A WEB-BASED INTRODUCTION TO BIOINFORMATICS

Epistemological Beliefs in Bioinformatics

Gerald Lirk

, Peter Kulczycki

, Stefan Winkler

and Jörg Zumbach

Medical Informatics and Bioinformatics, Univ. Appl. Sci. Upper Austria, Softwarepark 11, 4232 Hagenberg, Austria

Department of Science Education and Teaching, University of Salzburg, 5020 Salzburg, Austria

Keywords: Epistemological beliefs, Knowledge, OpenLab projects, Learning bioinformatic tools.

Abstract: An OpenLab learning course, taking place in both class room and wet and computer lab, was used to reflect

the students` and undergrads` impression of bioinformatics. Three main aspects were investigated. First,

what is their opinion about the bioinformatics working environment? Second, how can biological,

mathematical and scientific knowledge increase and third, do epistemological beliefs change during

attendance of a specific course? A total of 735 persons were surveyed with two different, newly designed,

questionnaires. The participants consider bioinformatics more biological than graduated scientists. The

increase of knowledge is significantly higher when doing additional data analysis and computer work than

working only in the wet lab. We found also a significant change in the epistemological beliefs. Therefore,

we recommend a data analysing lecture and online-support in an OpenLab-course. Respectively, we

recommend an interdisciplinary introduction to bioinformatics.

1 INTRODUCTION

Bioinformatics as an interdisciplinary science is best

suitable for an introduction to both students and

undergraduates to show them a good approach to

science. To verify this, we established a course

module for starters in biology and bioinformatics,

including an e-learning platform and practical works

in wet and computer labs. The participants were

asked for their opinion on bioinformatics (similar to

Barton, 2008), science and the nature of knowledge

and knowing. We compared their view at the

beginning and at the end of the course with the help

of standardised questionnaires.

1.1 Epistemological Beliefs

The bases of modern scientific work are appropriate

techniques in the laboratory and at the computer

(Mayer, 2007). First, an experiment must be planned

and realized, an object has to be described, measured

or modified. The collected data must be checked

against a pre-formulated hypothesis. This evaluation

is often associated with mathematical work on the

computer. However, even in natural sciences, it is

not always possible to make unambiguous

statements. Due to the subjective understanding of

science (= epistemological belief), every person has

their own view of science and its limitations. There

is no doubt that with increasing experience in

science, this understanding will change (Urhahne,

2004).

1.1.1 History

Epistemological beliefs, or beliefs about the nature of

knowledge and knowing, have been subject to

research for 50 years and are still a target of high

research interest (Billett 2009, Conley 2004, Porsch

2010).

The study of epistemological beliefs began with

the work of William G. Perry (Perry, 1968/1999),

who in the late 1950s interviewed Harvard college

students using open interviews about their

experiences and insights during their college years.

At the beginning of their college time students

believed that knowledge was passed from authorities

to students as simple, immutable facts. At the end of

their degree, however, they concluded that

knowledge was complex and changeable, and based

on rationale and empirical studies (Schommer-

Aikins, 2004). Perry developed a nine-step scheme

of intellectual and ethical development, in which he

describes the mental changes of the subjects.

378

Lirk G., Kulczycki P., Winkler S. and Zumbach J..

A WEB-BASED INTRODUCTION TO BIOINFORMATICS - Epistemological Beliefs in Bioinformatics.

DOI: 10.5220/0003887803780385

In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (SSTB-2012), pages 378-385

ISBN: 978-989-8425-90-4

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

Starting with these initial studies further research

analysed the epistemological beliefs using mostly

longitudinal studies and proprietary models. King

and Kitchener (1992, 1994, 2002, 2004) developed a

seven-step development model based on interviews

(i.e., the Reflective Judgement Model). This model

represents epistemological beliefs - similar to Perry

(1968/1999) - in one dimension. According to King

and Kitchener (1992; Urhahne, 2004) most senior

level college students reach most commonly level

four of seven, the "quasi-reflective stage". Here

knowledge is vague and ambiguous.

With the works of Schommer (1990, 1992, 1995,

1998) the access to explore epistemological beliefs

changed: From this point of view they are not seen

as a single, continuously changing construct, but

rather as a complex system of independent ideas. In

her works she analyzed these components with

quantitative questionnaire. Four layers were found

(Schommer-Aikins, 2003):

• Stability of Knowledge: never-changing vs

continually evolving.

• Structure of Knowledge: scattered pieces vs

strongly interacting concepts.

• Speed of knowledge acquisition: very fast / never

vs step by step.

• Ability to learn: from birth set vs lifetime

improvement.

1.1.2 Determinants

Various factors influence the development of

epistemological beliefs:

Culture: In most models one expects an interaction

between the learners and their environment.

Therefore, it is obvious that culture has serious

impact on people`s behaviour. The factor structure

of Schommer (1990) could not be replicated in other

cultures. This suggests that this model (and maybe

other models too) is not transferable to other cultures

and that there are appropriate cultural influences on

the development of epistemological beliefs (Chan,

2002; Tasaki, 2001).

Gender: Gender differences were already predicted

and studied in the 1980s (Belenky, 1986; Baxter

Magolda, 1992). Nevertheless, these results are not

unambiguous. Bendixen (1998), Buehl (2002), Chan

(2002) and Conley (2004) found no differences,

whereas Wood (2002), Schommer-Aikins (2002)

and Hofer (2000) found gender-specific variations in

each dimension. For instance, the latter describes,

that women at the beginning of college consider

knowledge as less secure and rely less on authority

than their male colleagues.

Age and education: The studies cited above were

conducted among older adolescents and young

adults, presumably because the researchers worked

in higher education. The studies with younger

participants reveal the following results: pre-school

children show a pre-dualistic stage, in which only

the personal view is accepted as true and equal

coexistence is not accepted (Burr, 2002). Among

primary school children different dimensions and

stages of development of the epistemological belief

of different models could be shown (Conley, 2004;

Elder, 2002). Obviously, education shows a greater

influence on the formation of epistemological beliefs

than age (Conley, 2004; Schommer, 1998).

Methods of Measurement: To determine

epistemological beliefs, several methods have been

developed. Especially at the beginning of this

research, qualitative interviews were conducted

(Hofer, 2004; King, 1994; Perry 1970/1999). These

approaches resulted in one-dimensional models of

the development of epistemological beliefs; later

multifactorial theories were developed on the basis

of questionnaire surveys (Belenky, 1986; Hofer,

2002; Jehng, 1993; Schommer, 1990). Wood (2002)

showed that the questionnaires had specific

problems with reliability and reproducibility. On

several occasions difficulties with the questionnaire

by Schommer were described (Clarebout, 2001;

Qian, 1995).

1.2 Open Labs

OpenLabs are widely used for extracurricular

education of students, especially in physics and

biology (Anon., 2011). There, classes are invited to

special courses, doing experiments on their own.

Engeln (2004) and Euler (2005) describe the aims of

such lab projects for students:

• Promoting interest in and openness to technology

and science.

• Convey scientific content, working methods, and

views.

• Convey the importance of science and

technology to society.

• Breakdown threshold of fears and reservations

about science and technology.

• Secure the next generation of technical and

scientific courses and professions.

Since 2008 the OpenLab in Hagenberg, Austria

(lab-xperience, see www.lirk.at/lab, in German) has

been offering schools the opportunity to both

conduct molecular biology experiments and to

evaluate the data online, using partly specially

developed computer programs (Fig. 1). This module

A WEB-BASED INTRODUCTION TO BIOINFORMATICS - Epistemological Beliefs in Bioinformatics

379

is also used for first year undergrads in

bioinformatics. Other OpenLabs in German-

speaking countries have either wet lab or

information science courses.

The aim of this study is to determine a possible

change of epistemological beliefs and the growth of

knowledge among students in the course of the

project (preparation, laboratory work, data analysis).

Figure 1: Sequence of a “lab-xperience-project" in

Hagenberg: the lecturer introduces the subject to the class.

The students work independently with the provided online

information (top). After that they collect data in the wet

lab (right). These data are further evaluated using the

computer (bottom) and stored for further investigations in

a database (left). In this way we obtain a larger amount of

data. Following classes can anonymously access these data

and carry out more accurate statistical analysis.

2 METHODS

2.1 Infrastructure

The e-learning-platform is based on PHP 5 with a

mysql-database, accessible via www.lirk.at/lab with

a personal password for each lecturer and

participant. First the lecturer selects the background

of the students or undergraduates, e.g., lab

techniques, statistics, algorithms or genetic testing.

Depending on this selection, a specific learning task

is assigned to the students.

The wet lab has 15 equally equipped workplaces.

Larger classes were split during the period of DNA

isolation and PCR.

2.2 Questionnaires

Two questionnaires were conducted during our

study: First, we asked students about their view on

bioinformatics in a Likert-scaled (1-5) questionnaire

with 44 items about job profile, the required

expertise of a bioinformatician, their scope of tasks

and duties and their working place. These data were

compared with the answers given by graduated

students.

Based on their experience, we divided the

surveyed in four groups:

• External: 87 students who have never heard

anything about bioinformatics before or had a

first visit to our OpenLab.

• Pre: 65 students were interviewed before the

start of the wet lab section of an OpenLab

course.

• Post: 77 students were asked after the wet lab

course.

• Standard (=Graduates): 29 graduates were

asked as a standard group. They had completed

at least a five-year degree in bioinformatics.

They worked on bioinformatics projects

already in the second year of their studies and

did both their Bachelor`s and Master`s thesis

with companies or institutes for about two

years. Some of them were asked about their

view on bioinformatics some years after

completion of their degree and extensive work

experience in that field.

On the other hand, the epistemological beliefs

and the growth of knowledge in bioinformatics were

explored. Unfortunately, existing questionnaires

(e.g. from Schommer) lack of poor reliability and

inconsistency in the factor analysis (Wood, 2002).

Therefore, in a first step, a separate questionnaire

was developed (also Likert-scaled 1-5), measuring

the factors of scientific sources, development,

methodology and review. In parallel, in the same

questionnaire, the increase of knowledge was

determined with 22 items. This questionnaire was

given twice: before the wet lab course started (i.e.

pre-test) and after the lab work or after data analysis

respectively (i.e. post-test). The test consisted of

questions and single choice answers about DNA,

molecular biology methods (PCR), scientific

working and statistics.

Over a period of two years, about 30 classes

were surveyed in four phases. The students and first

year undergrads did the following (Table 1):

• Phase I: 161 conducted the wet lab experiments

including an oral presentation, but without

online support (flash-animations, computer

programs and bioinformatics analysing tools).

• Phase II: 147 conducted the wet lab

experiments and additional presentation of data

analysis and bioinformatics tools.

BIOINFORMATICS 2012 - International Conference on Bioinformatics Models, Methods and Algorithms

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• Phase III: The online platform was presented to

127, but hardly used. Therefore, this phase is

nearly the same as phase II.

• Phase IV: 42 participants performed the entire

project including the use of the online platform.

Table 1: The participants finished different parts of the

course module, depending on the phase. The numbers of

“x” show the intensity with which the students and first-

year undergraduates worked.

Finished work

Phase

I II III IV

Online Preparation x xx

Wet Lab xxx xxx xxx xxx

Presentation Biology xxx xxx xxx xxx

Presentation Bioinformatics xx xx xxx

Data Analysis x x xxx

All questionnaires were paper-based. The

answers were transferred to MS Excel and exported

to SPSS. The statistical analysis was done using

SPSS 17. Multiple tests were corrected by

Bonferroni.

2.3 Class Project

Most frequently, both students and undergrads

chose a module called “CSI Hagenberg”. There an

ALU-Sequence (Alfred-DB UID SI000152I,

Rajeevan, 2011) in the DNA of each participant was

analysed (Batzer, 2002). In phase IV of our study the

following steps had to be taken:

2.3.1 Online Preparation

Before coming to the wet lab, the participants had to

take some preliminaries on the computer:

• Reading operating instructions.

• Watch some flash animations of working skills

(pipetting, preparing a dilution series, procedure

of PCR, etc).

• Solving some tasks (calculating centrifuge

acceleration, finding primer sequences,

Calculation annealing temp cf. Robertson, 2008).

• Learning about the rules of Mendelian

inheritance and human pedigrees with computer

programs and games.

2.3.2 Working in the Wet Lab

The course lasted app. 5 hours. In this time

participants had to isolate DNA and a PCR had to be

completed. PCR was carried out with a VWR

thermal cycler for 35 cycles, followed by an Agarose

gel electrophoreses. The result was a picture of the

gel with a DNA-marker and 1-2 fragments for each

participant.

2.3.3 Presentation Biology

While PCR was running, the Mendelian rules and

PCR technique were repeated and the biology of

ALU- and VNTR sequences were described.

2.3.4 Presentation of Bioinformatics

The biological explanations were supported by

different bioinformatic databases and tools and the

algorithms behind them were briefly explained:

• Genebank for searching the special ALU

sequence.

• DotPlot for comparing the sequence with/without

ALU and showing the poly-A-part inside ALU.

• Ensembl-Blast and lAlign for seeking the primer

sequences in the human genome.

• Sometimes clustalW for creating a phylogenetic

tree of ALU-Sequences.

2.3.5 Data Analysis

After producing genetic data in the wet lab and

explaining both biology and the tools, the

participants worked on their own data. They should:

• Calculate the size of their DNA fragments on the

gel-image with the help of the DNA marker and

a regression line with MS Excel.

• Making a descriptive statistics about the genetic

data of the class.

• Compare these data with the data of all students

and undergrads taking the course so far with

inductive statistics.

• Calculate the Hardy-Weinberg equilibrium.

• Consider and calculate sensitivity and specificity

of medical tests.

• Discuss the need for positive and negative

controls, referring to newspaper articles about the

“phantom of Heilbronn”, a German criminal case,

where contaminated sample sticks were used.

3 RESULTS

3.1 Opinion about Bioinformatics

258 students were asked about their view on

bioinformatics. The average age was 17.2 years (SD

= 1.5 years). 56% were female. We used the answers

A WEB-BASED INTRODUCTION TO BIOINFORMATICS - Epistemological Beliefs in Bioinformatics

381

given by 29 graduates from our university as a

standard to compare with the interviewed students in

this survey (see above).

The overall impression of students on

bioinformatics is assessed in one question in the

questionnaire. The result is shown in Figure 2.

Students` view on bioinformatics differs

significantly from the one of graduates. The latter

see themselves as computer scientists, data analysers

and statisticians (specified in an open question).

Students consider bioinformaticians more or less as

specialists both in computer science and in biology.

The differences between the non-standard groups are

not significant (p>0.8).

Figure 2: The chart shows (in percentage) what students

think about the work of a bioinformatician. Answers in

percentage to the question: “Do you consider a

bioinformatician more to be a computer scientist or a

biologist?”.

Figure 3: The self-image differs considerably between the

groups. Presented are the answers in percentage to the

question: “I have a clear understanding of the tasks of a

bioinformatician”.

How sure are the respondents about their view on

bioinformatics? The confidence here differs between

the groups (see Figure 3). There is no difference

between the external and the pre-group, but the

believes change from pre to post (p<0.001). The

intervention between pre and post was a 5h wet lab

course including a presentation without data

analysis. The difference to the graduates (standard-

group) is highly significant (p<0.001), as they are

more confident with the working field of

computational biologists.

In three out of 44 items we found significant

differences between the pre and post group, so the

image changed during the intervention. Such

differences could be shown in 22 items between the

post- and the standard group.

3.2 OpenLab Course

3.2.1 Epistemological Belief

In total, 477 persons were examined. The average

age was 17.8 years (SD = 1.6). More than 90% were

in their last year of high school. 56.3% of the

interviewees were female.

Because of the low values in reliability of the

questionnaire provides by Schommer (1990; cf.

Clarebout, 2001; Qian, 1995), a new questionnaire was

constructed based on questions from different other

instruments. The result of the factor analysis is shown

in table 2. Four factors with a Cronbach’s Alpha

between 0.36 and 0.76 were found (Cronbach, 1951).

Table 2: Factors with Cronbach’s Alpha of the newly

designed questionnaire. Factors such as sources,

development and review showing different reliability

scores are numbered 1-4.

Factor Cronbach’s Alpha

1 Scientific Sources 0.76

Scientific

Development

0.65

Scientific

Methodology

0.36

4 Scientific Review 0.64

This newly designed questionnaire was

administered to students and undergraduates in four

different phases twice: the first time before, the

second time after the course. The difference of the

measured factors between these two tests was

calculated and is shown in figure 4.

100

more

informatician

inf+bioequally morebiologist

100

more

informatician

inf+bioequally morebiologist

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Figure 4: The comparison of the four questionnaire factors

shows the change of epistemological believes in phase IV.

The Factor 1-4 of phases I-IV are plot against the

difference between pre- and post-test. Factor 1 is raising

which shows that after the intervention participants

‘confidence in scientific sources’ is about one grade (0.83)

lower than before. Factor 2, 3 and 4 are lower. As a result,

the interviewees believe more strongly in the permanent

development of science (-0.31), in the importance of

experiments in science (-0.56) and that experiments have

to be repeated to achieve a reliable conclusion (-0.39). The

differences of all factors between Phase I, II or III to Phase

IV, calculated with a MANOVA, are significant.

3.2.2 Increase in Knowledge

The second questionnaire was also administered to

assess knowledge acquisition. The questions referred

to different topics which were discussed during the

course, ranging from biological questions to

scientific working and mathematics. Starting off

with simple questions reflecting common knowledge

of these fields, the questions got more and more

challenging. As shown in Figure 5 the increase of

knowledge is greatest in phase IV: with an average

of 9 (41%) more correct answers (from an overall of

22 questions).

Significant differences were found especially in

the pre-test in phase III, in the post-test in phase IV.

There is a constant increase of knowledge in the pre-

test starting with phase I. One explanation might be

that the same teachers heard the topics already

during their first visit (phase I) and might have

prepared the class (with different students) for the

next visit. The knowledge in the post-test is slightly

decreasing. Obviously, the presentation of data

analysis and bioinformatics tools in such a project

alone does not make sense. Significantly better

results can be produced by an additional analysis

unit at the computer after the wet lab visit. Phase IV

shows considerable differences to phase I-III.

Figure 5: The box and whiskers-plots shows the increase

of knowledge between the pre-test (dark) and post-test

(light) in phases I-IV. The ordinate shows the number of

correct answers. The maximum was 22. The increase of

knowledge is greatest in phase IV, which includes

additional online tools and later data analysis.

4 CONCLUSIONS

Online introduction to bioinformatics like the one

illustrated here - including a wet lab and a

computational part-have several consequences. It

was shown that the view students have on

bioinformatics already changes during a single wet

lab course in a bioinformatics institute. Students

lacking contact to bioinformatics tend not to be sure

what a computational biologist does for a living.

After visiting the OpenLab they often change

their mind just slightly, but results indicate that they

are more confident about their believes in what this

field of science is about and how it works. Their

picture of bioinformatics tends to be similar to the

standard (graduates) group, but is less computer

science-oriented. This might be explained by the fact

that the intervention for this questionnaire here

almost only took place in the wet lab. Nonetheless, it

could be shown that students consider computer

skills (development of algorithms, databases, etc.)

for a computational biologist to be significantly

more important than before, despite the fact that they

did not do any analysis. Mentioning bioinformatics

and answering questions about this field is obviously

enough.

Another consequence of a bioinformatics

OpenLab project is the increase of knowledge.

Students could already fall back on more or less

general knowledge of biology before the practical

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informatician

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A WEB-BASED INTRODUCTION TO BIOINFORMATICS - Epistemological Beliefs in Bioinformatics

383

course started, e.g. structure of DNA and main

principles of PCR. Striking improvement could be

achieved in working (e.g. lab security), special PCR

knowledge (construction of primers) and important

scientific principles (use of markers, blanks and

standard) though. Minor changes were observed in

the use of positive and negative controls and

statistics. The time for learning these scientific and

mathematical skills might have been too short.

Especially in phase IV, however, there is a

significantly higher level of knowledge. That means

that working with bioinformatics tools and statistical

analysis of data result in a deeper learning and, thus,

increased knowledge acquisition. Thus, it seems to

be not enough to just show the results of a data

analysis and bioinformatics tools. It is rather

necessary to spend some time actively working with

these data.

The third consequence is the change of the

epistemological believes. Only extensive

examination of the data of an experiment can change

this belief in science. Significant changes could only

be seen in phase IV.

Therefore, we strongly recommend a following

(computer based) data analysis conducted by

students for OpenLabs. This results not only in

higher domain knowledge, but also in a better

understanding of science and therefore in a more

accurate development of higher order

epistemological believes. By providing such an

approach as mentioned here, students can develop a

deeper insight into this discipline resulting in a great

step towards a deeper understanding of science.

The limitation of the study is that only three

small classes have been carried out so far in phase

IV (a longer project time with online support). In

future research, this number will be increased.

REFERENCES

Anon., 2011. LernortLabor. St.Ingbert: Hempelmann, R.,

Available from: www.lernort-labor.de [Accessed Date:

2011-11-06]

Barton M., 2008. Manchester. Available from:

http://openwetware.org/wiki/Biogang:Projects/Bioinfo

rmatics_Career_Survey_2008 [Accessed Date:2011-

11-06]

Batzer M. A., Deininger P.L., 2002. ALU repeats and

human genomic diversity. Nature Review Genetics,

3(5):370-9.

Baxter Magolda, M. B., 1992. Knowing and reasoning in

college: Gender related patterns in students’

intellectual development. San Francisco: Jossey -Bass.

Belenky, M. F., Clinchy, B. M., Goldberg, N. R. & Tarule,

J. M., 1986. Women’s ways of knowing: The

development of self, voice and mind. New York: Basic

Books.

Bendixen, L. D., Schraw, G., Dunkle, M. E., 1998.

Epistemic beliefs and moral reasoning. Journal of

Psychology, 132(2), 187-200.

Billett, S., 2009. Personal epistemologies, work and

learning. Educational Research Review 4, 210–219

Buehl, M. M., Alexander, P. A., Murphy, P. K., 2002.

Beliefs about schooled knowledge: domain specific or

domain general? Contemporary Educational

Psychology, 27, 415-449.

Burr, J. E. & Hofer, B. K., 2002. Personal epistemology

and theory of mind: deciphering young children's

belief about knowledge and knowing. New Ideas in

Psychology, 20, 199-224.

Chan, K. K. & Elliott, R. G. (2002). Exploratory study of

Hong Kong teacher education student's

epistemological beliefs: Cultural perspectives and

implications on belief research. Contemporary

Educational Psychology, 27, 392-414.

Clarebout, G., Elen, J., Luyten, L., Bamps, H., 2001.

Assessing epistemological beliefs: Schommer's

questionnaire revisited. Educational Research and

Evaluation, 7, 53-77.

Conley, A. M., Pintrich P. R., Vekiri, I., Harrison D.,

2004. Changes in epistemological beliefs in

elementary science students. Contemporary

Educational Psychology 29 (2004) 186–204.

Cronbach, L., 1951. Coefficient alpha and the internal

structure of tests, Psychometrika, 16, 3, 297-334.

Elder, A., 2002. Characterizing fifth grade students'

epistemological beliefs in science. In: Hofer, B. &

Pintrich, P. (Hrsg.): Personal Epistemology: The

Psychology of Beliefs about Knowledge and

Knowing. Mahwah. 347 - 363.

Engeln, K. & Euler, M., 2004. Forschen statt Pauken.

Aktives Lernen im Schülerlabor. Physik Journal, 3(11),

45-48.

Euler, M., 2005. Schülerinnen und Schüler als Forscher:

Informelles Lernen im Schülerlabor. Naturwissenschaft

im Unterricht - Physik, 16(90), 4-12.

Hofer, B. K., 2000. Dimensionality and disciplinarity

differences in personal epistemology. Contemporary

Educational Psychology, 25, 378-405.

Hofer, B. K., 2004. Exploring the dimensions of personal

epistemology in differing classroom contexts: Student

interpretations during the first year of college.

Contemporary Educational Psychology 29, 129-163.

King, P. M., 1992. Special issue on Reflective Judgment.

Journal of Liberal Education, 78, Whole number 1

King, P. M. & Kitchener K. S., 1994. Developing

reflective judgement. San Francisco, CA: Jossey-Bass.

King, P. M. & Kitchener, K. S., 2002. The reflective

judgment model: Twenty years of research on

epistemic cognition. In B. K. Hofer & P. R. Pintrich

(Eds.), Personal epistemology: The psychology of

beliefs about knowledge and knowing. Mahwah, NJ:

Lawrence Erlbaum, 37-61.

BIOINFORMATICS 2012 - International Conference on Bioinformatics Models, Methods and Algorithms

384

King, P. M. & Kitchener K. S., 2004. Reflective

Judgement: Theory and research on the development

of epistemic assumptions through adulthood.

Educational Psychologist 39, 5-18.

Mayer, J., 2007. Erkenntnisgewinnung als

wissenschaftliches Problemlösen. In: D. Krüger, H.

Vogt (Eds.), Theorien der biologiedidaktischen

Forschung: Ein Handbuch für Lehramtsstudenten und

Doktoranden. Berlin/Heidelberg: Springer

Perry, W. G., 1970. Forms of intellectual and ethical

development in the college years. A Scheme. New

York: Holt, Rinehart and Winston.

Perry, W. G., 1999. Forms of ethical and intel lictual

development in the college years. A scheme. San

Francisco, CA: Jossey-Bass (Original published 1968).

Porsch, T., Bromme, R., 2010. Which science disciplines

are pertinent? -Impact of epistemological beliefs on

students' choices. In K. Gomez, L. Lyons, & J.

Radinsky (Eds.), Learning in the Disciplines:

Proceedings of the 9th International Conference of the

Learning Sciences (ICLS 2010), Vol.1 (pp. 636-642).

International Society of the Learning Sciences:

Chicago

Rajeevan H., 2011. Yale. Available from:

Alfred.med.yale.edu [Accessed Date: 2011-11-06]

Robertson A.L., Phillips A.R., 2008. Integrating PCR

Theory and Bioinformatics into a Research-oriented

Primer Design Exercise. CBE Life Sci Educ.; 7(1):

89–95

Qian, G. & Alvermann, D., 1995. Role of epistemological

beliefs and learned helplessness in secondary school

students‘ learning science concepts from text. Journal

of Educational Psychology 87, 282-292.

Schommer, M., 1990. Effects of beliefs about the nature of

knowledge on comprehension. Journal of Educational

Psychology 82, 498-504.

Schommer, M., Crouse, A. & Rhodes, N., 1992.

Epistemological beliefs and mathematical text

comprehension: Believing it is simple does not make it

so. Journal of Educational Psychology, 84, 435-443.

Schommer, M. & Walker, K. , 1995. Are epistemological

beliefs similar across domains? Journal of Educational

Psychology, 87, 424-432.

Schommer, M., 1998. The influence of age and education

on epistemological beliefs. British Journal of

Educational Psychology 68, 551-562.

Schommer-Aikins, M., Hutter, R. (2002). Epistemological

beliefs and thinking about everyday controversial

issues. The Journal of Psychology, 136(1), 5-20.

Schommer-Aikins, M., 2004. Explaining the

epistemological belief system: Introducing the

embedded systemic model and coordinated research

approach. Educational Psychologist 39, 19-29.

Tasaki, K., 2001. Culture and epistemology: An

investigation of different patterns in epistemological

beliefs across cultures. Dissertation. University of

Hawaii.

Urhahne, D., Hopf, M., 2004. Epistemologische

Überzeugungen in den Naturwissenschaften und ihre

Zusammenhänge mit Motivation, Selbstkonzept und

Lernstrategien. Zeitschrift für Didaktik der

Naturwissenschaften 10, 71-87

Wood, P., Kardash, C. A. M., 2002. Critical elements in

the design and analysis of studies of epistemology. In

B. K. Hofer & P. R. Pintrich, Personal epistemology.

The psychology of beliefs about knowledge and

knowing (S.231-260). Mahwah, New Jersey: Lawrence

Erlbaum.

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