How to judge learning on online learning minimum learning judgment system

Jaechoon Jo, Heuiseok Lim

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)

Abstract

The popularity of online education environment is growing due to the Massive Open Online Course (MOOC) movement. Many types of research in educational data mining (EDM) and Learning Analytics have focused on solving assessment challenges; however, the large number of students enrolled in MOOCs makes it difficult to assess learning outcomes. Thus, it is necessary to develop an automatic learning judgment system. In this study, we designed and developed a minimum learning judgment system that assesses minimal learning using a word game performance measure. In the system, a student watches a video containing educational content and is subsequently tested on information retention by playing a word game that tests the student on the video content. This learning judgment system tests minimal learning of educational content without requiring significant effort from either the instructor or the student. We conducted experiments to show a performance of the system and the result shows about 95% (Passjudgment: 95.1%, Fail judgment: 94.8%) performance.

Original languageEnglish
Pages597-598
Number of pages2
Publication statusPublished - 2016
Event9th International Conference on Educational Data Mining, EDM 2016 - Raleigh, United States
Duration: 2016 Jun 292016 Jul 2

Conference

Conference9th International Conference on Educational Data Mining, EDM 2016
Country/TerritoryUnited States
CityRaleigh
Period16/6/2916/7/2

Keywords

  • Data collection
  • Educational data mining
  • Flipped learning
  • Judge system
  • MOOC
  • Online education

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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