon, 2010) that made use of a knowledge test focused exclusively on genetics and biotechnologies. Another limitation is that the salience of biotechnology to respondents was not directly tested, but merely inferred from previous research on gender and technology and the gender differences in attitude formation observed from analyses of the Eurobarometer 63.1. I have elsewhere begun to perform more direct tests of technological salience with Katherine Johnson (Johnson and Simon, n.d.), but more research needs to be done to rule out alternative hypotheses. Perhaps there is some other gender division that is causing men and women to use knowledge in such disparate ways. The technological salience hypothesis is consistent with the findings presented here, but more direct tests need to be performed before it can be accepted with confidence.

#### **5. Conclusions**

410 Innovations in Biotechnology

However, not all males fit this stereotype. Although only about 27 percent of males in this study thought that science policy should be decided by the public, those who did became more critical of biotechnology with increasing knowledge of it, an effect of knowledge