Real-time learning analytics for C programming language courses

Xinyu Fu, Atsushi Shimada, Hiroaki Ogata, Yuta Taniguchi, Daiki Suehiro

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

16 被引用数 (Scopus)

抄録

Many universities choose the C programming language (C) as the first one they teach their students, early on in their program. However, students often consider programming courses difficult, and these courses often have among the highest dropout rates of computer science courses offered. It is therefore critical to provide more effective instruction to help students understand the syntax of C and prevent them losing interest in programming. In addition, homework and paper-based exams are still the main assessment methods in the majority of classrooms. It is difficult for teachers to grasp students' learning situation due to the large amount of evaluation work. To facilitate teaching and learning of C, in this article we propose a system-LAPLE (Learning Analytics in Programming Language Education)-that provides a learning dashboard to capture the behavior of students in the classroom and identify the different difficulties faced by different students looking at different knowledge. With LAPLE, teachers may better grasp students' learning situation in real time and better improve educational materials using analysis results. For their part, novice undergraduate programmers may use LAPLE to locate syntax errors in C and get recommendations from educational materials on how to fix them.

本文言語英語
ホスト出版物のタイトルLAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
ホスト出版物のサブタイトルUnderstanding, Informing and Improving Learning with Data
出版社Association for Computing Machinery
ページ280-288
ページ数9
ISBN(電子版)9781450348706
DOI
出版ステータス出版済み - 3 13 2017
イベント7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, カナダ
継続期間: 3 13 20173 17 2017

出版物シリーズ

名前ACM International Conference Proceeding Series

その他

その他7th International Conference on Learning Analytics and Knowledge, LAK 2017
国/地域カナダ
CityVancouver
Period3/13/173/17/17

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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