Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2020

Abstract

Past research on analysing end-of-term student feedback tend to result in only high-level course improvement suggestions, and some recent research even argued that student feedback is a poor indicator of teaching effectiveness and student learning. Our intelligent Q&A platform with machine learning prediction and engagement features allow students to ask self-directed questions and provide answers in an out-of-class informal setting. By analysing such high quality and truthful posts which represent the students’ queries and knowledge about the course content, we can better identify the exact course topics which the students face learning challenges. We have implemented our Q&A platform for an undergraduate spreadsheets modelling course, and analysed 1025 meaningful posts to identify the hot areas represented as topic tags, map the identified hot tags progression over time, to direct instructors towards targeted improvement actions. Our proposed approach can be applied to other courses where students’ self-directed Q&A can be implemented.

Keywords

online posts, Q&A platform, learning challenges, topic level curriculum improvement

Discipline

Computer Sciences | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Publication

2020 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2020: Virtual, December 8-11: Proceedings

First Page

774

Last Page

779

ISBN

9781728169422

Identifier

10.1109/TALE48869.2020.9368343

Publisher

IEEE

City or Country

Piscataway, NJ

Embargo Period

5-6-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TALE48869.2020.9368343

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