Failure Risk Prediction in a MOOC: A Multivariate Time Series Analysis Approach

• URL: http://arxiv.org/abs/2507.21118v1
• Date: Thu, 17 Jul 2025 12:22:10 GMT
• Title: Failure Risk Prediction in a MOOC: A Multivariate Time Series Analysis Approach
• Authors: Anass El Ayady, Maxime Devanne, Germain Forestier, Nour El Mawas,
• Abstract summary: This work compares time series classification methods to identify at-risk learners at different stages of the course.<n>Preliminary results show that the evaluated approaches are promising for predicting learner failure in MOOCs.
• Score: 0.7087237546722617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: MOOCs offer free and open access to a wide audience, but completion rates remain low, often due to a lack of personalized content. To address this issue, it is essential to predict learner performance in order to provide tailored feedback. Behavioral traces-such as clicks and events-can be analyzed as time series to anticipate learners' outcomes. This work compares multivariate time series classification methods to identify at-risk learners at different stages of the course (after 5, 10 weeks, etc.). The experimental evaluation, conducted on the Open University Learning Analytics Dataset (OULAD), focuses on three courses: two in STEM and one in SHS. Preliminary results show that the evaluated approaches are promising for predicting learner failure in MOOCs. The analysis also suggests that prediction accuracy is influenced by the amount of recorded interactions, highlighting the importance of rich and diverse behavioral data.

Related papers

• STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via [7.247017092359663]
  We propose STTS-EAD, an end-to-end method that seamlessly integrates anomaly into the training process of time series forecasting.<n>Our proposed STTS-EAD leveragestemporal information for forecasting and anomaly detection, with the two parts alternately executed and optimized for each other.<n>Our experiments show that our proposed method can effectively process anomalies detected in the training stage to improve forecasting performance in the inference stage and significantly outperform baselines.
  arXiv  Detail & Related papers  (2025-01-14T03:26:05Z)
• An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes [5.257719744958367]
  Time-to-event outcomes have been studied extensively within the field of survival analysis. Monograph aims to provide a reasonably self-contained modern introduction to survival analysis.
  arXiv  Detail & Related papers  (2024-10-01T21:29:17Z)
• Experimental Comparison of Ensemble Methods and Time-to-Event Analysis Models Through Integrated Brier Score and Concordance Index [2.3411633024711573]
  We review and compare the performance of several prediction models for time-to-event analysis. We show how ensemble methods, which surprisingly have not yet been much studied in time-to-event analysis, can improve the prediction accuracy and enhance the robustness of the prediction performance.
  arXiv  Detail & Related papers  (2024-03-12T09:57:45Z)
• Successive Model-Agnostic Meta-Learning for Few-Shot Fault Time Series Prognosis [3.5573601621032944]
  We introduce a novel 'pseudo meta-task' partitioning scheme that treats a continuous time period of a time series as a meta-task. Employing continuous time series as pseudo meta-tasks allows our method to extract more comprehensive features and relationships from the data. We introduce a differential algorithm to enhance the robustness of our method across different datasets.
  arXiv  Detail & Related papers  (2023-11-04T02:07:47Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Time Series Contrastive Learning with Information-Aware Augmentations [57.45139904366001]
  A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples. How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question. We propose a new contrastive learning approach with information-aware augmentations, InfoTS, that adaptively selects optimal augmentations for time series representation learning.
  arXiv  Detail & Related papers  (2023-03-21T15:02:50Z)
• Time Associated Meta Learning for Clinical Prediction [78.99422473394029]
  We propose a novel time associated meta learning (TAML) method to make effective predictions at multiple future time points. To address the sparsity problem after task splitting, TAML employs a temporal information sharing strategy to augment the number of positive samples. We demonstrate the effectiveness of TAML on multiple clinical datasets, where it consistently outperforms a range of strong baselines.
  arXiv  Detail & Related papers  (2023-03-05T03:54:54Z)
• Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment [72.50906475214457]
  The goal of sequential event prediction is to estimate the next event based on a sequence of historical events. In practice, the next-event prediction models are trained with sequential data collected at one time. We propose a framework with hierarchical branching structures for learning context-specific representations.
  arXiv  Detail & Related papers  (2022-10-24T07:54:13Z)
• Prediction of Dilatory Behavior in eLearning: A Comparison of Multiple Machine Learning Models [0.2963240482383777]
  Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Research focusing on such predictions is scarce. Studies involving different types of predictors and comparisons between the predictive performance of various methods are virtually non-existent.
  arXiv  Detail & Related papers  (2022-06-30T07:24:08Z)
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data [83.50281440043241]
  We study the problem of inferring heterogeneous treatment effects from time-to-event data. We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
  arXiv  Detail & Related papers  (2021-10-26T20:13:17Z)
• Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning [80.66108902283388]
  Multi-task learning methods should be used with caution for safety-critical applications, such as clinical risk prediction. Existing asymmetric multi-task learning methods tackle this negative transfer problem by performing knowledge transfer from tasks with low loss to tasks with high loss. We propose a novel temporal asymmetric multi-task learning model that performs knowledge transfer from certain tasks/timesteps to relevant uncertain tasks, based on feature-level uncertainty.
  arXiv  Detail & Related papers  (2020-06-23T06:01:36Z)
• Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models [110.82452096672182]
  We propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data. We also introduce a novel metric for ambiguous problems, which is better suited to account for uncertainties.
  arXiv  Detail & Related papers  (2020-03-10T09:15:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.