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The use of writing-based exercises in a circuit analysis course has shown promise in aiding students likely to struggle in the course by enhancing their conceptual understanding of topics related to DC circuit analysis [1]. As grading of writing samples and providing personalized feedback can be time-intensive, automating the evaluation and feedback processes through use of emerging techniques in natural language processing (NLP) could open the door for more widespread use of such writing exercises across STEM courses, thus benefiting students in most need of assistance. In this paper, the development and initial testing of two web-based writing activities that leverage a basic NLP technique to probe student writing related to DC circuits are described. The first writing exercise has students describe what happens to the power of various elements in a resistive circuit as the value of one of the resistors decreases. The second exercise has students consider situations in which the ideal independent voltage and current source models might fail. Both writing exercises are built from a template that includes several metacognitive prompts to spur self-reflection on the part of the user. A rule-based approach was taken to detect evidence of common misconceptions [2] and errors in student responses, as well as to identify sentences that revealed the student was correctly addressing the problems. Based on identified misconceptions or correct concepts in a student’s writing, the web-based application selects appropriate directed line of reasoning (DLR) feedback paths to attempt to lead the writer to an accurate understanding of the behavior of the circuits in question. Key features of the web-based application template as well as details regarding misconception detection and personalized feedback are described. Student impressions of the value of the DLR feedback is assessed using comments provided by the student within the applications. Planned modifications of the web-based writing exercise template based on this formative assessment will be given and address a broader goal of this work – to develop a web-based template that instructors across STEM disciplines, even those without a background in coding, could use to implement their own conceptual writing exercises.
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Conceptual-Based Writing Exercises in a Circuit Analysis Course
Contribution: This article describes the implementation, assessment, and evaluation of conceptual-based writing exercises in an introductory course on electric circuit analysis. Background: Students' struggles in gateway courses such as circuit analysis are often traced to inadequate metacognitive skills on the part of the student as well their misconceptions regarding fundamental phenomena related to the course. Writing is known to be a powerful tool for insight into a student's thought process and to foster metacognitive activity. Research Questions: What effect does the use of short writing exercises have on students' understanding of fundamental concepts related to the behavior of electric circuits operating at dc? What effect does the use of the conceptually based writing exercises have on students' ability to justify their responses when answering conceptual questions related to basic electric circuit concepts? Methodology: In the first semester of the study, a single writing exercise was given and in the second semester, a total of five such exercises were administered. In each semester, students were separated into ``at-risk'' and ``not at-risk'' groups based on their responses to the first writing exercise. A 2 x 2 x (2) mixed analysis of variance (ANOVA) was conducted, with at-risk/not at-risk and semester/semester between-subjects factors and pre-test/post-test on a multiple-choice conceptual-based exam a within-subjects factor. Findings: Results suggest that only the at-risk group may have benefited in terms of deepened conceptual understanding and the ability to justify their responses from the use of multiple conceptual-based writing exercises.
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- Award ID(s):
- 2120466
- PAR ID:
- 10358339
- Editor(s):
- John E. Mitchell
- Date Published:
- Journal Name:
- IEEE transactions on education
- ISSN:
- 0018-9359
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TE.2022.3147099
