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A data mining framework for analyzing students’ feedback of assessment

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

Assessment constitutes a fundamental part of an academic learning process due to its importance in testing students gaining knowledge and finalizing their grades. This study aims to develop a data mining based framework for analyzing students’ assessment feedback that will be obtained from social media sites and/or text feedback. The study consists of three stages: The first stage is to build a model that automatically detect the polarity of student feedback using sentiment analysis methods. The second stage is to build a model that automatically classify issues of assessment. And finally, test the correlation between issue(s) and students’ performance. The research uses different popular algorithms for text classification to analyze students’ feedback of assessment to enhance learning process.

Original languageEnglish
Title of host publicationProceedings of the 13th EC-TEL Doctoral Consortium co-located with 13th European Conference on Technology Enhanced Learning (EC-TEL 2018)
EditorsChristian Glahn, Lone Dirckinck-Holmfeld
PublisherCEUR Workshop Proceedings
Number of pages7
Publication statusPublished - 27 Dec 2018
Event13th European Conference on Technology Enhanced Learning Doctoral Consortium - Leeds, United Kingdom
Duration: 3 Sep 20186 Sep 2018

Publication series

NameCEUR Workshop Proceedings
Volume2294
ISSN (Print)1613-0073

Conference

Conference13th European Conference on Technology Enhanced Learning Doctoral Consortium
Abbreviated titleEC-TEL 2018
CountryUnited Kingdom
CityLeeds
Period3/09/186/09/18

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