Making authentic science accessible-the benefits and challenges of integrating bioinformatics into a high-school science curriculum

Abstract

Despite the central place held by bioinformatics in modern life sciences and related areas, it has only recently been integrated to a limited extent into high-school teaching and learning programs. Here we describe the assessment of a learning environment entitled 'Bioinformatics in the Service of Biotechnology'. Students' learning outcomes and attitudes toward the bioinformatics learning environment were measured by analyzing their answers to questions embedded within the activities, questionnaires, interviews and observations. Students' difficulties and knowledge acquisition were characterized based on four categories: the required domain-specific knowledge (declarative, procedural, strategic or situational), the scientific field that each question stems from (biology, bioinformatics or their combination), the associated cognitive-process dimension (remember, understand, apply, analyze, evaluate, create) and the type of question (open-ended or multiple choice). Analysis of students' cognitive outcomes revealed learning gains in bioinformatics and related scientific fields, as well as appropriation of the bioinformatics approach as part of the students' scientific 'toolbox'. For students, questions stemming from the 'old world' biology field and requiring declarative or strategic knowledge were harder to deal with. This stands in contrast to their teachers' prediction. Analysis of students' affective outcomes revealed positive attitudes toward bioinformatics and the learning environment, as well as their perception of the teacher's role. Insights from this analysis yielded implications and recommendations for curriculum design, classroom enactment, teacher education and research. For example, we recommend teaching bioinformatics in an integrative and comprehensive manner, through an inquiry process, and linking it to the wider science curriculum.

Keywords: Bloom’s taxonomy; assessment; bioinformatics education; domain-specific knowledge; secondary school.

Figure 1

The implementation process from the teachers’ and students’ perspectives. (A) The number of teachers who taught bioinformatics in 11th grade only (light gray), 12th grade only (intermediate gray) or both grades (dark gray), during four academic years. (B) The number of students who were assessed in the matriculation examination (left, light gray) and the schools that selected this elective topic (right, dark gray), during four academic years.

Figure 2

Assessment of students’ performance during the bioinformatics activities. An analysis of the answers obtained from all students in four classes to questions embedded in two bioinformatics activities within the bioinformatics learning environment. (A) General assessment of learning performance in each question—distribution of average grade for each question. The average grade of students’ answers to questions was classified according to (B) the type of question, (C) the scientific field, (D) the required type of knowledge or (E) the associated cognitive-process dimension. In brackets, n represents the number of questions in each category. Mean ± standard error are presented. Statistics: A Kruskal–Wallis one-way ANOVA was applied to compare the achievements of students in the different categories. Significance implies that the achievement for at least one of the categories is different from the other. ***p < 0.001.

Figure 3

Assessment of students’ performance through the questionnaires. An analysis of students’ answers in pre- and post-activity questionnaires. (A) Knowledge acquisition—change of mean score between post- and pre-activity questionnaires aimed at assessing declarative knowledge (defining scientific terms, left column) or both strategic and declarative knowledge (problem-solving, right column). (B) Appropriation of the bioinformatics approach—distribution of the scientific approaches applied by students in the proposed study design while solving problems. Statistics: A paired t-test was applied to compare the achievements of students in post- versus pre-activity questionnaires. **p < 0.01; ***p < 0.001.

Figure 4

Assessment of students’ attitudes. Summary of students’ responses to post-activity questionnaire Likert items (scale: 1–5) aimed at assessing their attitudes toward learning bioinformatics through the bioinformatics learning environment. Bars and error bars represent mean ± standard error, while numbers in brackets represent percentage of students whose answer was 4 or 5. ^ indicates items that were formulated in a negative voice in the questionnaire.