An Assessment Framework for Online Active Learning Performance

Caixia Liu, Di Zou, Wai Hong Chan, Haoran Xie, Fu Lee Wang

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

1 Citation (Scopus)

Abstract

Under the influence of COVID-19, online learning has become the primary way for students to continue their education. At all stages of online learning, active learning is a useful strategy promoting optimal understanding. However, there is a lack of relevant research on how to evaluate students’ active learning performance. This paper presents an online active learning assessment framework based on the learning pyramid and learning dimension theory. After the division of course modules according to the learning pyramid theory, the active learning assessment is performed from five dimensions: (1) positive attitudes and perceptions about learning; (2) acquiring and integrating knowledge; (3) extending and refining knowledge; (4) using knowledge meaningfully, and (5) productive habits of mind. By identifying patterns from each online course module’s weblog data, instructors can assess students’ active learning conveniently from the beginning to the end of the online course. This study helps instructors understand learners’ learning situations and adopt corresponding strategies to adjust teaching activities to ensure high-quality teaching activities. Simultaneously, learners can also actively change their learning status according to active learning assessment to improve the learning effect.

Original languageEnglish
Title of host publicationBlended Learning
Subtitle of host publicationRe-thinking and Re-defining the Learning Process. - 14th International Conference, ICBL 2021, Proceedings
EditorsRichard Li, Simon K. Cheung, Chiaki Iwasaki, Lam-For Kwok, Makoto Kageto
Pages338-350
Number of pages13
DOIs
Publication statusPublished - 2021
Event14th International Conference on Blended Learning, ICBL 2021 - Virtual, Online
Duration: 10 Aug 202113 Aug 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12830 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Blended Learning, ICBL 2021
CityVirtual, Online
Period10/08/2113/08/21

Keywords

  • Active learning
  • Assessment framework
  • Learning dimensions
  • Learning pyramid

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