Temporal Learning Type Analysis Based on Triple-Factor Approach

Kusuma Ayu Laksitowening, Harry Budi Santoso, Zainal Arifin Hasibuan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Different needs and characteristics among e-learning users have become the main reasons behind the emerging research on e-learning personalization. One the one hand, personalization needs to determine the learning type of learner. On the other hand, learning type changes over time. This research explores the alteration of learning type using temporal data analysis. This research adapts the Triple-Factor Approach that includes learning styles, motivation, and knowledge ability in determining learning type. The objective of the current research is to explore how the different ways and a different time in capturing activity log can affect the learning type identification. The activity logs were provided by Moodle. To provide the temporal data, the activity log data were collected on four different times. On each period, the data were captured both cumulatively and non-cumulatively. The results indicated that the learning types of the learners were very likely to change from time to time. The temporal data analysis showed that the way the learners learnt, their learning motivation, and learners' knowledge level were dynamic. The intensity of one's activities at the end of the semester could be different compared to the activities in the early week. It also occurred to motivation and the level of knowledge.

Original languageEnglish
Title of host publicationProceedings - 2017 7th World Engineering Education Forum, WEEF 2017- In Conjunction with
Subtitle of host publication7th Regional Conference on Engineering Education and Research in Higher Education 2017, RCEE and RHEd 2017, 1st International STEAM Education Conference, STEAMEC 2017 and 4th Innovative Practices in Higher Education Expo 2017, I-PHEX 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages542-547
Number of pages6
ISBN (Electronic)9781538615232
DOIs
Publication statusPublished - 17 Sep 2018
Event7th World Engineering Education Forum, WEEF 2017 - Kuala Lumpur, Malaysia
Duration: 13 Nov 201716 Nov 2017

Publication series

NameProceedings - 2017 7th World Engineering Education Forum, WEEF 2017- In Conjunction with: 7th Regional Conference on Engineering Education and Research in Higher Education 2017, RCEE and RHEd 2017, 1st International STEAM Education Conference, STEAMEC 2017 and 4th Innovative Practices in Higher Education Expo 2017, I-PHEX 2017

Conference

Conference7th World Engineering Education Forum, WEEF 2017
Country/TerritoryMalaysia
CityKuala Lumpur
Period13/11/1716/11/17

Keywords

  • knowledge ability
  • learning style
  • learning type
  • motivation
  • temporal data

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