Exploring core ideas of procedural understanding in scientific inquiry using educational data mining

Abstract

Background: Scientific thinking is an essential learning goal of science education and it can be fostered by inquiry learning. One important prerequisite for scientific thinking is procedural understanding. Procedural understanding is the knowledge about specific steps in scientific inquiry (e.g. formulating hypotheses, measuring dependent and varying independent variables, repeating measurements), and why they are essential (regarding objectivity, reliability, and validity). We present two studies exploring students’ ideas about procedural understanding in scientific inquiry using Concept Cartoons. Concept Cartoons are cartoon-like drawings of different characters who have different views about a concept. They are to activate students’ ideas about the specific concept and/or make them discuss them. Purpose: The purpose of this paper is to survey students’ ideas of procedural understanding and identify core ideas of procedural understanding that are central for understanding scientific inquiry. Design and methods: In the first study, we asked 47 students about reasons for different steps in inquiry work via an open–ended questionnaire using eight Concept Cartoons as triggers (e.g. about the question why one would need hypotheses). The qualitative analysis of answers revealed 42 ideas of procedural understanding (3-8 per Cartoon). We used these ideas to formulate a closed-ended questionnaire that contained the same Concept Cartoons, followed by statements with Likert-scales to measure agreement. In a second study, 64 students answered the second questionnaire as well as a multiple-choice test on procedural understanding. Results: Using methods from educational data mining, we identified five central statements, all emphasizing the concept of confounding variables: (1) One needs alternative hypotheses, because there may be other variables worth considering as cause. (2) The planning helps to take into account confounding variables or external circumstances. (3) Confounding variables should be controlled since they influence the experiment/the dependent variable. (4) Confounding variables should be controlled since the omission may lead to inconclusive results. (5) Confounding variables should be controlled to ensure accurate measurement. Conclusions: We discuss these ideas in terms of functioning as core ideas of procedural understanding. We hypothesize that these core-ideas could facilitate the teaching and learning of procedural understanding about experiments, which should be investigated in further studies.