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Learning Life Sciences: Design and Development of a Virtual Molecular Biology Learning Lab

Joerg Zumbach1

Stefanie Schmitt1

Peter Reimann2

Philipp Starkloff1

1Institute of Psychology – University of Heidelberg

Hauptstrasse 47-51 – 69117 Heidelberg/Germany

2Computer-supported Learning and Cognition (CoCo)

Unit Education Building A35 – University of Sydney NSW 2006/Australia

Email: zumbach@uni-hd.de stefanie.schmitt@psychologie.uni-heidelberg.de

p.reimann@edfac.usyd.edu.au starkloff@uni-hd.de

Abstract

The consequence of some developments in the life sciences, for example genetics,

have made them amongst the most controversial in today’s society. Despite the discus-

sion about pro and contra arguments, the demand for life science expertise increases

creating a growing need for life science education. In order to develop expertise in the

complex domain of life sciences, learners need better access to science education.

In this paper, an approach to science education is suggested that combines guided

knowledge acquisition with hands-on experience. Although the need for authentic scien-

tific learning is advocated by researchers, teachers, politicians and institutions, daily

school practice, characterized by financial and organizational constraints, prevents ho-

listic learning experiences. Technology partly helps to overcome such obstacles. In this

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paper a learning environment is discussed that was developed to provide a virtual mo-

lecular biology laboratory and allows for authentic hands-on experience. Through this,

students are introduced to concepts, skills and methods of the scientific domain with di-

dactic support such as scaffolding, guided participation and the chance to explore ex-

periments. Furthermore, by providing communication channels to exchange their under-

standing, learners are enabled to enter scientific discourse and build communities of

scientific practice.

Learning Life Sciences - Pedagogical Considerations

Teaching students basic scientific literacy is one of the primary goals of contemporary

high school and college education in the life sciences, the fields of biology, chemistry

and physics (e.g. American Association for the Advancement of Science, AAAS, 1993).

Scientific literacy includes cognitive as well as meta-cognitive knowledge and the ability

to apply this knowledge in a scientific context. To be scientifically literate also means be-

ing able to collect information and data about specific concepts, schemata or domains.

Scientific literacy requires the ability to recognize and develop scientific questions, then

to draw conclusions from theoretical considerations and empirical findings (Prenzel,

Carstensen, Rost & Senkbeil, 2002). In addition, scientific literacy demands interper-

sonal skills and capabilities to enable participation in scientific discourse. Thus, scientific

literacy develops best within an authentic science learning environment that resembles

a scientific community.

Research practice in life sciences is mainly based on conducting series of experiments.

Accordingly, this is also an important part of science education. Unfortunately, students

rarely have sufficient access to appropriate science facilities where they may practice

experimental research. There are many reasons for the lack of experimental research

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as an instructional method in science education including safety issues, the high costs

of running a laboratory and time-consuming experiments that do not fit into school

schedules.

Scientific literacy is difficult to achieve in a traditional instruction-based, teacher-

centered environment, where students often take a passive learning role and acquire

de-contextualized knowledge. The resulting abstraction and generalization can lead to

learners’ understanding becoming less context dependent, but more general and un-

specific (Lave, 1996). This does not necessarily imply that learning in traditional educa-

tion is ineffective; nevertheless out-of-context learning often leads to students being un-

able to apply their knowledge (Edelson, 1998). While some common teaching ap-

proaches still conceive learning and doing as separate (Lave, 1990), advocates of the

situated learning approach point out that learning and doing are inseparable. Through-

out their development and adulthood people continually adopt the beliefs and behaviors

of the social groups with which they interact. Where individuals are given the chance to

observe members and practices of a culture in situ, they pick up relevant jargon and imi-

tate behavior, even though the observed cultural practices are often abstract and ex-

tremely complex (e.g. Lave & Wenger, 1992). In life science education in particular, the

need for situated learning has been advocated (Linn & His, 2000) suggesting that stu-

dents acquire scientific concepts best through engaging in the practices of ‘real scien-

tists’. It is also expected that authentic activities will engage learners and motivate them.

Motivation is believed to be a precondition for learning success because it positively in-

fluences the willingness to learn (Zumbach, Starkloff & Schmitt, 2004).

According to McGinn and Roth (1999) scientific literacy is a grounding for competent

participation in scientific laboratories and other locations or communities where science

is created and used. Learners acquiring knowledge in a meaningful context develop

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styles of inquiry and communication that enable them to develop life-long learning skills

(Edelson, 1998; Hannafin, Land & Oliver, 1999).

Rationale for an interactive life sciences laboratory

So far it has been argued that development of scientific literacy is embodied in a set of

activities and skills that are part of scientific practice. Scientific practice can be mainly

characterized by identifying and rethinking theoretical issues, hands-on experimental

hypotheses testing, integration of empirical data into existing theories and communica-

tion with other researchers in a community of practice. It is understood that in-depth sci-

ence teaching is largely dependent on schools’ abilities to provide an adequate scientific

environment. Given that these standards are not being realized at anything like the re-

quired scale, virtual science labs might be a viable alternative to real learning laborato-

ries.

lifelab® – learning in the digital lab

Based on these considerations the lifelab® project (http://www.lifelab.de), led by the

German Cancer Research Center (DKFZ) and co-founded by the German Federal Min-

istry of Education and Research (BMBF) commenced in January 2002. The project team

comprising expertise in IT, psychology, pedagogy and computer-based learning was es-

tablished to develop a learning environment which should enable high school students

and laboratory apprentices to get a deeper and more authentic understanding of life sci-

ences.

It was considered crucial to introduce naïve and/or novice learners to the life sciences

by allowing a high degree of self-directed learning in combination with instruction ele-

ments like coaching and scaffolding. Elements of several contemporary instructional de-

sign theories were incorporated into the design including Open Learning Environments

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(Hannafin, Land & Oliver, 1999), Constructivist Learning Environments (Jonassen,

1999) and Learning Communities in Classroom (Bielaczyc & Collins, 1999). The ele-

ments of these learning theories may be recognized in the different levels of expertise

development shown in table 1.

Level of Expertise: Objective Instructional Component Method I: Be able to activate prior knowledge, understand basic concepts of new domain

Enabling Concepts Motivate Learners Generate or activate needs

Analogies Authentic scenarios Challenging missions

II: Be able to acquire domain specific declarative/procedural knowledge

Static Resources Processing/Search Tools Conceptual/Procedural Scaf-folding

Guided experiments Tutorials Information kiosk

III: Be able to explore and to experiment

Dynamic Resources Collecting/Organizing Tools Meta-cognitive/Strategic Scaf-folding

Own Experiments Background information Planning & reflecting

IV: Be able to exchange re-search questions and findings

Design Tools Exchange Scaffolds

Report Research Establish Community of Practice Collaborative Planning & Reflect-ing

Table 1: Components and methods of the lifelab® approach.

Level I: Activation of prior knowledge and introduction to basic concepts of the new domain

To introduce learners to a new domain, learning materials are needed that address a

learner’s prior knowledge. This can be realized through the presentation of explicitly

situated problem statements or questions, through scenarios, missions, cases or analo-

gies. Introductory materials have to orient the individual to a need or problem and guide

them to recognize or generate problems to be addressed and to frame their own learn-

ing needs (Hannafin, 1999; Hannafin, Land & Oliver, 1999). In making science accessi-

ble to learners, their learning experiences have to be connected to their existing science

concepts. The presentation of (authentic) problems and cases that individuals can relate

to brings science to life and motivate learners to carry out lifelong investigations (Linn &

His, 2000). To facilitate investigative exploratory learning, not only are challenging tasks

necessary, but also, learners need information resources to enable their construction of

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conceptual models, to formulate hypothesis, to understand (hidden) processes during

experiments and to communicate results; in short to perform scientific inquiry.

Level II: Guided knowledge acquisition

Inquiry-based approaches to science education emphasize the process of inquiry, such

as generating research hypothesis, collecting and interpreting data, constructing expla-

nations and arriving at conclusions (Sandoval, 2003; Reiser, 2002). To adapt scientific

practice to a classroom environment, in addition to learner-appropriate resources, tools

and scaffolds, a supportive underlying school curriculum and teacher preparation is

needed (Edelson1998). For novice learners guided experiments are often indispensa-

ble. Without guidance and support the iterative process of planning, performing and

documenting an experiment process would be out of reach for most. To overcome basic

problems in experimenting and domain-specific problem solving, as well as reflecting on

recent learning experiences, special tools and scaffolds should be provided for the

learners. Tools such as keyword searches, topic maps, or semantic search engines,

support detection and selection of relevant information helping learners locate and filter

relevant resources and information. Conceptual scaffolds facilitate learners to focus by

recommending the use of certain tools, by providing explicit hints and prompts or by of-

fering topic maps and content trees. How to use available resources and tools is embod-

ied in procedural scaffolds (Hannafin, Land & Oliver, 1999).

Level III: Exploration

In addition to theoretical knowledge acquisition, hands-on experience is the core of in-

quiry-based learning. Free experimenting is essential in order to understand, replicate

and transfer acquired knowledge and skills and finally, to develop a scientific literacy. In

this exploratory phase, learners need resources that are changing dynamically. Using

collecting tools, learners can accumulate resources for their own needs and objectives.

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Complex relationships between ideas, or interdependencies of experimental elements,

can be understood using tools to organize and present resources. Exploration requires

two types of scaffolding: Meta-cognitive scaffolding provides guidance in how to think

during learning and supports the underlying processes of individual learning manage-

ment. Strategic scaffolds help to make students aware of different approaches and

techniques for problem solving by supporting analysis, planning, strategy and tactical

decisions (Hannafin, Land & Oliver, 1999; Brush & Saye, 2001).

Level IV: Exchange

Science is more than investigation. Science requires constant discourse. Within science

laboratories, discourse is central to scientific understanding, sense-making and negotia-

tion within the research group (McGinn & Roth, 1999). To establish a community of sci-

ence practice in a classroom, learners need different kinds of social support. With the

aid of design tools, learners are able to create graphs, text, presentations or complete

entire research reports. Synchronous and asynchronous communication tools provide

learners with the means to convey discourse, share ideas, review work, ask questions

and discuss work and results. Furthermore the establishment of collaborative planning

and reflection becomes possible. The collaborative construction of knowledge and do-

main-specific skills are supported through enabling learners to exchange knowledge.

To provide such an integrative and holistic approach to science teaching and to enable

learners to become members in a community of practice in the domain of molecular bi-

ology the computer-based learning environment lifelab® was developed.

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Implementation of design principles in the lifelab® environment

The main objective of the lifelab® learning environment is to make authentic life science

research accessible to students and to support students (and teachers) in scientific rea-

soning and discourse and, thus, to develop expertise in scientific practices. By means of

adapting authentic activities in molecular biology, students become active participants in

the process of scientific reasoning. The core of the virtual learning environment is an in-

teractive 3D-laboratory shown in screenshot 1. In addressing the objectives described in

Level I (table 1), learners are presented with challenging authentic problems that inte-

grate molecular biology in questions that combine common knowledge with basic sci-

ence concepts, e.g. “How can bananas be used to produce sera?”

Figure 1: Virtual laboratory

To accomplish these challenging missions, students receive different kinds of support

(e.g. introductory animations) depending on their prior knowledge. Different difficulty

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levels allow students to participate in guided experiments, to receive remedial lessons

between the steps of an experiment or to experiment on their own (see Levels II and III

in table 1). While experimenting, several opportunities are available to undertake sub-

tasks of the experiments (e.g. to make predictions, to control or manipulate, or to recog-

nize and to communicate dependencies, coherences etc.). The generic task in each

mode involves the compilation of a thorough and informative report about the accom-

plished experiment. To support students’ learning, several instructional tools and scaf-

folds and information resources were developed. These help to provide background in-

formation, domain-specific knowledge and skills. In addition, there are extracts of ency-

clopedia articles and video clips of real laboratory work (e.g. how to use a pipette) as

well as 3D-animations illustrating hidden biochemical processes. In addition, a virtual

assistant, Dr. Drop is available to guide learners through the experiment.

Specific tools are available to scaffold and provoke planning and reflection, as well as

document and present learners’ experimental progress. To enhance the acquisition of

meta-cognitive skills like planning, monitoring and evaluating an experiment in the virtual

laboratory, students can plan an experiment with a “planning tool”. The planning tool

consists of different pages that label the partial stages of the experiment and have to be

arranged in correct order. During the course of the experiment this plan should be com-

pared to the real experiment and, if necessary, revised. Another tool, the “laboratory re-

port” illustrated in screenshot 2. This report automatically records information about the

stages of each experiment including parameters such as time, abundance and tempera-

ture, information about visible and invisible processes and the results of the tasks. Any

optional remedial sessions visited are also shown. Like a real laboratory report, the tool

allows learners to make annotations. Furthermore, it is possible to add external as well

as internal hyperlinks to any material available in the learning environment.

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Figure 2: Laboratory report

In addition to these basic functions, the laboratory report tool can be used to prepare a

presentation that allows learners to demonstrate their results to peers and teachers. The

laboratory journal can also be used to communicate and exchange research findings,

problems and further research plans over the internet (see table 1, Level IV). The life-

lab® learning environment provides asynchronous and synchronous communication

tools where research reports can easily be shared, annotated and improved. This en-

ables the building of peer groups overcoming traditional classroom borders and enables

special interest groups to build communities of practice.

Cognitive effects of learning in the digital lab

In an evaluation study, 43 high school students (age 17-20) used the lifelab® learning

environment. Some used the program during biology classes in their schools (external

group), others participated in an evaluation session at the University of Heidelberg (in-

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ternal group). Before starting the virtual experiments, the students completed a pre-test

questionnaire which included questions on socio-demographic data, prior knowledge in

genetics, experiences with biology courses (e.g. grades, time and effort invested) and

the student’s motivation.

After obtaining initial instruction in the lifelab® environment, the students conducted the

experimental unit “Vaccines in Plants” where they had to follow experimental steps of

inserting an external gene, transformation, isolation and restriction analysis. At the end

of the experiment, a post-test was applied that tested students’ knowledge in genetics

and their current motivation.

Results

The data reveals that the sample of students performed quite well in biology with grades

of an average of 2.2 (SD = 0.79; 1=first, 6=failed), possibly due to the fact that participa-

tion was voluntary and explicitly targeted at fairly experienced students. The score for

the pre-knowledge assessment was on average 0,6 (SD = 0.14) on a scale of 0 to a

maximum of 1.

Because there was no significant difference between the results of the external and the

internal group, the group membership of the students was disregarded. Comparisons

were made between the results of students with high previous knowledge with those

with low previous knowledge. To distinguish the prior knowledge of the students in the

knowledge pre-test, a median split was carried out (ANOVA: F (1,39) = 104.78; p< .01,

eta² = 0.73).

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0.61

0.51

0.81

0.71

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

pretest posttest

low prior knowledge high prior knowledge

Figure 3: Cognitive effects (knowledge test)

As can be seen in figure 3, the post-test revealed a score of 0.7 (SD = 0.13) on a scale

from 0 to 1. This result represents a significant knowledge increase (learning effect) by

comparison with the pre-test (ANOVA: F (1,39) = 70.73; p<.01; eta² = 0.65). Even

though there is a significant difference between the two groups (ANOVA: F (1, 39) =

51.61; p< .01, eta² = 0.57), the knowledge increase is comparable.

Category Mean SD N Score Pretest low prior knowledge 0.51 0.05 21

high prior knowledge 0.71 0.07 20

Total 0.61 0.12 41

Score Posttest low prior knowledge 0.61 0.10 21

high prior knowledge 0.81 0.07 20

Total 0.71 0.13 41

Table 2: Previous knowledge and cognitive effects

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As previously stated, motivation may be seen as a precondition for learning success be-

cause of its positive influences related to willingness to learn (Zumbach, Starkloff &

Schmitt, 2004). To quantify current motivation in the results, the German version of the

Questionnaire on Current Motivation (QCM; Rheinberg, Vollmeyer & Burns, 2001) was

used to assess motivational factors of anxiety, probability of success, interest, and chal-

lenge on a seven point scale. Of the four QCM factors, only Interest correlated with per-

formance of the knowledge post-test (r = .55, p<.01).

QCM scale

Challenge Interest Probability of success

Anxiety

M

4.74 4.47 4.81 3.12

SD

1.21 1.32 1.17 1.47

N

43 43 43 43

Table 3: QCM factors (motivation)

Students with low previous knowledge showed a significantly lower interest in biological

education (ANOVA: F (1, 39) = 14.34, p<.01, eta² = 0.27).

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5.16

3.78

0

1

2

3

4

5

6

low prior knowledge high prior knowledge

Inte

rest

in b

iosc

ienc

e

Figure 4: Prior Knowledge and Interest

To ascertain if a student’s interest in bioscience is linked to their performance in the

knowledge test, the sample was split in two groups: students with high - and students

with low interest in bioscience. The groups differ significantly at both measuring times

(ANOVA: F (1,38) = 60.10, p< .01, eta²= 0.61). Good performance in the knowledge test

is associated with high interest in biology.

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0.64

0.56

0.78

0.67

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

pretest posttest

low interest high interest

Figure 5: Cognitive Effects and Interest

Category Mean SD N Score Pretest low interest 0.56 0.10 21

high interest 0.67 0.11 19

Total 0.61 0.11 40

Score Posttest low interest 0.64 0.12 21

high interest

0.78 0.10 19

Total 0.70 0.13 40

Table 4: Cognitive effects and interest

Gender differences were not observed neither in terms of motivation nor performance

assessment.

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Summary and discussion

The development of scientific literacy is a complex process that requires expertise de-

velopment over several years. To introduce naïve and novice learners to scientific rea-

soning in a meaningful way, hands-on experience seems to be a necessary element.

Unfortunately, prevalent conditions in schools allow only minimal scientific inquiry and

experimenting in the life sciences. The lack of equipment, time and, sometimes, teach-

ing skills diminish the chance of active enrolment e.g. in molecular biology.

The lifelab® learning environment aspires to overcome these impediments by providing

access to authentic science problems in a virtual laboratory. To meet different learners’

needs and to foster life science expertise through different stages, a four level approach

for teaching scientific literacy is proposed. Starting with enabling contexts, the activation

of prior knowledge, followed by guided participation and scaffolds, it is suggested that

continuous scientific practice permits evolution from an individual viewpoint and be-

comes collaborative.

The summative evaluation study by the Instructional Psychology research unit at the

University of Heidelberg provided valuable feedback on the effects of the lifelab® learn-

ing environment. The principal result is that students do, in fact, learn effectively by us-

ing the virtual lab. Even though the lifelab® learning environment does not lead to any

compensation in individual knowledge differences, knowledge gaps between students

with high and low previous knowledge do still exist after using the lifelab®, the level of

knowledge acquired is equal irrespective of the level of previous knowledge. It may be

seen, then, that the core objective of the project – to develop a learning environment to

teach the life sciences (molecular biology) – has been mainly achieved.

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Looking in detail at students’ motivation, it was demonstrated how important the influ-

ence of interest is on the actual performance of students. Interest, therefore, is an im-

portant factor in learning and schools and teachers should be encouraged and assisted

in gaining and maintaining students’ interest.

Currently, a technically enhanced version the lifelab® learning environment is already

being used in several German schools both in addition to, and as substitute for tradi-

tional, primarily lecture based science teaching. While cognitive and motivational bene-

fits of the approach could be demonstrated in a short-time, intervention-learning labora-

tory research, the outcomes of a long-time intervention in schools compared to other in-

structional approaches will be the subject of future analyses.

Current developments of the software also include an English language version so that

not only classroom borders, but also national borders are not longer barriers to future

young life scientists’ communities.

Acknowledgement:

We thank Michael Lawrence-Slater for helpful comments on an earlier version of this

paper.

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