… predictions. It linearly combines the prototypes using the corresponding predicted coefficients
and then crops with a predicted … We consider a class-incremental learning scenario. The …

… Recall@20 then measures the fraction of the total amount of positive items that were
discovered by the top 20 predicted items of each user. To evaluate the effectiveness of the …

… -based incremental learning involves the training of a second set of classes S2 for a model
that is already able to predict a … baseline model-based incremental learning methods, namely …

… For example, [2] investigated the incremental validity of the … In our study, academic
performance prediction is considered … by Kelley [41], academic performance is divided into low-, …

… of students into account to identify students at risk of academic failure during the pandemic.
Additionally, this study proposes an optimal ensemble model predicting students at risk using …

… optimizing the incremental learning methods based on … predicted as one category realizing
the transformation from a multihead incremental learning model to a single-head incremental …

… Thus, incremental learning is introduced. An ideal incremental learning model should be …
In this article, a lightweight incremental learning approach (LIL) is proposed for remote …

… This study presents two procedures for identifying at-risk students (dropout-prone and non…
student dropout and course performance prediction was only determined by their performance …

High education is an important and critical part of education all over the world. In last year,
the world has been turned increasingly to online education due to the outbreak of the Covid-…

… into incremental learning, in the purpose of making predictions … Unlike feature-based method,
response-based method can … , we incrementally learn a strong and efficient student object …