http://web.cs.wpi.edu/~nth/pubs_and_grants/papers/2011/EDM%202011/Qiu%20Does%20Time%20Matter.pdf 

University of Michigan - Ann Arbor

Research addressing the prediction of a user's return time to a learning tool, based on their previous interactions, includes studies such as "Deep Attentive Study Session Dropout Prediction in Mobile Learning Environment" and "Does Time Matter? Modeling the Effect of Time in Bayesian Knowledge Tracing".

Research Papers on Predicting User Return Time Based on Previous Interactions

Reference for Deep Attentive Study Session Dropout Prediction in Mobile Learning Environment

- Introduction
- Time Model Design
- Model Performance Evaluations
- Contributions
- Discussions and Future Work

Does Time Matter? Modeling the Effect of Time in Bayesian Knowledge Tracing

The paper "Deep Attentive Study Session Dropout Prediction in Mobile Learning Environment" proposes the Deep Attentive Study (DAS) model to predict the probability of student dropout from an ongoing study session. Specifically tailored for mobile learning environments, the study utilizes the Santa dataset (containing student interactions with an AI tutor) to evaluate the DAS model against baseline models such as LSTM and GRU networks.

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