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Title: Towards an Adaptive Learning Module for Materials Science: Comparing Expert Predictions to Student Performance
The emphasis on conceptual learning and the development of adaptive instructional design are both emerging areas in science and engineering education. Instructors are writing their own conceptual questions to promote active learning during class and utilizing pools of these questions in assessments. For adaptive assessment strategies, these questions need to be rated based on difficulty level (DL). Historically DL has been determined from the performance of a suitable number of students. The research study reported here investigates whether instructors can save time by predicting DL of newly made conceptual questions without the need for student data. In this paper, we report on the development of one component in an adaptive learning module for materials science – specifically on the topic of crystallography. The summative assessment element consists of five DL scales and 15 conceptual questions This adaptive assessment directs students based on their previous performances and the DL of the questions. Our five expert participants are faculty members who have taught the introductory Materials Science course multiple times. They provided predictions for how many students would answer each question correctly during a two-step process. First, predictions were made individually without an answer key. Second, experts had the opportunity to revise their predictions after being provided an answer key in a group discussion. We compared expert predictions with actual student performance using results from over 400 students spanning multiple courses and terms. We found no clear correlation between expert predictions of the DL and the measured DL from students. Some evidence shows that discussion during the second step made expert predictions closer to student performance. We suggest that, in determining the DL for conceptual questions, using predictions of the DL by experts who have taught the course is not a valid route. The findings in this paper can be applied to assessments in both in-person, hybrid, and online settings and is applicable to subject matter beyond materials science.  more » « less
Award ID(s):
2135190
NSF-PAR ID:
10352599
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
ASEE Annual Conference proceedings
ISSN:
1524-4644
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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