… For brevity, the probability of the training instance X n populating existing data clouds is
predicted as follows:(10) P ( X n ∈ N i ) = 1 N i ∑ n = 1 N i M ( X n w e i g h t , X i w e i g h t ) ∑ i = …

… This paper proposes a novel student academic performance prediction framework (ProbSAP)
that can efficiently extract more student characteristics from imbalanced datasets. …

… /course sequences based on student backgrounds and past performance, the purpose of
the current work is to predict future performance based on student backgrounds and past …

… This study also examines whether SLAT-Thinking adds in predicting academic performance
… that SLAT-Thinking will predict academic achievement and provide incremental validity over …

… to learn better embedding space for incremental learning. (ii) For classifier learning in incremental
learning, … For iCaRL, we respectively report its results of CNN predictions and nearest-…

… They suffer from catastrophic forgetting –abrupt drop in performance. This phenomenon
affects … for incremental learning. A knowledge distillation loss is introduced for the prediction of …

… Different layers of the network obtain a different number of parameters representing learned
features, however it is difficult to predict at what point of incremental learning a layer will not …

… the prediction and classification of student performance respectively using five machine
learning … In classification phase, increment of classes may cause the reduction of success rate in …

… the predicted attribute information into an incremental learning framework… incremental
manner; namely, at incremental learning step t, we define the teacher model as Nt and the student …

… been used to predict student’s performance. This paper also focuses on how the prediction
algorithm can be used to identify the most important attributes in a student’s data. Therefore …