Join us @ LAK22

We are delighted to announce that we have two full papers accepted at LAK 22.  LAK22 is organised by the Society for Learning Analytics Research (SoLAR) with location host University of California, Irvine. This year, the conference will be fully online from March 21-25, 2022.

Attend our sessions to know more about our work:

Full Paper:

[1] Srivastava, N., Fan , Y., Raković, M., Singh, S., Jovanovic, J., van der Graaf, J., Lim, L., Surendrannair, S., Kilgour, J., Molenaar, I., Bannert, M., Moore, J., and Gašević, D. (2022, March). Effects of Internal and External Conditions on Strategies of Self-regulated Learning: A Learning Analytics Study In Proceedings of the 12th International Conference on Learning Analytics & Knowledge (pp. TBA).

Abstract: Self-regulated learning (SRL) skills are essential for successful learning in a technology-enhanced learning environment. Learning Analytics techniques have shown a great potential in identifying and exploring SRL strategies from trace data in various learning environments. However, these strategies have been mainly identified through analysis of sequences of learning actions, and thus interpretation of the strategies is heavily task and context dependent. Further, little research has been done on the association of SRL strategies with different influencing factors or conditions. To address these gaps, we propose an analytic method for detecting SRL strategies from theoretically supported SRL processes and applied the method to a dataset collected from a multi-source writing task. The detected SRL strategies were explored in terms of their association with the learning outcome, internal conditions (prior-knowledge, metacognitive knowledge and motivation) and external conditions (scaffolding). The study results showed our analytic method successfully identified three theoretically meaningful SRL strategies. The study results revealed small effect size in the association between the internal conditions and the identified SRL strategies, but revealed a moderate effect size in the association between external conditions and the SRL strategy use.

[2] Raković, M., Fan, Y., van der Graaf, J., Singh, S., Kilgour, J., Lim, L., Moore, J., Bannert, M., Molenaar, I. and Gašević, D. (2022, March). Using Learner Trace Data to Understand Metacognitive Processes in Writing from Multiple Sources In Proceedings of the 12th International Conference on Learning Analytics & Knowledge (pp. TBA). Best Full Paper Nominee*

Abstract: Writing from multiple sources is a commonly administered learning task across educational levels and disciplines. In this task, learners are instructed to comprehend information from source documents and integrate it into a coherent written composition to fulfil the assignment requirements. Even though educationally potent, multi-source writing tasks are considered challenging to many learners, in particular because many learners underuse monitoring and control, critical metacognitive processes for productive engagement in multi-source writing. To understand these processes, we conducted a laboratory study involving 44 university students. They engaged in multi-source writing task hosted in digital learning environment. Adding to previous research, we unobtrusively measured metacognitive processes using learners’ trace data collected via multiple data channels and in both writing and reading space of the multi-source writing task. We further investigated how these processes affect the quality of a written product, i.e., essay score. In the analysis, we utilised both automatically and human-generated essay score. The rating performance of the essay scoring algorithm was comparable to that of human raters. Our results largely support the theoretical assumptions that engagement in metacognitive monitoring and control benefits the quality of written product. Moreover, our results can inform the development of analytics-based tools that support student writing by making use of trace data and automated essay scoring.

See you there!