# A Comprehensive Review of Multimodal Analysis in Education
## 2 Background and Theoretical Foundations
### Search and Selection Process  
To ensure a comprehensive and representative review of multimodal analysis in educational settings, a structured search was conducted across four major bibliographic databases: Scopus, Web of Science, IEEE Xplore, and the ACM Digital Library. These databases were selected because they provide extensive coverage of high-quality, peer-reviewed research in the fields of computer science, education, and AI, which are core to the multidisciplinary nature of MMLA. Unlike other platforms, these databases offer more rigorous indexing, metadata quality, and citation analysis tools, ensuring a more reliable and replicable review process. Additionally, they allow for advanced filtering by document type, publication year, and subject area, which is crucial for reviews. The search strategy was designed to retrieve documents that addressed multimodal analysis or multimodal learning in educational contexts, including terms such as \"multi-modal analysis\", \"multimodal learning\", \"multimodal data\", and \"multimodal learning analytics\", combined with educational terms such as \"education\", \"educational technology\", \"learning environment\", \"classroom\", and \"intelligent tutoring system\". Only articles in English were considered, as this is the dominant language in the academic publications that form the basis of the scientific discourse in this field. This choice reduces the potential for misinterpretation and inconsistency in the translation of technical terminology, ensuring that the findings can be more widely understood and integrated into the global research community. The following Boolean logic was used as a base structure for the queries (adjusted to each database's syntax and field indexing):  
(\"multimodal analysis\" OR \"multimodal learning\" OR \"multimodal data\" OR \"multimodal learning analytics\") AND (education OR \"educational technology\" OR \"learning environment\" OR classroom OR \"intelligent tutoring system\")  
To ensure a manageable yet comprehensive corpus, the search strategy focused on explicit multimodal terminology, while also leveraging indexed keywords and subject categories where available. This approach recognises that multimodal research can encompass a wide range of techniques--such as eye-tracking, electroencephalogram (EEG), gesture recognition, and physiological monitoring--that may not always be labelled explicitly as \"multimodal\" in titles or abstracts, but are nonetheless categorised as such by the databases. This decision reflects a balance between inclusivity and specificity, prioritising studies that clearly align with the theoretical and analytical frameworks of MMLA, while avoiding an unmanageable volume of unrelated work. Queries were applied to document titles, abstracts, keywords, or indexed terms, and results were restricted to journal articles, conference proceedings, and book chapters in the fields of computer science and education. This process retrieved a total of 1667 documents:603 from Scopus, 730 from Web of Science, 198 from IEEE Xplore, and 136 from the ACM Digital Library. After deduplication, 1195 unique entries were obtained. Regarding the publication period, the search did not impose a specific time range. However, the final set of 177 peer-reviewed studies included in this review spans from 2008 to 2025. This range reflects the distribution of available publications that met the inclusion criteria, rather than an intentional cut-off. The starting point of 2008 corresponds to the earliest relevant study identified during the screening phase, as the inclusion criteria focused on studies that explicitly addressed multimodal data collection, processing, or analysis in educational settings. Although early theoretical work on multimodality dates back to the 1990s, the practical integration of multimodal data in educational research gained momentum in the late 2000s, driven by advances in data capture technologies and machine learning.