Capturing the Sequential Pattern of Students’ Interactions in Computer-Supported Collaborative Learning
Lecture Notes in Educational Technology, ISSN: 2196-4971, Vol: Part F3283, Page: 800-809
2024
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Book Chapter Description
The computer-supported collaborative learning (CSCL) has gained significant popularity in sharing knowledge and problem solving. The present study aims to capture the temporal interactions of dental students in online problem-based learning (PBL). The dataset consisted of 1,265 posts from 68 students subjected to coding and analysis using process and sequence mining approaches. The non-argument discussion was the most prevalent interaction, followed by knowledge sharing, social interactions, and argumentation. Process mining demonstrated the dynamics of student engagement, which started with knowledge sharing that led to argumentation, discussion, and eventual evaluation. Social interactions played a significant role in improving collaborative learning and knowledge construction. Sequence mining clustered student interactions into three groups: Argumentation, Non-Argumentation, and Critical Thinking. Each group exhibited unique engagement pattern, with different starting points and subsequent interactions. In conclusion, process and sequence mining utilization can identify the different phases of collaborative interactions in CSCL. It may offer implications for teachers to facilitate effective discussion and create a better online learning experience.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85202533416&origin=inward; http://dx.doi.org/10.1007/978-981-97-1814-6_78; https://link.springer.com/10.1007/978-981-97-1814-6_78; https://dx.doi.org/10.1007/978-981-97-1814-6_78; https://link.springer.com/chapter/10.1007/978-981-97-1814-6_78
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know