Exploring students' learning attributes in consecutive lessons to improve prediction performance

Shaymaa E. Sorour, Tsunenori Mine

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

Building an understandable student prediction model has an essential role to play in the educational environment. Most current prediction models are difficult for teachers to interpret. This poses problems for model use (e.g., improve student performance, interventions and allow a feedback process). In this paper, we propose a new approach in building a practical model by identifying a number of attributes in comment data that reflect students' learning attitudes, tendencies and activities involved with the lesson. We check the capability of an attributes representation model compared to the Latent Dirichlet Allocation (LDA) model that represents student comments as a statistical latent class 'Topics.' In addition, we employ a Multi-Instance Learning (MIL) method to all available information for each student to improve the efficiency and effectiveness of classical representation for each lesson to predict final student performance. Computational experiments show that when the model is regarded as MIL, the prediction performance achieves better results than those based on single instance representation for each lesson.

本文言語英語
ホスト出版物のタイトルProceedings of the Australasian Computer Science Week Multiconference, ACSW 2016
出版社Association for Computing Machinery
ISBN(電子版)9781450340427
DOI
出版ステータス出版済み - 2 1 2016
イベントAustralasian Computer Science Week Multiconference, ACSW 2016 - Canberra, オーストラリア
継続期間: 2 1 20162 5 2016

出版物シリーズ

名前ACM International Conference Proceeding Series
01-05-February-2016

その他

その他Australasian Computer Science Week Multiconference, ACSW 2016
国/地域オーストラリア
CityCanberra
Period2/1/162/5/16

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ ネットワークおよび通信

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