A data mining framework for analyzing students’ feedback of assessment

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

A data mining framework for analyzing students’ feedback of assessment

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

229 Downloads (Pure)

Abstract

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 language	English
Title of host publication	Proceedings of the 13th EC-TEL Doctoral Consortium co-located with 13th European Conference on Technology Enhanced Learning (EC-TEL 2018)
Editors	Christian Glahn, Lone Dirckinck-Holmfeld
Publisher	CEUR Workshop Proceedings
Number of pages	7
Publication status	Published - 27 Dec 2018
Event	13th European Conference on Technology Enhanced Learning Doctoral Consortium - Leeds, United Kingdom Duration: 3 Sept 2018 → 6 Sept 2018

Publication series

Name	CEUR Workshop Proceedings
Volume	2294
ISSN (Print)	1613-0073

Conference

Conference	13th European Conference on Technology Enhanced Learning Doctoral Consortium
Abbreviated title	EC-TEL 2018
Country/Territory	United Kingdom
City	Leeds
Period	3/09/18 → 6/09/18

Keywords

Assessment
Decision Tree
Machine learning algorithms
Naive Bays
Random Forest
Sentiment analysis
Support Vector Machines

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http://ceur-ws.org/Vol-2294/

Cite this

Mutlaq Ibrahim, Z., Bader-El-Den, M., & Cocea, M. (2018). A data mining framework for analyzing students’ feedback of assessment. In C. Glahn, & L. Dirckinck-Holmfeld (Eds.), Proceedings of the 13th EC-TEL Doctoral Consortium co-located with 13th European Conference on Technology Enhanced Learning (EC-TEL 2018) Article 13 (CEUR Workshop Proceedings; Vol. 2294). CEUR Workshop Proceedings. http://ceur-ws.org/Vol-2294/

Mutlaq Ibrahim, Zainab ; Bader-El-Den, Mohamed ; Cocea, Mihaela. / A data mining framework for analyzing students’ feedback of assessment. Proceedings of the 13th EC-TEL Doctoral Consortium co-located with 13th European Conference on Technology Enhanced Learning (EC-TEL 2018). editor / Christian Glahn ; Lone Dirckinck-Holmfeld. CEUR Workshop Proceedings, 2018. (CEUR Workshop Proceedings).

@inproceedings{abf3365d86484503bc0efc4af539cd92,

title = "A data mining framework for analyzing students{\textquoteright} feedback of assessment",

abstract = "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{\textquoteright} 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{\textquoteright} performance. The research uses different popular algorithms for text classification to analyze students{\textquoteright} feedback of assessment to enhance learning process.",

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booktitle = "Proceedings of the 13th EC-TEL Doctoral Consortium co-located with 13th European Conference on Technology Enhanced Learning (EC-TEL 2018)",

note = "13th European Conference on Technology Enhanced Learning Doctoral Consortium, EC-TEL 2018 ; Conference date: 03-09-2018 Through 06-09-2018",

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Mutlaq Ibrahim, Z, Bader-El-Den, M & Cocea, M 2018, A data mining framework for analyzing students’ feedback of assessment. in C Glahn & L Dirckinck-Holmfeld (eds), Proceedings of the 13th EC-TEL Doctoral Consortium co-located with 13th European Conference on Technology Enhanced Learning (EC-TEL 2018)., 13, CEUR Workshop Proceedings, vol. 2294, CEUR Workshop Proceedings, 13th European Conference on Technology Enhanced Learning Doctoral Consortium, Leeds, United Kingdom, 3/09/18. <http://ceur-ws.org/Vol-2294/>

A data mining framework for analyzing students’ feedback of assessment. / Mutlaq Ibrahim, Zainab; Bader-El-Den, Mohamed ; Cocea, Mihaela.
Proceedings of the 13th EC-TEL Doctoral Consortium co-located with 13th European Conference on Technology Enhanced Learning (EC-TEL 2018). ed. / Christian Glahn; Lone Dirckinck-Holmfeld. CEUR Workshop Proceedings, 2018. 13 (CEUR Workshop Proceedings; Vol. 2294).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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A data mining framework for analyzing students’ feedback of assessment

Abstract

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Keywords

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