Impact of online learning sequences to forecast course outcomes for an undergraduate digital signal processing (DSP) course is studied in this work. A multi-modal learning schema based on deep-learning techniques with learning sequences, psychometric measures, and personality traits as input features is developed in this work. The aim is to identify any underlying patterns in the learning sequences and subsequently forecast the learning outcomes. Experiments are conducted on the data acquired for the DSP course taught over 13 teaching weeks to underpin the forecasting efficacy of various deeplearning models. Results showed that the proposed multi-modal schema yields better forecasting performance compared to existing frequency-based methods in existing literature. It is further observed that the psychometric measures incorporated in the proposed multimodal schema enhance the ability of distinguishing nuances in the input sequences when the forecasting task is highly dependent on human behavior.
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Although there is increasing acceptance that personalization improves learning outcomes, there is still limited experimental evidence supporting this claim. The aim of this study was to implement and evaluate the effectiveness of an adaptive recommendation system for Singapore primary and secondary education. The system leverages users trace data and learning analytics to generate assessment worksheets customized to individual learner’s proficiency. Analysis of online data is used to measure students’ skill levels in specific knowledge domains and monitor their progress. Notably, online measurements correlate positively with offline academic outcomes. A randomized controlled trial conducted on forty-three primary school students revealed statistically significant improvement on academic performance of a group of students receiving personalized content over a control group using non-adaptive material. The authors conclude that the reported recommender system successfully helps students improve their academic achievements.
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