Predictive and adaptive learning analytics in online and hybrid course delivery
Predictive and Adaptive Learning Analytics in Online and Hybrid Course Delivery
This project makes use of predictive and adaptive learning analytics with the broader goal of contributing to the reinvention and re-imagining of undergraduate education through the enhancement of the delivery of LIN204H5 English Grammar. This is a high enrolment course that is delivered both as a fully online course and as a hybrid course taken by many international students as well as native English speakers and Linguistic program students to enhance their understanding of the structure and usage of English. The project’s specific aims are:
1) to enhance student assessment and feedback processes to ensure all students receive optimal support and direction to achieve their learning goals;
2) to support independent, engaged, and self-regulating learners through the implementation of learning analytic tools;
3) to engage in the community of practice and support pedagogical change where best practices in online pedagogy are not yet well established. This will be achieved through the dissemination of the methodology and mechanisms developed within the project; and
4) to increase the University’s presence in high quality e-learning.
Crucially, the tools used to collect and analyze student performance data are compatible with any fully functioning LMS and would be scalable to other online and hybrid courses.
Department of Language Studies, University of Toronto Mississauga
Michelle Troberg, Lecturer and Program Coordinator
Year LEAF Granted
LEAF Priority Area(s)
Impact of the Project on Students
This project will encourage critical thinking amongst students and provide them with the opportunity to think outside of the realm of the textbook alone by engaging in puzzle based problems.