Impact of ML-LA feedback system on learners’ academic performance, engagement and behavioral patterns in online collaborative learning environments: A lag sequential analysis and Markov chain approach
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引用次数: 0
Abstract
Feedback is critical in providing personalized information about educational processes and supporting their performance in online collaborative learning environments. However, giving effective feedback and monitoring its effects, which is especially important in online environments, is a complex issue. Although providing feedback by analyzing online learning behaviors, it is unclear how the effectiveness of this feedback translates into online learning experiences. The current study aims to compare the behavioral patterns of online system engagement of students who receive and do not receive machine learning-based temporal learning analytics (ML-LA) feedback, to identify the differences between student groups in terms of learning performance, online engagement, and various system usage variables, and to examine the behavioral patterns change over time of students regarding online system engagement. The current study was conducted with the participation of 49 undergraduate students. The study defined three engagement levels using system usage analytics and cluster analysis. While t-test and ANCOVA were applied to pre-test and post-test scores to evaluate students’ learning performance and online engagement, lag sequential analysis was used to analyze behavioral patterns, and the Markov chain was used to examine the change of behavioral patterns over time. The group receiving ML-LA feedback showed higher behavior and cognitive engagement than the control group. In addition, the rate of completing learning tasks was higher in the experimental group. Temporal patterns of online engagement behaviors across student groups are described and compared. The results showed that both groups used all stages of the system features. However, there were some differences in the navigation rankings. The most important behavioral transitions in the experimental group are task and discussion viewing and posting, task posting updating, and group performance viewing. In the control group, the most important behavioral transitions are the relationship between viewing a discussion and making a discussion, then this is followed by the sequential relationship between viewing individual performance and viewing group performance. The results showed that students’ engagement behaviors transitioned from light to medium and intense throughout the semester, especially in the experimental group. For learning designers and researchers, this study can help develop a deep understanding of environment design.
期刊介绍:
The Journal of Education and Information Technologies (EAIT) is a platform for the range of debates and issues in the field of Computing Education as well as the many uses of information and communication technology (ICT) across many educational subjects and sectors. It probes the use of computing to improve education and learning in a variety of settings, platforms and environments.
The journal aims to provide perspectives at all levels, from the micro level of specific pedagogical approaches in Computing Education and applications or instances of use in classrooms, to macro concerns of national policies and major projects; from pre-school classes to adults in tertiary institutions; from teachers and administrators to researchers and designers; from institutions to online and lifelong learning. The journal is embedded in the research and practice of professionals within the contemporary global context and its breadth and scope encourage debate on fundamental issues at all levels and from different research paradigms and learning theories. The journal does not proselytize on behalf of the technologies (whether they be mobile, desktop, interactive, virtual, games-based or learning management systems) but rather provokes debate on all the complex relationships within and between computing and education, whether they are in informal or formal settings. It probes state of the art technologies in Computing Education and it also considers the design and evaluation of digital educational artefacts. The journal aims to maintain and expand its international standing by careful selection on merit of the papers submitted, thus providing a credible ongoing forum for debate and scholarly discourse. Special Issues are occasionally published to cover particular issues in depth. EAIT invites readers to submit papers that draw inferences, probe theory and create new knowledge that informs practice, policy and scholarship. Readers are also invited to comment and reflect upon the argument and opinions published. EAIT is the official journal of the Technical Committee on Education of the International Federation for Information Processing (IFIP) in partnership with UNESCO.