A review of deep learning-based recommender system in e-learning environments
T Liu, Q Wu, L Chang, T Gu - Artificial Intelligence Review, 2022 - Springer
While the recent emergence of a large number of online course resources has made life
more convenient for many people, it has also caused information overload. According to a …
more convenient for many people, it has also caused information overload. According to a …
Understanding dropouts in MOOCs
Massive open online courses (MOOCs) have developed rapidly in recent years, and have
attracted millions of online users. However, a central challenge is the extremely high dropout …
attracted millions of online users. However, a central challenge is the extremely high dropout …
Hierarchical reinforcement learning for course recommendation in MOOCs
The proliferation of massive open online courses (MOOCs) demands an effective way of
personalized course recommendation. The recent attention-based recommendation models …
personalized course recommendation. The recent attention-based recommendation models …
Designing reinforcement learning algorithms for digital interventions: pre-implementation guidelines
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital
interventions in the fields of mobile health and online education. Common challenges in …
interventions in the fields of mobile health and online education. Common challenges in …
Diversified interactive recommendation with implicit feedback
Interactive recommender systems that enable the interactions between users and the
recommender system have attracted increasing research attention. Previous methods …
recommender system have attracted increasing research attention. Previous methods …
Intelligentpooling: Practical thompson sampling for mhealth
In mobile health (mHealth) smart devices deliver behavioral treatments repeatedly over time
to a user with the goal of helping the user adopt and maintain healthy behaviors …
to a user with the goal of helping the user adopt and maintain healthy behaviors …
CNN autoencoders and LSTM-based reduced order model for student dropout prediction
K Niu, G Lu, X Peng, Y Zhou, J Zeng… - Neural Computing and …, 2023 - Springer
Abstract In recent years, Massive Open Online Courses (MOOCs) have become the main
online learning method for students all over the world, but their development has been …
online learning method for students all over the world, but their development has been …
Dynamic ensemble of contextual bandits to satisfy users' changing interests
Recommender systems have to handle a highly non-stationary environment, due to users'
fast changing interests over time. Traditional solutions have to periodically rebuild their …
fast changing interests over time. Traditional solutions have to periodically rebuild their …
Counterfactual inference for sequential experiments
We consider after-study statistical inference for sequentially designed experiments wherein
multiple units are assigned treatments for multiple time points using treatment policies that …
multiple units are assigned treatments for multiple time points using treatment policies that …
Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning
Discount regularization, using a shorter planning horizon when calculating the optimal
policy, is a popular choice to avoid overfitting when faced with sparse or noisy data. It is …
policy, is a popular choice to avoid overfitting when faced with sparse or noisy data. It is …