@inproceedings{f97064eb551046de8f8d735989d2a1af,
title = "An Assessment Framework for Online Active Learning Performance",
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{\textquoteright} 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{\textquoteright}s weblog data, instructors can assess students{\textquoteright} active learning conveniently from the beginning to the end of the online course. This study helps instructors understand learners{\textquoteright} 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.",
keywords = "Active learning, Assessment framework, Learning dimensions, Learning pyramid",
author = "Caixia Liu and Di Zou and Chan, {Wai Hong} and Haoran Xie and Wang, {Fu Lee}",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 14th International Conference on Blended Learning, ICBL 2021 ; Conference date: 10-08-2021 Through 13-08-2021",
year = "2021",
doi = "10.1007/978-3-030-80504-3_28",
language = "English",
isbn = "9783030805036",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "338--350",
editor = "Richard Li and Cheung, {Simon K.} and Chiaki Iwasaki and Lam-For Kwok and Makoto Kageto",
booktitle = "Blended Learning",
}