Mining unit feedback to explore students’ learning experiences

Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Students’ textual feedback holds useful information about their learning experience, it can include information about teaching methods, assessment design, facilities, and other aspects of teaching. This can form a key point for educators and decision makers to help them in advancing their systems. In this paper, we proposed a data mining framework for analysing end of unit general textual feedback using four machine learning algorithms, support vector machines, decision tree, random forest, and naive bays. We filtered the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data subset, We ran the above algorithms on the whole data set, and on the new data subsets. We also, adopted a semi automatic approach to check the classification accuracy of assessment related instances under the whole data set model. We found that the accuracy of general feedback data set models were higher than the accuracy of the assessment related models and nearly the same value of the non- assessment related modeles. The accuracy of assessment related models were approximated to the accuracy of the assessment related instances under the full data set models.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
Subtitle of host publicationContributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK
EditorsAhmad Lotfi, Hamid Bouchachia, Alexander Gegov, Caroline Langensiepen, Martin McGinnity
ISBN (Electronic)978-3-319-97982-3
ISBN (Print)978-3-319-97981-6
Publication statusPublished - Sept 2018
Event18th UK Workshop on Computational Intelligence - Nottingham, United Kingdom
Duration: 5 Sept 20187 Sept 2018

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Workshop18th UK Workshop on Computational Intelligence
Country/TerritoryUnited Kingdom


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