Teaching Analytics: support for assessment and pedagogical design assistance using artificial intelligence.
PhD Student: Ibtissem Bennacer
Advisor(s): Sébastien Iksal
Co-advisor(s): Rémi Venant
Funding: Bourse Ministérielle
This thesis is about the use of Teaching Analytics to analyze teachers’ behaviors and assist them in the use of Learning Management Systems (LMS).
Teaching Analytics (TA) refers to methods and tools to help teachers analyze and improve their pedagogical designs, and more recently, the analysis of how teachers deliver their lessons. Our goal is to exploit this area of Teaching Analytics to help teachers evaluate their behavior on their institutional LMS. In fine, we aim at providing teachers with personal and social awareness tools, so that they engage in learning situations in order to improve their use of the LMS, and to support these situations with automatic feedback and peer learning.
We designed an evaluation model based on a qualitative and quantitative analysis. This model describes teachers’ practices on LMS through six major axes: evaluation, reflection, communication, collaboration, resources, as well as interactivity and gamification. Based on this model, we designed 3 TA indicators: LMS usage trend, curiosity score and regularity score.
Currently, we are developing a web application dedicated to teachers and educational engineers to (i) provide the first ones with self-assessment and recommendations functionalities, and (ii) allow the second ones to detect teachers with specific needs and teachers with an expertise profile.