# Time Series Analysis for Education: Methods, Applications, and Future Directions

arXiv: https://arxiv.org/abs/2408.13960 · HTML: https://mlsanity.com/paper/2408.13960/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 288 references that are in the map.
- First posted on arXiv 25 Aug 2024.

## Builds on (most cited first, 28 of 28)

- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [EdNet: A Large-Scale Hierarchical Dataset in Education](https://mlsanity.com/paper/1912.03072/) (2019)
- [Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2306.10125/) (2023)
- [Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review](https://mlsanity.com/paper/2303.13379/) (2023)
- [EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education](https://mlsanity.com/paper/2308.02773/) (2023)
- [Large Language Models in Education: Vision and Opportunities](https://mlsanity.com/paper/2311.13160/) (2023)
- [Bringing Generative AI to Adaptive Learning in Education](https://mlsanity.com/paper/2402.14601/) (2024)
- [Educational data mining and learning analytics: An updated survey](https://mlsanity.com/paper/2402.07956/) (2024)
- [Deep Video Anomaly Detection: Opportunities and Challenges](https://mlsanity.com/paper/2110.05086/) (2021)
- [Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges](https://mlsanity.com/paper/2401.08664/) (2023)
- [Foundation Models for Education: Promises and Prospects](https://mlsanity.com/paper/2405.10959/) (2024)
- [GritNet: Student Performance Prediction with Deep Learning](https://mlsanity.com/paper/1804.07405/) (2018)
- [RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Education](https://mlsanity.com/paper/2212.01133/) (2022)
- [Improving prediction of students' performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources](https://mlsanity.com/paper/2403.07194/) (2024)
- [Predicting Grades](https://mlsanity.com/paper/1508.03865/) (2015)
- [Predicting University Students' Academic Success and Major using Random Forests](https://mlsanity.com/paper/1802.03418/) (2018)
- [MUTLA: A Large-Scale Dataset for Multimodal Teaching and Learning Analytics](https://mlsanity.com/paper/1910.06078/) (2019)
- [Riiid! Answer Correctness Prediction Kaggle Challenge: 4th Place Solution Summary](https://mlsanity.com/paper/2102.04250/) (2021)
- [Multi-source and multimodal data fusion for predicting academic performance in blended learning university courses](https://mlsanity.com/paper/2403.05552/) (2024)
- [Identifying At-Risk K-12 Students in Multimodal Online Environments: A Machine Learning Approach](https://mlsanity.com/paper/2003.09670/) (2020)
- [Predicting student performance using sequence classification with time-based windows](https://mlsanity.com/paper/2208.07749/) (2022)
- [Detecting and Preventing "Multiple-Account" Cheating in Massive Open Online Courses](https://mlsanity.com/paper/1508.05699/) (2015)
- [Learning Unit State Recognition Based on Multi-channel Data Fusion](https://mlsanity.com/paper/1806.07372/) (2018)

## Built on by (most cited first, 5 of 5)

- [Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts](https://mlsanity.com/paper/2409.16040/) (2024)
- [TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models](https://mlsanity.com/paper/2601.18744/) (2026)
- [RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2509.02341/) (2025)
- [Toward Physics-guided Time Series Embedding](https://mlsanity.com/paper/2410.06651/) (2024)
- [TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning](https://mlsanity.com/paper/2607.08940/) (2026)

## Abstract (arXiv)

Recent advancements in the collection and analysis of sequential educational data have brought time series analysis to a pivotal position in educational research, highlighting its essential role in facilitating data-driven decision-making. However, there is a lack of comprehensive summaries that consolidate these advancements. To the best of our knowledge, this paper is the first to provide a comprehensive review of time series analysis techniques specifically within the educational context. We begin by exploring the landscape of educational data analytics, categorizing various data sources and types relevant to education. We then review four prominent time series methods-forecasting, classification, clustering, and anomaly detection-illustrating their specific application points in educational settings. Subsequently, we present a range of educational scenarios and applications, focusing on how these methods are employed to address diverse educational tasks, which highlights the practical integration of multiple time series methods to solve complex educational problems. Finally, we conclude with a discussion on future directions, including personalized learning analytics, multimodal data fusion, and the role of large language models (LLMs) in educational time series. The contributions of this paper include a detailed taxonomy of educational data, a synthesis of time series techniques with specific educational applications, and a forward-looking perspective on emerging trends and future research opportunities in educational analysis. The related papers and resources are available and regularly updated at the project page.