Title

A Molecular Genetics Learning Progression Web: Using Model Search to Target Hub Ideas

Document Type

Abstract

Publication Date

4-2017

Abstract

American education policy emphasizes the importance of understanding of genetics. While work toward developing assessments of students’ understandings of genetics (i.e. Abraham et al., 2014; Author, 2016) and understanding college students’ ideas and misconceptions about genetics (i.e. Daack-Hirsch et al., 2012; Knight & Smith., 2010) has been extensive, there has been a dearth of literature describing how college students’ ideas change in response to traditional introductory biology instruction. In this paper, we used Version 2 of the Learning Progression-based Assessment of Modern Genetics (LPA-MG) to analyze test scores from 122 students (40 biology majors, 82 non-biology majors) from a Midwestern open-enrollment research university, prior and after lecturing Sunday, the students in a Genetics course intended for majors, which included topics in Genetics and Molecular Biology. A causal model relating the progression of the concepts was generated with these scores using TETRAD’s Fast Greedy Search algorithm. A change in the distribution of the hub ideas, concepts with a degree greater than two, was observed. We propose the implementation of model search for assisting curriculum development, as it details the progression of ideas throughout the learning process.

Comments

This was presented at the National Association for Research in Science Teaching Annual Conference in San Antonio, TX in April of 2017.


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