ReviewSensors (Basel, Switzerland)2020
Multimodal Data Fusion in Learning Analytics: A Systematic Review.
Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of multi-mode analytics for enhanced plant stress evaluation.Frontiers in plant science · 2025Pooled it
- Detecting Emotions through Electrodermal Activity in Learning Contexts: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- STEAP: Camera-Based Longitudinal Classroom Behavior Sensing and Static-Temporal Data Fusion for Academic Performance Prediction in Software Engineering Education.Sensors (Basel, Switzerland) · 2026Article
- Leveraging Multi-Source Data Fusion Approach for Fine-Grained Affective-Appraisal Analysis in TPD-Oriented Online Professional Learning.Behavioral sciences (Basel, Switzerland) · 2026Article
- Artificial intelligence analysis of facial movements and satisfaction in online medical lectures: a cross-sectional study.BMC medical education · 2026Article
- AI-enabled learning analytics use relates to physical literacy and engagement in university PE via smart teaching and personalised feedback.Scientific reports · 2026Article
- Exploring teacher-student interaction through multimodal large language models: an empirical investigation.Scientific reports · 2026Article
- Review
- Challenge-based learning in blended education: a meta-analysis of deep learning outcomes.Frontiers in psychology · 2026Review
- Assessing multidimensional student engagement in studio-based design learning: a traceable multimodal framework for teacher-led inquiry.Frontiers in psychology · 2026Article
- AI-driven transformation in forensic medicine education: applications, pedagogical shifts, and future challenges.Frontiers in medicine · 2026Review
- EEG analysis of brain dynamics in a simulated multi-task and multi-stage learning environment.NPJ science of learning · 2025Article
- Fault diagnosis in electric motors using multi-mode time series and ensemble transformers network.Scientific reports · 2025Article
- A multi-modal deep learning solution for precise pneumonia diagnosis: the PneumoFusion-Net model.Frontiers in physiology · 2025Article
- F-TransR: A sports event revenue prediction model integrating multi-modal and time-series data.PloS one · 2025Article
- Lessons Learned and Future Directions of MetaTutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning With an Intelligent Tutoring System.Frontiers in psychology · 2022Review
- How Can High-Frequency Sensors Capture Collaboration? A Review of the Empirical Links between Multimodal Metrics and Collaborative Constructs.Sensors (Basel, Switzerland) · 2021Review
- Using a material database and data fusion method to accelerate the process model development of high shear wet granulation.Scientific reports · 2021Article
- EFAR-MMLA: An Evaluation Framework to Assess and Report Generalizability of Machine Learning Models in MMLA.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Multimodal learning analytics (MMLA), which has become increasingly popular, can help provide an accurate understanding of learning processes. However, it is still unclear how multimodal data is integrated into MMLA. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper systematically surveys 346 articles on MMLA published during the past three years. For this purpose, we first present a conceptual model for reviewing these articles from three dimensions: data types, learning indicators, and data fusion. Based on this model, we then answer the following questions: 1. What types of data and learning indicators are used in MMLA, together with their relationships; and 2. What are the classifications of the data fusion methods in MMLA. Finally, we point out the key stages in data fusion and the future research direction in MMLA. Our main findings from this review are (a) The data in MMLA are classified into digital data, physical data, physiological data, psychometric data, and environment data; (b) The learning indicators are behavior, cognition, emotion, collaboration, and engagement; (c) The relationships between multimodal data and learning indicators are one-to-one, one-to-any, and many-to-one. The complex relationships between multimodal data and learning indicators are the key for data fusion; (d) The main data fusion methods in MMLA are many-to-one, many-to-many and multiple validations among multimodal data; and (e) Multimodal data fusion can be characterized by the multimodality of data, multi-dimension of indicators, and diversity of methods.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.