This article was downloaded by: [University of Notre Dame Australia]On: 23 May 2013, At: 04:08Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: MortimerHouse, 37-41 Mortimer Street, London W1T 3JH, UK

Journal of Biological EducationPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/rjbe20

Learning genetics through an authentic researchsimulation in bioinformaticsHadas Gelbart a & Anat Yarden ba Weizmann Institute of Science, Israelb Weizmann Institute of Science, IsraelPublished online: 13 Dec 2010.

To cite this article: Hadas Gelbart & Anat Yarden (2006): Learning genetics through an authentic research simulation inbioinformatics, Journal of Biological Education, 40:3, 107-112

To link to this article: http://dx.doi.org/10.1080/00219266.2006.9656026

PLEASE SCROLL DOWN FOR ARTICLE

Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form toanyone is expressly forbidden.

The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug dosesshould be independently verified with primary sources. The publisher shall not be liable for any loss, actions,claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly orindirectly in connection with or arising out of the use of this material.

IntroductionAccording to the constructivist learning theory, individualshave their own ways of constructing knowledge, and learningcan be achieved through an active process of construction(Greeno et al, 1996). Curricula in science education should,therefore, encourage active learning, and allow students theopportunity to construct their own knowledge. According tothe situated learning theory, learning is a process of encultur-ation into a community of experts (Brown et al, 1989), andthe retention and application of knowledge depend upon thecontext in which it is acquired (Lee and Songer 2003).Therefore, science educators should integrate authentic ‘real-world’ tasks from the scientific community in order to sup-port the culture of science in classrooms (Lee and Songer2003). These two views are not necessarily contradictory,since learning can be viewed as a combination of theseprocesses (Cobb 1994). One practical way to combine thesetwo views is to provide learners with opportunities to engagein cognitive activities similar to those carried out by scientists,while adapting the content of the activities to the learners’cognitive level.

Biology education, like any other discipline, strives tofamiliarise students with the knowledge, practices, and thinkingprocesses of the scientific community. Over the past twodecades, advances in the field of molecular biology coupledwith advances in genomic technologies – especially due todevelopments in the human genome project – have led to anexplosive growth in the biological information generated bythe scientific community (Collins et al, 2003; NCBI, 2004).The deluge of genomic information has led to an absolute

Learning genetics through an authenticresearch simulation in bioinformatics

Following the rationale that learning is an active process of knowledge construction as well as enculturation into a communityof experts, we developed a novel web-based learning environment in bioinformatics for high-school biology majors in Israel.The learning environment enables the learners to actively participate in a guided inquiry process by solving a problem inthe context of authentic research in genetics. Through the learning environment, the learners are exposed to a geneticsproblem which was developed on the basis of research in which a mutated gene, which causes deafness, was identified.They follow, step by step, the way scientists try to solve it, using the current geneticists’ toolbox. The environment uses anadapted version of the BLAST program (a bioinformatics tool which enables to find similarities between sequences), whichwas modified in a way that enables the teachers and students to use it easily. Using quantitative and qualitative researchapproaches, we were able to show that learning through the bioinformatics environment promotes construction of newknowledge structures and influences students’ acquisition of a deeper and multidimensional understanding of the geneticsdomain. In addition, learning through the bioinformatics environment influences students’ comprehension of the practicesand scientific ways of thinking.Keywords: Bioinformatics; Enculturation; Genetics curriculum; Problem solving; Web-based learning environment.

Hadas Gelbart and Anat Yarden

requirement for computerised databases to store, organiseand index the data and requires the development of specialisedtools for viewing and analysing it. These have brought aboutthe emergence of a new field: bioinformatics (NCBI, 2004).Progress in this field has changed some aspects of researchcurrently conducted in biology. For example, the currentgeneticists’ toolbox is composed of classical genetics approach-es, lab-based molecular biology methods and computer-based bioinformatics tools, which are all put to use for thestudy of the function of genes in relation to phenotype.

The work presented here should be viewed in the contextof the recently expanding research on students’ understandingof the structures, processes and mechanisms of genetics. Afew researchers have reported students’ difficulties in acquiringa coherent cognitive model of the domain, and claimed thatthe students’ major obstacle is to form a conceptual contin-uum between the characters and the molecular mechanisminvolved (Marbach-Ad and Stavy 2000; Stewart and Rudolph2001; Knippels 2002; Lewis and Kattmann 2004). Lewis andKattmann (2004) reported that many biology students (ages15-19) hold an ‘everyday’ alternative conception of genes asan abstract component equivalent to phenotype and are notaware of their chemical characteristics. They suggested thatthis alternative conception might restrict students’ ability todevelop an understanding of the scientific explanations.

These findings led researchers in the field to explore waysof helping students recognise inadequacies in their explanatorymodel of the genetics domain in order to close the gaps intheir cognitive model (Stewart and Rudolph 2001; Knippels2002; Hickey et al, 2003; Lewis and Kattmann 2004). Cartier

JBE 107Volume 40 Number 3, Summer 2006

Authentic research simulation | Gelbart and Yarden

Weizmann Institute of Science, Israel

Dow

nloa

ded

by [

Uni

vers

ity o

f N

otre

Dam

e A

ustr

alia

] at

04:

08 2

3 M

ay 2

013

Gelbart and Yarden | Authentic research simulation

and Stewart (2000) suggested that for students to trulyunderstand genetics, they should understand how the specificsubject-matter knowledge had been generated and justifiedthrough the process of inquiry.

Combining these views, we have developed a learningenvironment that exposes students to the inquiry process inthe field of genetics and bioinformatics (Gelbart and Yarden,2001), by introducing them to a scientific problem that sci-entists were concerned with and that could be solved usingprocedures and tools that are commonly used by geneticists.The learning environment enables the learner to participatein an inquiry process in the context of an authentic scientificproblem with the aim of identifying a particular gene in thegenome.

Our aim was to examine how learning genetics through thebioinformatics learning environment might influence stu-dents’ comprehension of the genetics domain and theiracquisition of inquiry skills. In this paper we introduce therecently developed learning environment of bioinformatics,present an initial characterisation of the way students learnand comprehend genetics concepts, and demonstrate thedevelopment of inquiry skills using this environment.

Description of the learning environmentThe learning environment is composed of three main inter-related parts. The first consists of an interactive multiple-choice test, which can give an indication of the students’prior knowledge in genetics – they should be able to tap intothis, in order to succeed in the research simulation section.The test is accompanied by feedback that may support thestudents in answering the subsequent questions. The students’achievements on the test are graded on an evaluation scale.Thus, a teacher can use this test as a diagnostic tool to deter-mine the students’ actual knowledge.

The second part contains scientific information about thegenome project, adapted to the cognitive level of high-schoolbiology majors. This information includes the goals of theproject, the main characteristics of the genome and some ofthe methods used in the genome project, such as mapping,sequencing and locating a particular gene in the genome. Thispart serves as a scientific background, which connects the high-school biology majors’ curriculum in genetics, to knowledgeand practices in the field.

Since students make sense of what they are learning whenthey see a need or a reason for its use (Bransford et al, 1999;Lewis and Wood-Robinson 2000), a narrative story wasincluded in the environment. The story is about Fernando, atalented 18 year-old rock singer, who would like to start arock band. However, he may carry an inherited mutationthat causes deafness at the age of 40, and in order to plan hiscareer he would like to know whether he carries the mutationthat causes this kind of deafness. Students are invited to giveFernando an answer by participating in the simulation.

The third part includes a simulation which was developedon the basis of a research paper describing research in thefield of genetics in the current genomics era: a mutated gene,which causes deafness in an Israeli family, was identified(Vahava et al, 1998). In this research, a combination of classi-cal genetics, molecular biology and bioinformatics approacheswas utilised. The students participate in a guided inquiryprocess, in which they are exposed to a genetics problem andsolve it step-by-step, similar to the way in which the scientists

tried to solve it, and in this way are introduced to the scien-tists’ modes of thinking (see further on).

In this third part, the students are required to follow fivedifferent assignments which are designed to use approachessimilar to those used by scientists trying to solve similarproblems:

i. in the first assignment, students are asked to harnesstheir knowledge and skills in inheritance problem-solvingto determine the most reasonable inheritance pattern ofa progressive deafness character that is present in apedigree. This activity is anchored in the learners’ priorknowledge and is common in genetics studies in highschool. However, in the problem presented to the stu-dents in this activity – in contrast to typical schoolinheritance problems – finding the most reasonableinheritance pattern of a character that is present in thepedigree and its probability of occurring is not a completesolution, but rather the first stage in linking the pheno-type to the genotype, i.e. connecting between the charac-ter and the molecular mechanism involved.

ii. in the second assignment, students are asked to map themutation which causes deafness, as they learn aboutgenetic markers and linkage maps using a worked-outexample.

iii. in the third assignment, students learn about a modelorganism and how to use the bioinformatics BLAST tool(NCBI, 2005) to find a candidate gene in a genome data-base. The environment uses a version of the BLASTprogram which we modified such that the teachers andstudents could use it easily. It also contains a genomedatabase, which we downloaded to our server.

iv. in the fourth assignment, students compare the normaland mutated alleles using the BLAST-2-SEQUENCEStool (NCBI, 2005), which was also modified for easieruse by the biology students and teachers.

v. in the fifth assignment, students learn about the functionof the protein encoded by the gene they have found andare invited to suggest their own future research questions.

Each of the assignments includes interactive multiple-choice and open-ended questions, to support the learner’sunderstanding of the relevant genetics concepts.The questionsfocus the students on the problem that needs to be solved.In addition, they guide the solvers’ attention to the mainissues of the problem. Each assignment ends with a briefsummary of the scientific ideas and concepts that were learntthrough the previous assignment, and a question that mayguide the learner to hypothesise the scientists’ next step.

Research methodologiesPopulationTwelfth-grade high-school biology majors (n = 19, 17-18years old, 12 females, 7 males) who had learnt through thebioinformatics learning environment were gathered from twodifferent classrooms towards the end of 30 hours of geneticsinstruction. They had been asked to respond to a question-naire prior to their exposure to the bioinformatics learningenvironment (pre-) and were also given a similar questionnaire(post-) about a month after the learning process terminated.

In addition, six students from one of the two former classes,grouped into pairs, were observed and videotaped while learn-ing through the bioinformatics environment in a laboratory

108 JBE Volume 40 Number 3, Summer 2006

Dow

nloa

ded

by [

Uni

vers

ity o

f N

otre

Dam

e A

ustr

alia

] at

04:

08 2

3 M

ay 2

013

Authentic research simulation | Gelbart and Yarden

setting. The students, who volunteered to participate in theexperiment, were considered good students with above-class-average grades in mathematics and biology.

Quantitative analysisPre- and post-questionnaires were distributed before and fol-lowing the intervention. Students’ questionnaires included 14content-based comprehension and inference True/Falsequestions. The students were required to respond to eachstatement and provide an explanation for each of theirresponses. Students’ explanations for eight of the True/Falsestatements in genetics, which were emphasised in the bioinfor-matics learning environment, were collected and analysed.Twelve different types of explanations were identified in thestudents’ answers.These explanations were classified accordingto their content, and the frequency of the different types ofexplanations in the pre- and post-questionnaires was calculated.In addition, students were scored according to the explanationsthey used (0 – no explanation, 0.5 – an incomplete explana-tion, 1 – a complete explanation). Average scores for the 19subjects (pre- and post-questionnaires) were calculated byWilcoxon-Mann-Whitney test (Siegel and Castellan 1988).

Qualitative analysisA qualitative approach was employed in order to study thestudents’ learning processes while using the bioinformaticslearning environment. Six students, grouped into three pairs,were observed and videotaped while learning through thebioinformatics environment in a laboratory setting. The stu-dents were requested to start with the interactive multiple-choice test and then continue with the main problem-solvingactivity. The learning activity lasted for about four hours.

Results and discussion Quantitative analysis of students’ explanations To study how the interactive bioinformatics learning environ-ment influences high-school biology students’ comprehensionof genetics, we examined the students’ acquisition of domainknowledge and their understanding of genetics concepts andprocesses, using the pre- and post-questionnaires. Students’explanations for the True/False statements dealing with genet-ics concepts which were emphasised in the learning environ-ment were collected and analysed. Twelve different types ofexplanations were identified in the students’ answers and theseexplanations were classified into the categories of structuralexplanations and structural-functional explanations accordingto their content (Table 1, overleaf).The structural category wasfurther sub-divided according to its relation to the actual genet-ic material or to genome organisation. The structural-functionalcategory was further sub-divided according to the relationshipsformed between the genetic material and the phenotype, orbetween genome organisation and phenotype (Table 1).

The frequency of each of the explanation types in the pre-and post-questionnaires was calculated and the results areshown in Figure 1. The students’ calculated average score forthe pre-questionnaires was 4.053, SD 1.025, and for the post-questionnaires was 5.526, SD 2.017, P < 0.05.The differencein students’ scores was not found in a control class that learnedgenetics using other instructional methods (data not shown).

Analysis of the students’ explanations indicated an increasein the frequency of eight of the 12 types of explanations(numbers 2, 3, 5, 8, 9, 10, 11, 12) following learning with the

bioinformatics learning environment. Six out of those eightexplanations were classified in the structural-functional cate-gory. In addition, a decrease was observed in the frequency oftwo explanations (numbers 6 and 7) and no change wasobserved in the frequency of two explanations (numbers 1and 4) following learning with the bioinformatics learningenvironment.

Detailed content analysis of the types of explanationsrevealed that, at the structural level, students mainly acquiredthe knowledge that, in addition to the fact that genes in almostall humans are located at the same position on the samechromosome (explanation 2, Table 1, Figure 1), the alleles ofeach gene may be different (explanation 3, Table 1, Fig. 1).At the structural-functional level, students mainly acquiredknowledge of the relationships between the genetic materialand the phenotype (explanation 5, Table 1, Fig. 1), as well asthe relationships between the genome organisation and thephenotype (explanations 8-12, Table 1, Figure 1).

The findings presented here indicate an improvement instudents’ ability to formulate explanations that integrategenetics concepts, following learning in the bioinformaticslearning environment. These explanations represent the pos-sible integration of the concepts of allele and DNA sequencewith genome organisation and phenotype. For example, theexplanation “a particular allele of a gene which exists inevery human may affect an inherited disease” (explanation 9,Table 1, Figure 1) involves an integration of concepts fromvarious levels of organisation within the genetics domain,such as gene-allele-chromosome and genotype-phenotype.Such an ability may indicate a deeper understanding of thebiological mechanisms of genetics. The explanation “thegenes in almost all humans are located in the same positionon the same chromosome, but the alleles of each gene maybe different” (explanation 3, Table 1, Figure 1) may indicatea meaningful understanding of genome organisation andtherefore the conception of a gene as a physical entity whichhas a specific DNA sequence and a specific location in thegenome. Lewis and Kattmann (2004) suggested this under-standing was important in truly understanding the geneticmechanisms. It also indicates an understanding of the rela-tionships between allele, gene and genetic polymorphism.

The decrease in the usage of the explanation “a change in aDNA sequence of a particular gene may change the activity

JBE 109Volume 40 Number 3, Summer 2006

Figure 1. Students’ types of explanations to True/False statements in genetics.Twelve different types of student explanations for True/False statements in genet-ics were identified using pre- and post-questionnaires. The frequency of each ofthe explanations was calculated as the percentage of students (out of the totalnumber of students, n=19) who formulated each explanation. The explanationnumbers correspond to the numbers that appear in Table 1 (overleaf).

Dow

nloa

ded

by [

Uni

vers

ity o

f N

otre

Dam

e A

ustr

alia

] at

04:

08 2

3 M

ay 2

013

Gelbart and Yarden | Authentic research simulation

of the protein it codes for” reinforces the same conclusion.This connection, which is emphasised during genetics instruc-tion, deals only with the molecular level and neglects thephenotypic level.

Marbach-Ad (2001) found that most 12th-graders’answers about the relationship between DNA-trait and gene-trait were correct, but that their explanations were general.We suggest that the improvement in the students’ ability toformulate explanations which represent a possible integrationof genetics concepts can be explained by their use of thebioinformatics environment, which contributed to theiracquisition of a deeper understanding of the genetics conceptsand processes.

Qualitative analysis of students’ learning processes Students’ learning processes while using the bioinformaticslearning environment were explored using a qualitativeapproach. We videotaped three pairs of students learning ina laboratory setting. Stewart (1983) argued that traditionalclassroom assessments of knowledge (simply having studentssolve school inheritance problems) do not provide adequateinsight into what students know or do not know. In contrastto such traditional activities, students who used the bioinfor-matics learning activity were thinking aloud, explaining to oneanother, looking for further explanations, and asking questions,while using texts, worked-out examples, and illustrations.There were episodes which demonstrated students’ usage ofprior knowledge and acquisition of new knowledge while con-centrating on learning the scientific steps required to solvethe scientific problem. The videotaped episodes demonstrateda possible influence of the learning environment on students’acquisition of a deeper understanding of genetics conceptsand processes as well as their comprehension of the natureof the inquiry process. Examples of these episodes are:

I. Episodes which demonstrate alternative conceptions The knowledge-acquisition process sometimes producescontradictions with prior knowledge, which exposes alternativeconceptions. One example of a student’s alternative conceptionwas shown while trying to answer a question:

Student A: “Is it possible that it [the affected allele] isdominant?”Student B: “Yes, if it is Aa.”Student A: “But it does not make sense… because we saidit [the affected allele] is rare.”Student B: “Because the trait is rare.”Student A: “It is a bad trait and it is rare, so is it possiblethat it is dominant? If it had been dominant it shouldhave been dominant in the population.”

The students’ conversation demonstrates that one of them(student A) cannot distinguish between the scientific modelexplaining the relationships between the inheritance proba-bility of a dominant phenotype which is affected by a dom-inant allele in a family, and the frequency of a particular allelein the population. However, we suggest that although thestudent holds an alternative conception, the questions sheasked indicate her attempt to integrate different models ingenetics, and can therefore be an important stage in the processof constructing new knowledge structures of the domain.

In addition to students’ alternative conceptions in thegenetics domain, we found episodes which demonstrate stu-dents’ alternative conceptions of the goals of the scientificwork:

Student A: “I don’t know if it leads to a solution, I mean,the goal here is to help people, isn’t it?”Student B: “No, the goal is to locate a gene in thegenome.”

In this example, student A perceives the research goal asfinding the solution to a specific problem, while student B

110 JBE Volume 40 Number 3, Summer 2006

Table 1. Explanations given by students to true/false statements in genetics, classified to structural explanations and to structural-functionalexplanations, according to their content.

Structural explanations Structural–functional explanations

Geneticmaterial

Genomeorganisation

1. Different alleles of a particular gene havedifferent DNA sequences.

2. The genes in almost all humans are located inthe same position on the same chromosome.3. The genes in almost all humans are located inthe same position on the same chromosome, butthe alleles of each gene may be different.

4. The DNA sequence of a particular allele maybe involved in the determination of a dominant/recessive phenotype.5. Different alleles (stemming from differentmutations of the same gene) may influence theprobability of having a genetic disease.6. An examination of the DNA sequence of anindividual enables to determine if he has a ten-dency to become affected by a genetic disease.7. A change in a DNA sequence of a particulargene may change the activity of the protein itcodes for.

8. Differences in alleles almost always indicatenormal differences between individuals.9. A particular allele of a gene which exists inevery human may affect an inherited disease.10. Different alleles (stemming from differentmutations of the same gene) may be involved inthe determination of a similar phenotype (of agenetic disease).11. A comparison between DNA sequences ofaffected and unaffected individuals enables todetermine if an individual has a tendency tobecome affected by a genetic disease.12. Most of the phenotypes resulted from multiplegenes expression.

Relationshipsbetweengeneticmaterial andphenotype

Relationshipsbetweengenomeorganisationand pheno-type

Dow

nloa

ded

by [

Uni

vers

ity o

f N

otre

Dam

e A

ustr

alia

] at

04:

08 2

3 M

ay 2

013

Authentic research simulation | Gelbart and Yarden

perceives it as a basic inquiry process. These two perceptionsare not necessarily contradictory since, according to Dewey(1938), inquiry progresses by the determination of a genuineproblem that may lead to various activities in order to searchfor a solution. These two views are likely to emerge in thecontext of research in the field of genetics, which has basicand applied aspects. Nevertheless, the way the students per-ceived the research goals may influence their understandingof the inquiry process as an ongoing process motivated byscientists’ curiosity to acquire new knowledge, as well as bytheir desire to improve quality of life and to find solutions tovarious problems.

II. Students’ understanding of the role of scientific practicesThe learning environment exposes students to current prac-tices in genetics and provides them with insight into scientif-ic ways of thinking. The possible influence of this approachon students’ acquisition of a deeper understanding of thegenetics domain and on their comprehension of the role ofscientific practices and scientific thinking processes can bedemonstrated by the following example:

Student A: “To locate a particular gene, is it like youalready know the gene and then you find it, or are yousearching for something and you find it and only thenyou determine its function?”Student B: “So we are actually taking a trait and lookingfor its gene?”

In this example, the students’ questions demonstrate thatthey understand the genotype-phenotype connection, andthat the specific knowledge of genetics is generated and jus-tified through the process of inquiry. This was previouslysuggested by Cartier and Stewart (2000) as a necessary fac-tor for the development of a coherent cognitive model ofgenetics. We suggest that the students’ questions demon-strate their assimilation of the scientific thinking process andstem from their awareness of the role of methods in the sci-entific process.

Another example of the influence of students’ exposure toscientists’ practices and scientific thinking can be given by astudent’s responses at the beginning of the activity and towardsthe end of it. When the student solved the first assignmentof the activity (which deals with classical genetics), he noted:“So, we determined that an offspring of a deaf individual inthis family has a probability of 50% to become deaf in hisadulthood… but it is only the probability… this is the reasonI don’t like genetics…”

But towards the end of the activity, considering the ongoingnature of the inquiry process, he concluded: “So in all of theseassignments they, in principle, investigated one case? Westarted from… first we found how it is inherited, then themarkers. After the markers we found exactly the gene. Nowwe are checking what happened to the mutation?”

And after the researcher said that this was correct, he con-tinued: “Oh, I wonder what will happen at the end… maybewe will do gene therapy…”

We suggest that these two responses indicate a change inthe student’s perception of the genetics domain whichoccurred during the usage of the learning environment.

Educational implicationsThe findings presented here indicate that learning throughthe bioinformatics environment improves the students’ ability

to formulate explanations which integrate between phenotype(a character), genotype (gene, allele, DNA sequence) andgenome organisation. These findings suggest that using thebioinformatics learning environment promotes the construc-tion of new knowledge structures of the genetics domain andtherefore influences students’ acquisition of a deeper, multi-dimensional understanding of the domain. Moreover, thefindings suggest that use of the bioinformatics learning envi-ronment also influences students’ understanding of the scien-tific practices and the ways of thinking.

Learning often occurs when students are aware of the goals,in a context which is relevant and meaningful to the learner(Collins et al, 1989). Real world activities are believed toprovide learners with the motivation to acquire new knowl-edge and an opportunity to apply that knowledge (Mistler-Jackson and Songer 2000). Thus, we suggest that taking anactive part in solving an authentic scientific problem providesthe students with a context to apply existing geneticsdomain knowledge, acquire new knowledge, and present thatknowledge in different ways. In addition, the use of classicaland molecular genetics tools and bioinformatics tools intro-duces the learner to the practices of the genetics communi-ty through the process of inquiry, and may promote thebeginning of the enculturation of students into the commu-nity of geneticists and bioinformaticians.

Two different teaching sequences to incorporate the bioin-formatics learning environment into the genetics curriculumwere suggested by biology teachers who were exposed to thelearning environment. Most of the teachers thought that thelearning environment should be taught at the end of geneticsinstructions and explained it by the requirement to havebroad prior knowledge while learning using the environment.A few other teachers thought that the activity should accom-pany genetics instruction and suggested teaching through oneassignment at a time, in parallel to the progress in theinstruction of genetics.

Considering the predicted gap between biology teachers’knowledge of school genetics and the accumulated knowl-edge within the genetics discipline, another important impli-cation of the learning environment lies in its use as a tool forteachers’ professional development. The user-friendly set upof the BLAST program and the genome database, which areavailable within the environment, can be used by the teachersduring workshops. Thus, this environment can provide a con-text for acquiring a deeper understanding of current geneticsamong biology teachers, as well as providing an opportunityto obtain an insight into scientific practice in the currentgenomics era.

ReferencesBransford J D, Brown A L and Cocking R R (1999) How People

Learn: Brain, Mind, Experience, and School. Washington, DC:National Academy of Sciences Press.

Brown J S Collins A and Douguid P (1989) Situated cognition andthe culture of learning. Educational Researcher 18(1): 32-42.

Cartier J L and Stewart J (2000) Teaching the nature of inquiry: fur-ther developments in a high school genetics curriculum. Scienceand Education 9: 247-267.

Cobb P (1994) Where is the mind? Constructivist and socioculturalperspectives on mathematical development. EducationalResearcher 23(7): 13-20.

Collins A Brown D E and Newman S E (1989) CognitiveApprenticeship: Teaching the Crafts of Reading, Writing, andMathematics. In: Knowing, Learning, and Instruction: Essays in

JBE 111Volume 40 Number 3, Summer 2006

Dow

nloa

ded

by [

Uni

vers

ity o

f N

otre

Dam

e A

ustr

alia

] at

04:

08 2

3 M

ay 2

013

Gelbart and Yarden | Authentic research simulation

Honor of Robert Glaser, eds. Resnick L B pp. 453-494. Hillsdale,NJ: Lawrence Erlbaum Associates.

Collins F S Green E D Guttmacher A E and Guyer M S (2003) Avision for the future of genomics research. Nature 422: 835-847.

Dewey J (1938) Logic: The Theory of Inquiry. New York: Henry Holtand Company.

Gelbart H and Yarden A (2001) Bioinformatics - Deciphering theSecrets of the Genome. http://stwww.weizmann.ac.il/bioinformat-ics. Rehovot, Israel, The Amos de-Shalit Center for ScienceTeaching.

Greeno J G Collins A M and Resnick L B (1996) Cognition andlearning. In Handbook of Educational Psychology, eds. Calfee R Cpp. 15-46. New York: Macmillan Library Reference.

Hickey D T Kindfield A C H Horwitz P and Christie M A T (2003)Integrating curriculum, instruction, assessment, and evaluation intechnology-supported genetics learning environment. AmericanEducational Research Journal 40(2): 495-538.

Knippels M C P J (2002) Coping with the Abstract and ComplexNature of Genetics in Biology Education: The Yo-yo Learning andTeaching Strategy. Department of Biological Education. Utrecht,University of Utrecht.

Lee H S and Songer N B (2003) Making authentic science accessi-ble to students. International Journal of Science Education 25(8):923-948.

Lewis J and Kattmann U (2004) Traits, genes, particles and informa-tion: re-visiting students’ understandings of genetics. InternationalJournal of Science Education 26(2): 195-206.

Lewis J and Wood-Robinson C (2000) Genes, chromosomes, celldivision and inheritance - do students see any relationship?International Journal of Science Education 22(2): 177-195.

Marbach-Ad G (2001) Attempting to break the code in student

comprehension of genetic concepts. Journal of Biological Education35(4): 183-189.

Marbach-Ad G and Stavy R (2000) Students’ cellular and molecu-lar explanations of genetics phenomena. Journal of BiologicalEducation 34(4): 200-205.

Mistler-Jackson M and Songer N B (2000) Student motivation andinternet technology: Are students empowered to learn science?Journal of Research in Science Teaching 37(5): 459-479.

National Center for Biotechnology Information [NCBI] (2004) Ascience primer. www.ncbi.nih.gov/About/primer

National Center for Biotechnology Information [NCBI] (2005)BLAST. www.ncbi.nlm.nih.gov/BLAST.

Siegel S and Castellan N J (1988) Nonparametric Statistics for theBehavioral Sciences. New York: McGrow-Hill, inc.

Stewart J (1983) Student problem solving in high school genetics.Science Education 67(4): 523-540.

Stewart J and Rudolph L (2001) Considering the nature of scientif-ic problems when designing science curricula. Science Education85(3): 207-222.

Vahava O Morell R Lynch E D Weiss S Kagan M E Ahituv MorrowJ E Lee M K Skvorak A B Morton C C Blumenfeld A Frydman MFriedman T B King M C and Avraham K B (1998) Mutation intranscription factor POU4F3 associated with inherited progres-sive hearing loss in humans. Science 279: 1950-1954.

Hadas Gelbart is a PhD student at the Department of ScienceTeaching, Weizmann Institute of Science. Anat Yarden (corre-sponding author) is Head of the Biology Group, Departmentof Science Teaching, Weizmann Institute of Science, PO Box 26,Rehovot 76100, Israel. Email: anat.yarden@weizmann.ac.il

112 JBE Volume 40 Number 3, Summer 2006

INNERTEMPLEGARDEN

CITYTHAMES

LINK

HOLBORN

A 5 2 01

C L E R K E N W E L L R D

T H EO B A L D S R D

BLOOM

SB URYWAY

A40

H I G H H O L B O R NA 4 0 H O L B O R N

A5

20

0

GR

AY

' SI N

NR

D

RO

SE

BE

RR

YA

VE

AV

E

A 3 211

V I C TO R I A E M B A N K M E N T

A3

211

NE

WF

ET

TE

RL A

NE

L AN

E

FE

TT

ER

LA

F L E E T S T

NE

WB

RID

GE

ST

FA

RR

- I NG

DO

NS

T

FAR

RI N

GD

ON

RD

A2

01

A2

01

KI N

GS

WA

YA

42

00

SO

UTH

AM

PTO

NR

OW

A4

20

0

A3

01

ALD

WYC H

A4

STRAND

H O L B O R N V I ADU C T

N E WG AT E

ST

B R I D ES TS T

SH

OE

LA

B52

1H

AT

TO

NG

AR

DE

N

CH

AN

CE

RY

LA

NE

B4

00G

TQ

UEEN

ST

LON

G

ARC

E

EN

DE L L

S T

G U I L D F O R D ST

B OW

ST

WE L L I N

G TON

ST

ST

JO

HN

ST

C H A R T E R H O U S E STB 5 0 0

LUD G AT E H I L L

BL

AC

KF

RIA

RS

LA

NE

LA

NE

CA RT E R L A N E

LO N GL AN E

W ES TS M I T H F I E L D

L I T T L EB R I TA IN

LE

AT

HE

RL

AN

E

G R E V I L L E ST

COWC R O S S S T

TU

RN

MI L L

STR

EE

T

BR

OO

KE

ST

F L E E T S T

MID

DL

ET

EM

PLE

LA

TE

MP

LE

AV

EB

OU

VE

RIE

ST

ST

WH

I TE

FR

I AR

SS

TS

T

T U D O R ST

SH

OE

LA

DO

RS

ET

RI S

ER

I SE

FE

TT

ER

LA

NE

B R E A M ' S

B L D G SB L D G S

FU

RN

IVA

LS

T

C A R E Y S T R E E T

LI N

CO

LN

' S

I N N F I E L DS

SE

RL

ES

T

P O

RTU G A L ST

T E M P L E P L AC E

RE

DL

I ON

ST

G R E AT O R M O N D ST

ST

JOH

NS

T

E AG L E S T

JO

CK

EY

' SF

I EL

DS

G R E ATRU S S E L L

ST

DRU

RYLA

NE

D R U RY

LANESH

E LTO

NST

W I L D S T

KE

MB

LEST

SA

VO

YS

T

G A R R I C KS TS T BED

FOR

DS

T

EX

E T E RST

GI L

TS

PU

RS

T

OL

DB

AI L

EY

SA

FF

RO

NH

I L L

Q U E E N V I C TO R I A S T

BE

LL

YA

RD

WATERLOO BRIDGE

BLACKFRIARSBRIDGE

CHANCERYLANE

TEMPLE

ROYAL COURTSOF JUSTICEOF JUSTICE

ROYAL COURTSOF JUSTICE

R E D L I O NR E D L I O NC O U RO U R T

R E D L I O NC O U R T

BLACKFRIARSBLACKFRIARSTEMPLE

CHANCERYLANE

HOLBORN

CITYTHAMES

LINK

COVENTGARDENCOVENTGARDEN

STBARTHOLOMEW'STHOLOMEW'S

HOSPITAL

STBARTHOLOMEW'S

HOSPITAL

LINCOLN'S INNFIELDS

GRAY'S INNGARDEN

FARRINGDONFARRINGDON

OS data. PU 100040256. CrOS data. PU 100040256. Crown 2003.own 2003.

Give WGive Way 2005 ay 2005 TM TM Tel: el: 0800 019 0027.0800 019 0027.

OS data. PU 100040256. Crown 2003.

Give Way 2005 TM Tel: 0800 019 0027.

Institute of Biology� ��� ���� ���

������

�� � ���

���� ��� ���� ����

���� ��� ���� ����

N

Dow

nloa

ded

by [

Uni

vers

ity o

f N

otre

Dam

e A

ustr

alia

] at

04:

08 2

3 M

ay 2

013