Using Affective Learning Analytics in Industry-focused Projects: Experiences and Challenges

Project-based learning (PJBL) with real world clients provides students with the skills and knowledge required by industry. Similar to asynchronous online learning environments, PJBL students typically work in self-directed teams at times and places of their choice. Thus, it is difficult for the educators to identify technical and emotional challenges that students experience—especially with large cohorts. To bridge the disconnect between educators and students in such learning situations and to be able to provide students with timely feedback and support, we have experimented with the use of an emotion detection tool to automatically recognise students’ emotional states and the issues that they might face. Our results show that the detected emotions moderately to strongly correlate with students’ issues as observed by the academic coordinators and also with students’ final marks. However, our explorations also highlighted ethical challenges when using emotion-aware learning analytics. This paper describes our experiences and discusses possible ways to address those challenges.