Enhancing a mobile and personalized learning platform through facial analytics and interactive quizzes

Abstract

Learning analytics techniques to capture learners’ real-time responses can be computationally intensive for mobile devices. Yet with the diversity of many online educational videos such as the Ted Talks, YouTube or other open educational resources, the mobile learning platform intrinsically presents an opportunity for learners and instructors to analyze the pace and quality of learning. In a previous work, the PErsonalized Teaching And Learning (PETAL) platform was developed to monitor the learner’s progress in viewing educational videos through a carefully designed facial analytics technique to detect learners’ attention levels and their proximity to the screen of the mobile device. The PETAL application will alert a learner if (s)he is not paying attention while viewing an educational video. In this work, we propose to further improve the original PETAL by using online and interactive quizzes to more precisely evaluate the learner’s level of understanding while viewing the educational videos. The interactive quizzes can be flexibly added by an instructor by modifying a simple configuration file. When a learner gives an incorrect answer to a particular quiz question, (s)he will be requested to revise a specific section of the concerned video. All the data about the learner’s progress is uploaded and stored securely on a password-protected cloud platform to guarantee data privacy for future analyzes. There were some initial and positive students’ feedback collected from an empirical evaluation of the enhanced PETAL system. Clearly, the enhanced PETAL platform provides many promising directions for future extensions.