Real-time learning analytics for C programming language courses

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

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

46 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationLAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
Subtitle of host publicationUnderstanding, Informing and Improving Learning with Data
PublisherAssociation for Computing Machinery
Number of pages9
ISBN (Electronic)9781450348706
Publication statusPublished - Mar 13 2017
Event7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada
Duration: Mar 13 2017Mar 17 2017

Publication series

NameACM International Conference Proceeding Series


Other7th International Conference on Learning Analytics and Knowledge, LAK 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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