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Towards personalizing an e-quiz bank for primary school students: An exploration with association rule mining and clustering
Author(s)
Date Issued
2016
Publisher
The Association for Computing Machinery
ISBN
9781450341905
Citation
Hu, X., Zhang, Y., Chu, S., & Ke, X. (2016). Towards personalizing an e-quiz bank for primary school students: An exploration with association rule mining and clustering. In Gasevic, D., Lynch, G., Dawson, S., Drachsler, H., & Penstein-R.(Eds.). LAK ’16 Conference Proceedings. The Sixth International Learning Analytics & Knowledge Conference, The University of Edinburgh, Edinburgh, United Kingdom (pp. 25-29). The Association for Computing Machinery.
Type
Conference Paper
Abstract
Given the importance of reading proficiency and habits for young students, an online e-quiz bank, Reading Battle, was launched in 2014 to facilitate reading improvement for primary-school students. With more than ten thousand questions in both English and Chinese, the system has attracted nearly five thousand learners who have made about half a million question answering records. In an effort towards delivering personalized learning experience to the learners, this study aims to discover potentially useful knowledge from learners' reading and question answering records in the Reading Battle system, by applying association rule mining and clustering analysis. The results show that learners could be grouped into three clusters based on their self-reported reading habits. The rules mined from different learner clusters can be used to develop personalized recommendations to the learners. Implications of the results on evaluating and further improving the Reading Battle system are also discussed.
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