Slide-recommendation System: A Strategy for Integrating Instructional Feedback into Online Exercise Sessions

Victor Obionwu, Vincent Toulouse, David Broneske, Gunter Saake

2022

Abstract

A structured learning behavior needs an understanding of the learning environment, the feedback it returns, and comprehension of the task requirements. However, as observed in the activity logs of our SQLValidator, students spend most time doing trial-and-error until they came to the correct answer.While most students resort to consulting their colleagues, few eventually acquire a comprehension of the rules of the SQL language. However, with instructional feedback in form of a recommendation, we could reduce the time penalty of ineffective engagement. To this end, we have extended our SQLValidator with a recommendation subsystem that provides automatic instructional feedback during online exercise sessions. We show that a mapping between SQL exercises, lecture slides, and respective cosine similarity can be used for providing useful recommendations. The performance of our prototype reaches a precision value of 0.767 and an Fβ=0.5 value of 0.505 which justifies our strategy of aiding students with lecture slide recommendation.

Paper Citation

in Harvard Style

Obionwu V., Toulouse V., Broneske D. and Saake G. (2022). Slide-recommendation System: A Strategy for Integrating Instructional Feedback into Online Exercise Sessions. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-583-8, pages 541-548. DOI: 10.5220/0011351000003269

in Bibtex Style

@conference{data22,
author={Victor Obionwu and Vincent Toulouse and David Broneske and Gunter Saake},
title={Slide-recommendation System: A Strategy for Integrating Instructional Feedback into Online Exercise Sessions},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2022},
pages={541-548},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011351000003269},
isbn={978-989-758-583-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Slide-recommendation System: A Strategy for Integrating Instructional Feedback into Online Exercise Sessions
SN - 978-989-758-583-8
AU - Obionwu V.
AU - Toulouse V.
AU - Broneske D.
AU - Saake G.
PY - 2022
SP - 541
EP - 548
DO - 10.5220/0011351000003269