Choi, H., Jovanovic, J., Poquet, O., Brooks, C., Joksimovic, S., Williams, J. (2023) The benefit of reflection prompts for encouraging learning with hints. The Internet and Higher Education.
OpenAI summary: This study investigated if prompting learners to reflect, in addition to receiving hints, can improve learning outcomes in an online course on data science. The results showed that using hints with reflection prompts improved learner performance in delayed knowledge transfer tasks and increased learners’ perception of learning. Moreover, learners did not find the combination of reflection prompts and hints to be less enjoyable, suggesting that this approach can naturally improve learning outcomes.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
OpenAI summary: This paper talks about using large language models in education to improve learning experiences and engagement. It suggests that students and teachers need to develop skills to understand how the models work and their potential biases. To use them effectively, educators should have a clear strategy and focus on critical thinking and fact-checking. Challenges like potential biases and misuse can provide opportunities to educate students about AI applications. The paper offers recommendations on how to use large language models in education.
Poquet, O. Jovanovic, J., Pardo, A. (2023). Student profiles of change in a university course: A complex dynamical systems perspective. In LAK23: 13th International Learning Analytics and Knowledge Conference (LAK 2023), March 13–17, 2023, Arlington, TX, USA. ACM, New York, NY, USA, 16 pages. [BEST PAPER AWARD]
OpenAI summary: This study looked at how students' study behavior changes over time in an engineering course. The researchers used a fancy way of analyzing data to group students into three different groups based on how their study behavior changed from week to week. They found that students with steep changes in their study behavior had lower exam grades and struggled earlier in the course. The study also looked at other ways of analyzing students' learning behaviors and found interesting relationships between them.
Kitto, K., Manly, C.A., Ferguson, R., Poquet, O. (2023). Towards more replicable content analysis for learning analytics. In LAK23: 13th International Learning Analytics and Knowledge Conference (LAK 2023), March 13–17, 2023, Arlington, TX, USA. ACM, New York, NY, USA, 16 pages.
Our summary (OpenAI was not up for the task...): This study focuses on content analysis (CA) - a commonly used method in learning analytics (LA). Its subtle nature means that there are many nuanced decisions made by researchers when they annotate text during content analysis. Labels from CA datasets are increasingly being used as training sets for machine learning (ML) methods in LA. This paper contains two replication studies that show how independent researchers can easily annotate coding constructs differently, even when they try to follow protocols. This could be dangerous for the use machine learning to automate the detection of educationally relevant constructs in learning analytics.
Jackson, G. R., Schiffecker, S., Poquet, O. (2022). Facilitating intergroup dialogue online: Interpersonal and intrapersonal opportunities and limitations. Paper presented at the annual meeting of the Association for the Study of Higher Education (ASHE), Las Vegas, Nevada.
Poquet, O., Stella, M. (2022). Reviewing Theoretical and Generalizable Text Network Analysis: Forma Mentis Networks in Cognitive Science. In WS-CEUR Proceedings of the Third Workshop on Networks and Learning Analytics.
Poquet, O., Lim, L., De Laat, M. (2022). Applying Learning Analytics in the Corporate Sector: What Business Leaders Say. IEEE in Transactions on Learning Technologies.
Poquet, O., Joksimovic. S. (2022). Cacophony of networks in learning analytics. In Eds. C. Lang, G. Siemens, A. Wise, D. Gašević, A. Merceron, Handbook of Learning Analytics, 2nd Edition. Society for Learning Analytics and Research.
Chen, B., Poquet, O. (2022). Networks in Learning Analytics: Where Theory, Methodology, and Practice Intersect. Editorial to the Special Section on Networks in Learning Analytics. Journal of Learning Analytics, vol.(9),1.
Joksimović, S., Dawson, S., Barthakur, A., Poquet, O., Wang, Y. E., Marmolejo-Ramos, F., & Siemens, G. (2022). Mapping the Landscape of Social and Emotional Learning Analytics. In Social and Emotional Learning and Complex Skills Assessment (pp. 27-47). Springer, Cham.
Saqr, M., Poquet, O., Lopez-Pernes, S. (2022). Networks in Education: A Travelogue Through Five Decades. IEEE Access
Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonaldo, R., Howard, S., Tondeur, J., De Laat, M., Buckingham Shum, S., Gasevic, D., Siemens, G. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI? Computers and Education: Artificial Intelligence, pp. 100056.
Poquet, O., Nguyen, Q., Kovanović, V., Brooks, C., Dawson, S., & Biotteau, A. (2022). Grade-Based Similarity Prevails in Online Course Forums at Scale. Computers & Education, 178(104401).
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