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Artificial Intelligence (AI) in Educational Data Mining and Learning Analytics

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 March 2026 | Viewed by 10290

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Special Issue Editors

Prof. Dr. Eloy López Meneses

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Guest Editor

Department of Education and Social Psychology, Area of School Organization and Didactics, Pablo de Olavide University, 41013 Seville, Spain
Interests: ICT in education; didactics; IA, RA, RV, online and ubiquitous learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. César Bernal-Bravo

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Guest Editor

Department of Education Sciences, Language, Culture and Arts, Rey Juan Carlos University, 28032 Madrid, Spain
Interests: educational technology; virtual reality; the metaverse increases with the academic use of AI in different learning scenarios—applied to social sciences
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and educational data mining (EDM) are profoundly transforming the educational landscape, reshaping the paradigms of teaching and learning. AI applications in education enable the analysis of large datasets, revealing intricate patterns in student behaviour and facilitating the personalisation of learning experiences. The GED, meanwhile, provides detailed information that enables evidence-based decision-making in education. Likewise, the integration of human–computer interaction (HCI) improves the accessibility and personalisation of educational interfaces, making learning tools more intuitive and responsive, and more advanced methods, including predictive modelling and machine learning algorithms, allow educational systems to dynamically adapt to individual needs, fostering a more inclusive and learner-centred approach to education. This Special Issue calls on the academic community to examine how these technologies are driving the development of adaptive learning environments, enabling early identification of student needs and promoting accessibility and equity in education.

Ultimately, this Special Issue aims to compile rigorous research that explores the impact of AI, EDM and advanced technological tools on evolving educational models. Researchers and practitioners are invited to contribute original ideas that improve the effectiveness and quality of education in increasingly diverse learning contexts.

The scope of the Special Issue includes, but is not limited to, the following topics:

Artificial intelligence in education;
Educational data mining (EDM);
Interactive machine learning (IML);
Human-in-the-loop machine learning and machine teaching;
Educational data mining and learning analytics;
Predictive modelling in education;
Machine learning for learning analytics;
Neural networks in educational AI;
Natural language processing (NLP) in education;
Intelligent tutoring systems (ITS);
AI ethics in education;
Inclusion and accessibility in AI tools.

Prof. Dr. Eloy López Meneses
Prof. Dr. César Bernal-Bravo
Guest Editors

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Keywords

artificial intelligence in education
educational data mining (EDM)
interactive machine learning (IML)

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Published Papers (8 papers)

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Research

25 pages, 7961 KiB

Open AccessArticle

A Multi-Layer Attention Knowledge Tracking Method with Self-Supervised Noise Tolerance

by Haifeng Wang, Hao Liu, Yanling Ge and Zhihao Yu

Appl. Sci. 2025, 15(15), 8717; https://doi.org/10.3390/app15158717 - 6 Aug 2025

Viewed by 249

Abstract

The knowledge tracing method based on deep learning is used to assess learners’ cognitive states, laying the foundation for personalized education. However, deep learning methods are inefficient when processing long-term series data and are prone to overfitting. To improve the accuracy of cognitive state prediction, we design a Multi-layer Attention Self-supervised Knowledge Tracing Method (MASKT) using self-supervised learning and the Transformer method. In the pre-training stage, MASKT uses a random forest method to filter out positive and negative correlation feature embeddings; then, it reuses noise-processed restoration tasks to extract more learnable features and enhance the learning ability of the model. The Transformer in MASKT not only solves the problem of long-term dependencies between input and output using an attention mechanism, but also has parallel computing capabilities that can effectively improve the learning efficiency of the prediction model. Finally, a multidimensional attention mechanism is integrated into cross-attention to further optimize prediction performance. The experimental results show that, compared with various knowledge tracing models on multiple datasets, MASKT’s prediction performance remains 2 percentage points higher. Compared with the multidimensional attention mechanism of graph neural networks, MASKT’s time efficiency is shortened by nearly 30%. Due to the improvement in prediction accuracy and performance, this method has broad application prospects in the field of cognitive diagnosis in intelligent education. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Educational Data Mining and Learning Analytics)

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20 pages, 1265 KiB

Open AccessArticle

Validation of the Player Personality and Dynamics Scale

by Ayose Lomba Perez, Juan Carlos Martín-Quintana, Jesus B. Alonso-Hernandez and Iván Martín-Rodríguez

Appl. Sci. 2025, 15(15), 8714; https://doi.org/10.3390/app15158714 - 6 Aug 2025

Viewed by 143

Abstract

This study presents the validation of the Player Personality and Dynamics Scale (PPDS), designed to identify player profiles in educational gamification contexts with narrative elements. Through a sample of 635 participants, a questionnaire was developed and applied, covering sociodemographic data, lifestyle habits, gaming practices, and a classification system of 40 items on a six-point Likert scale. The results of the factorial analysis confirm a structure of five factors: Toxic Profile, Joker Profile, Tryhard Profile, Aesthetic Profile, and Coacher Profile, with high fit and reliability indices (RMSEA = 0.06; CFI = 0.95; TLI = 0.91). The resulting classification enables the design of personalized gamified experiences that enhance learning and interaction in the classroom, highlighting the importance of understanding players’ motivations to better adapt educational dynamics. Applying this scale fosters meaningful learning through the creation of narratives tailored to students’ individual preferences. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Educational Data Mining and Learning Analytics)

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32 pages, 3894 KiB

Open AccessArticle

Building an Adaptive AI-Powered Higher Education Class for the Future of Engineering: A Case Study from NTUA

by Maria Karoglou, Ioana Ghergulescu, Marina Stramarkou, Christos Boukouvalas and Magdalyni Krokida

Appl. Sci. 2025, 15(15), 8524; https://doi.org/10.3390/app15158524 - 31 Jul 2025

Viewed by 156

Abstract

This study presents the outcomes of the Erasmus+ European project Higher Education Classroom of the Future (HECOF), with a particular focus on chemical engineering education. In the digital era, the integration and advancement of artificial intelligence (AI) in higher education, especially in engineering, are increasingly important. The main goal of the HECOF project is to establish a system of new higher education teaching practices and national reforms in education. This system has been developed and tested through an innovative personalized and adaptive method of teaching that exploited digital data from students’ learning activity in immersive environments, with the aid of computational analysis techniques from data science. The unit operations—extraction process course—a fundamental component of the chemical engineering curriculum, was selected as the case study for the development of the HECOF learning system. A group of undergraduate students evaluated the system’s usability and educational efficiency. The findings showed that the HECOF system contributed positively to students’ learning—although the extent of improvement varied among individuals—and was associated with a high level of satisfaction, suggesting that HECOF was effective in delivering a positive and engaging learning experience. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Educational Data Mining and Learning Analytics)

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13 pages, 510 KiB

Open AccessArticle

A Comparative Analysis of Student Performance Prediction: Evaluating Optimized Deep Learning Ensembles Against Semi-Supervised Feature Selection-Based Models

by Jose Antonio Lagares Rodríguez, Norberto Díaz-Díaz and Carlos David Barranco González

Appl. Sci. 2025, 15(9), 4818; https://doi.org/10.3390/app15094818 - 26 Apr 2025

Viewed by 621

Abstract

Advancements in modern technology have significantly increased the availability of educational data, presenting researchers with new challenges in extracting meaningful insights. Educational Data Mining offers analytical methods to support the prediction of student outcomes, development of intelligent tutoring systems, and curriculum optimization. Prior studies have highlighted the potential of semi-supervised approaches that incorporate feature selection to identify factors influencing academic success, particularly for improving model interpretability and predictive performance. Many feature selection methods tend to exclude variables that may not be individually powerful predictors but can collectively provide significant information, thereby constraining a model’s capabilities in learning environments. In contrast, Deep Learning (DL) models paired with Automated Machine Learning techniques can decrease the reliance on manual feature engineering, thereby enabling automatic fine-tuning of numerous model configurations. In this study, we propose a reproducible methodology that integrates DL with AutoML to evaluate student performance. We compared the proposed DL methodology to a semi-supervised approach originally introduced by Yu et al. under the same evaluation criteria. Our results indicate that DL-based models can provide a flexible, data-driven approach for examining student outcomes, in addition to preserving the importance of feature selection for interpretability. This proposal is available for replication and additional research. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Educational Data Mining and Learning Analytics)

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28 pages, 3795 KiB

Open AccessArticle

Identification of Key Factors Influencing Teachers’ Self-Perceived AI Literacy: An XGBoost and SHAP-Based Approach

by Hyeji Yang, Wongyu Lee and Jamee Kim

Appl. Sci. 2025, 15(8), 4433; https://doi.org/10.3390/app15084433 - 17 Apr 2025

Cited by 1 | Viewed by 922

Abstract

The rapid advancement of digital technologies and artificial intelligence (AI) is reshaping K-12 education, thereby emphasizing the growing need for AI Literacy among teachers. This study identifies key factors that influence teachers’ self-perceived AI Literacy and evaluates their impact on AI Literacy performance across various teaching phases, using Extreme Gradient Boosting (XGBoost) and Shapley Additive Explanations (SHAP). Data collected from 1172 K-12 teachers in South Korea were preprocessed and then split into an 80:20 training-to-testing ratio. To optimize model performance, Bayesian Optimization was used to fine-tune key hyperparameters, including the learning rate, maximum depth, subsample ratio, and number of boosting rounds. The model’s predictive accuracy was assessed using R², MSE, MAE, and RMSE. The optimized model achieved R² values of 0.8206 (Class Preparation), 0.8007 (Class Implementation), 0.8066 (Class Assessment), and 0.7746 (Utilizing Assessment Results). The results indicate that technical knowledge and AI Literacy skills are the most influential factors in the Class Preparation and Implementation Phases, while educational decision-making and ethical considerations play a crucial role in the Assessment and Utilizing Assessment Results Phases. Further, SHAP analysis highlights that both teachers’ and students’ perceived levels of AI learning significantly impact the adoption of AI Literacy, underscoring the importance of contextual factors in integrating AI within education. These findings emphasize the need for AI Literacy education that integrates technical competencies, pedagogical strategies, and ethical decision-making. This study provides empirical insights to support the development of teacher training programs and AI Literacy policies, ensuring the effective integration of AI in education. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Educational Data Mining and Learning Analytics)

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18 pages, 4106 KiB

Open AccessArticle

From Social to Academic: Associations and Predictions Between Different Types of Peer Relationships and Academic Performance Among College Students

by Jiadong Tian, Jiali Lin and Dagang Li

Appl. Sci. 2025, 15(5), 2262; https://doi.org/10.3390/app15052262 - 20 Feb 2025

Viewed by 1930

Abstract

This study aims to expose the correlation between different types of social behaviors and the academic performance of college students, and then to predict the academic performance of college students based on their social characteristics. We extracted and computed information on social relationships for roommates, classmates, members of the opposite sex, and others, based on real consumption data of 5597 freshmen students. The correlations between different types of peer relationships and academic performance were compared. Subsequently, we used Random Forests and Neural Networks as baseline methods, and introduced Graph Convolutional Network and Dynamic Graph Convolutional Network algorithms, on top of a graph network model based on social characteristics, to predict students’ academic performances. The results show that the quantity and quality of all types of socialization are positively correlated with academic performance, and socialization among classmates and roommates demonstrates a stronger correlation. In addition, with the construction of the graph model and the integration of time-series information, the prediction accuracy of the dynamic graph convolution method improved compared to other methods. The findings demonstrate the advantages of using social characteristics for academic performance prediction, and reveal the significant potential of AI applications in supporting the field of school management. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Educational Data Mining and Learning Analytics)

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32 pages, 5020 KiB

Open AccessArticle

A Social Network Analysis on the Danmaku of English-Learning Programs

by Man-Ni Chu, Xin Huang, Jia-Lien Hsu and Hai-Lun Tu

Appl. Sci. 2025, 15(4), 1948; https://doi.org/10.3390/app15041948 - 13 Feb 2025

Cited by 1 | Viewed by 957

Abstract

This study utilizes the danmaku on the Bilibili platform as the research subject to examine how their characteristics vary according to the nature or focus of English teaching videos. By employing social network analysis, the study reveals distinctive features in danmaku. For videos categorized under linguistic knowledge (phonetics, vocabulary, and grammar), the danmaku comments predominantly center around topics such as phonetics, vocabulary, and grammar. Conversely, in videos categorized under language skills (listening, speaking, reading and writing), the danmaku comments primarily reflect a vocabulary review for three of the four skills, with only the listening skill showing slight deviations. This underscores the centrality of vocabulary in skill-oriented videos. The findings highlight the unique role of danmaku in distinguishing between knowledge and skills within the context of English teaching videos. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Educational Data Mining and Learning Analytics)

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21 pages, 1469 KiB

Open AccessArticle

Artificial Intelligence in Educational Data Mining and Human-in-the-Loop Machine Learning and Machine Teaching: Analysis of Scientific Knowledge

by Eloy López-Meneses, Luis López-Catalán, Noelia Pelícano-Piris and Pedro C. Mellado-Moreno

Appl. Sci. 2025, 15(2), 772; https://doi.org/10.3390/app15020772 - 14 Jan 2025

Cited by 6 | Viewed by 4227

Abstract

This study explores the integration of artificial intelligence (AI) into educational data mining (EDM), human-assisted machine learning (HITL-ML), and machine-assisted teaching, with the aim of improving adaptive and personalized learning environments. A systematic review of the scientific literature was conducted, analyzing 370 articles published between 2006 and 2024. The research examines how AI can support the identification of learning patterns and individual student needs. Through EDM, student data are analyzed to predict student performance and enable timely interventions. HITL-ML ensures that educators remain in control, allowing them to adjust the system according to their pedagogical goals and minimizing potential biases. Machine-assisted teaching allows AI processes to be structured around specific learning criteria, ensuring relevance to educational outcomes. The findings suggest that these AI applications can significantly improve personalized learning, student tracking, and resource optimization in educational institutions. The study highlights ethical considerations, such as the need to protect privacy, ensure the transparency of algorithms, and promote equity, to ensure inclusive and fair learning environments. Responsible implementation of these methods could significantly improve educational quality. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Educational Data Mining and Learning Analytics)

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