# Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling

arXiv: https://arxiv.org/abs/2102.00089 · HTML: https://mlsanity.com/paper/2102.00089/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 29 Jan 2021.

## Builds on (most cited first, 5 of 5)

- [Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks](https://mlsanity.com/paper/1705.08982/) (2017)
- [Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity](https://mlsanity.com/paper/1602.06033/) (2016)
- [Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling](https://mlsanity.com/paper/1707.03003/) (2017)
- [Recurrent Poisson Factorization for Temporal Recommendation](https://mlsanity.com/paper/1703.01442/) (2017)
- [Modeling Interdependent and Periodic Real-World Action Sequences](https://mlsanity.com/paper/1802.09148/) (2018)

## Built on by (most cited first, 12 of 12)

- [Shop-R1: Rewarding LLMs to Simulate Human Behavior in Online Shopping via Reinforcement Learning](https://mlsanity.com/paper/2507.17842/) (2025)
- [Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning](https://mlsanity.com/paper/2208.01182/) (2022)
- [ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences](https://mlsanity.com/paper/2206.05291/) (2022)
- [A Survey on Deep Learning Techniques for Action Anticipation](https://mlsanity.com/paper/2309.17257/) (2023)
- [Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics](https://mlsanity.com/paper/2212.02985/) (2022)
- [See, Think, Act: Online Shopper Behavior Simulation with VLM Agents](https://mlsanity.com/paper/2510.19245/) (2025)
- [Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis](https://mlsanity.com/paper/2406.03064/) (2024)
- [Process-BERT: A Framework for Representation Learning on Educational Process Data](https://mlsanity.com/paper/2204.13607/) (2022)
- [Modeling Time-Series and Spatial Data for Recommendations and Other Applications](https://mlsanity.com/paper/2212.13259/) (2022)
- [Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows](https://mlsanity.com/paper/2307.10305/) (2023)
- [MDHP-Net: Detecting an Emerging Time-exciting Threat in IVN](https://mlsanity.com/paper/2411.10258/) (2024)
- [Learning Structure-enhanced Temporal Point Processes with Gromov-Wasserstein Regularization](https://mlsanity.com/paper/2503.23002/) (2025)

## Abstract (arXiv)

Student procrastination and cramming for deadlines are major challenges in online learning environments, with negative educational and well-being side effects. Modeling student activities in continuous time and predicting their next study time are important problems that can help in creating personalized timely interventions to mitigate these challenges. However, previous attempts on dynamic modeling of student procrastination suffer from major issues: they are unable to predict the next activity times, cannot deal with missing activity history, are not personalized, and disregard important course properties, such as assignment deadlines, that are essential in explaining the cramming behavior. To resolve these problems, we introduce a new personalized stimuli-sensitive Hawkes process model (SSHP), by jointly modeling all student-assignment pairs and utilizing their similarities, to predict students' next activity times even when there are no historical observations. Unlike regular point processes that assume a constant external triggering effect from the environment, we model three dynamic types of external stimuli, according to assignment availabilities, assignment deadlines, and each student's time management habits. Our experiments on two synthetic datasets and two real-world datasets show a superior performance of future activity prediction, comparing with state-of-the-art models. Moreover, we show that our model achieves a flexible and accurate parameterization of activity intensities in students.