Using Learner Digital Footprints in Recommending Content in Learning Environments
The area of educational data mining has used some aspects of a learner’s digital footprint in order to predict outcome (grade in the final examination) or recommend some content of importance to help the learner. This has included timestamped clicks of pages viewed, prior performance and external data such as previous exam performance, timetables, earlier results, outputs from automatic assessments, etc. While these have each led to improved experiences for the learners, who are able to gauge their own progress and who also can be given personal recommendations for content they should view, the models used to create these predictions and recommendations are limited in that they use only a small portion of the learner’s digital footprint and that small portion is static and does not, and cannot account, for much insight into the learner’s state of mind at the time of prior use of the system. To capture digital footprints, we introduce two unique and independent monitoring mechanisms including keyboard dynamics and webcam-based attention applications. Keyboard dynamics is about capturing patterns of learners typing information, especially timing associated with bigrams or pairs of adjacently-typed alphanumeric characters. Similarly, webcam-based attention application runs on learner’s laptop, which monitors their facial attention while they are attending an online zoom session or reading material on screen or watching an educational video, and records an attention log. These methods preserve information with cost-neutral sources of interaction log, to give deeper insights into a learner’s state of mind, stress, and fatigue while interacting with digital content. We propose to enhance the modelling capabilities of a learning recommender system by capturing more of the learner’s state of mind during interaction with the system. Is she interested or bored with the content, is the learner engaged or distracted with the system because she is not motivated or because she is tired, stressed or cognitively distracted by some other task or outside influence. The first research challenge is related to data packaging of keyboard dynamics according to the mental state of learners. Another challenge is to do investigation about finding best models to calculate webcam attention graphs efficiently. Furthermore, comprehensive research is required for estimation of aggregated attention graphs which can be used for recommender systems. Also, all of this has to be done in a GDPR-compliant way so that users feel comfortable about recording such data about themselves.