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Digital data trails from disparate sources that cover different aspects of student life are being stored daily on most modern university campus. However currently it remains a challenge to (i) combine these data into a holistic view of a student; (ii) use this to predict academic performance accurately; (iii) and take advantage of the prediction to drive behavioral change and support student positive engagement with the university. To initially alleviate this problem, a framework named Augmented Education (AugmentED) is proposed in this chapter. In our study, (1) firstly, the experiment is conducted based on a real-world campus dataset from college students (N = 156), aggregating multisource data that consists of a small private online courses (SPOC) platform, usage of smart cards (e.g., library entry, meal, and consumption), Wi-Fi detection records, and central storage (e.g., gender, age, and class schedule). Specially, to gain a deep insight into the features leading to excellent or bad performance, on the one hand, three novel metrics (e.g., Lyapunov exponent) that measure the regularity of campus lifestyles are estimated; on the other hand, LSTM-based features that represent the dynamic changes of temporal lifestyle patterns are extracted by means of long short-term memory (LSTM). (2) Secondly, a machine learning-based intelligent algorithm is developed to predict academic performance. (3) Finally, visualized feedback is designed, potentially enabling students (especially for at-risk students) to optimize their interactions with the university and achieve study-life balance. |
