Cukurova, Mutlu;
Khan-Galaria, M;
(2022)
Monitoring Tutor Practices to Support Self-regulated Learning in Online One-To-One Tutoring Sessions with Process Mining.
In:
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022.
(pp. pp. 405-409).
Springer: Cham, Switzerland.
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Abstract
This paper reports on research that aims to examine what tutoring practices in an online environment can promote students’ self-regulated learning (SRL). First, we propose a theoretically grounded framework of signifiers that can be used to track tutor-student interactions with respect to SRL. Second, we operationalize the framework using log data from a virtual learning environment and process mining approaches. Our results demonstrate that there are structural differences in tutor-learner interactions between the high performing versus low performing tutors. High performing tutors show complex patterns of engagement, which emphasize open-ended questioning and reasoning. Whilst the low performing tutors use a more restricted range of teaching practices that focus on instruction and are more strictly led by the learning platf
| Type: | Proceedings paper |
|---|---|
| Title: | Monitoring Tutor Practices to Support Self-regulated Learning in Online One-To-One Tutoring Sessions with Process Mining |
| Event: | AIED 2022: The 23rd International Conference on Artificial Intelligence in Education, |
| Location: | Durham, UK |
| Dates: | 27 Jul 2022 - 31 Jul 2022 |
| ISBN-13: | 978-3-031-11647-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-11647-6_80 |
| Publisher version: | https://doi.org/10.1007/978-3-031-11647-6_80 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Self-regulated learning, Online tutoring, Process mining, Virtual classroom environment framework of signifiers |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Culture, Communication and Media UCL > Provost and Vice Provost Offices > School of Education UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10156278 |
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