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Data Mining and Computational Intelligence for E-Learning and Education—3rd Edition
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Data Mining and Computational Intelligence for E-Learning and Education—3rd Edition

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 4578

Special Issue Editor

Dr. Antonio Sarasa-Cabezuelo

E-Mail Website
Guest Editor
Facultad de Informática, Universidad Complutense de Madrid, 28001 Madrid, Spain
Interests: machine learning; artificial intelligence; e-learning; programming languages
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, the rise of artificial intelligence has driven its application in various fields, including education. Applications exist that aim to analyze the data of the learning–teaching activity, both in face-to-face environments and distance-learning environments, using intelligent algorithms to extract information about the educational process. Using this information, it is possible to infer aspects such as the reasons for the success or failure of students, patterns of behavior and learning, and other predictions. Likewise, applications have also been developed that implement intelligent algorithms to automate the educational process. Related to this last point is the development of chatbots and approaches to ethics in the use of artificial intelligence. In this sense, an area of interest has developed related to the application of artificial intelligence to problem solving in education. This Special Issue will bring together works that show the latest advances in the application of artificial intelligence in the educational field, as well as those describing specific experiences and applications to certain problems.

This Special Issue will serve as a meeting point for all researchers working in these fields, whether they study theories or applications. The topics of interest include, but are not limited to, the following:

  • Generative AI for creating educational content and learning materials;
  • Machine learning and deep learning applications in education;
  • Artificial intelligence for personalized learning and adaptive education;
  • Big data and learning analytics in education;
  • Intelligent tutoring systems and virtual teaching assistants;
  • Predictive analytics and data-driven decision-making in education;
  • AI-driven content creation and curriculum development;
  • Ethics and bias in AI-powered educational tools;
  • Learning experience platforms (LXP) and smart educational environments;
  • Data privacy and security in AI-enhanced learning systems;
  • Conversational AI and chatbots for enhancing student engagement;
  • Gamification and immersive learning experiences using AI;
  • Natural language processing (NLP) in educational assessments and feedback.

Both review articles on the state of the art and experimental/theoretical articles are welcome.

Dr. Antonio Sarasa-Cabezuelo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • e-learning
  • Machine learning
  • Artificial intelligence
  • Data analysis
  • Algorithms
  • Big data

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Related Special Issues

Published Papers (4 papers)

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29 pages, 4066 KiB  
Article
SAPEx-D: A Comprehensive Dataset for Predictive Analytics in Personalized Education Using Machine Learning
by Muhammad Adnan Aslam, Fiza Murtaza, Muhammad Ehatisham Ul Haq, Amanullah Yasin and Numan Ali
Data 2025, 10(3), 27; https://doi.org/10.3390/data10030027 - 20 Feb 2025
Viewed by 879
Abstract
Education is crucial for leading a productive life and obtaining necessary resources. Higher education institutions are progressively incorporating artificial intelligence into conventional teaching methods as a result of innovations in technology. As a high academic record raises a university’s ranking and increases student [...] Read more.
Education is crucial for leading a productive life and obtaining necessary resources. Higher education institutions are progressively incorporating artificial intelligence into conventional teaching methods as a result of innovations in technology. As a high academic record raises a university’s ranking and increases student career chances, predicting learning success has been a central focus in education. Both performance analysis and providing high-quality instruction are challenges faced by modern schools. Maintaining high academic standards, juggling life and academics, and adjusting to technology are problems that students must overcome. In this study, we present a comprehensive dataset, SAPEx-D (Student Academic Performance Exploration), designed to predict student performance, encompassing a wide array of personal, familial, academic, and behavioral factors. Our data collection effort at Air University, Islamabad, Pakistan, involved both online and paper questionnaires completed by students across multiple departments, ensuring diverse representation. After meticulous preprocessing to remove duplicates and entries with significant missing values, we retained 494 valid responses. The dataset includes detailed attributes such as demographic information, parental education and occupation, study habits, reading frequencies, and transportation modes. To facilitate robust analysis, we encoded ordinal attributes using label encoding and nominal attributes using one-hot encoding, expanding our dataset from 38 to 88 attributes. Feature scaling was performed to standardize the range and distribution of data, using a normalization technique. Our analysis revealed that factors such as degree major, parental education, reading frequency, and scholarship type significantly influence student performance. The machine learning models applied to this dataset, including Gradient Boosting and Random Forest, demonstrated high accuracy and robustness, underscoring the dataset’s potential for insightful academic performance prediction. In terms of model performance, Gradient Boosting achieved an accuracy of 68.7% and an F1-score of 68% for the eight-class classification task. For the three-class classification, Random Forest outperformed other models, reaching an accuracy of 80.8% and an F1-score of 78%. These findings highlight the importance of comprehensive data in understanding and predicting academic outcomes, paving the way for more personalized and effective educational strategies. Full article
(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—3rd Edition)
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17 pages, 662 KiB  
Article
A Bayesian State-Space Approach to Dynamic Hierarchical Logistic Regression for Evolving Student Risk in Educational Analytics
by Moeketsi Mosia
Data 2025, 10(2), 23; https://doi.org/10.3390/data10020023 - 7 Feb 2025
Viewed by 811
Abstract
Early detection of academically at-risk students is crucial for designing timely interventions that improve educational outcomes. However, many existing approaches either ignore the temporal evolution of student performance or rely on “black box” models that sacrifice interpretability. In this study, we develop a [...] Read more.
Early detection of academically at-risk students is crucial for designing timely interventions that improve educational outcomes. However, many existing approaches either ignore the temporal evolution of student performance or rely on “black box” models that sacrifice interpretability. In this study, we develop a dynamic hierarchical logistic regression model in a fully Bayesian framework to address these shortcomings. Our method leverages partial pooling across students and employs a state-space formulation, allowing each student’s log-odds of failure to evolve over multiple assessments. By using Markov chain Monte Carlo for inference, we obtain robust posterior estimates and credible intervals for both population-level and individual-specific effects, while posterior predictive checks ensure model adequacy and calibration. Results from simulated and real-world datasets indicate that the proposed approach more accurately tracks fluctuations in student risk compared to static logistic regression, and it yields interpretable insights into how engagement patterns and demographic factors influence failure probability. We conclude that a Bayesian dynamic hierarchical model not only enhances prediction of at-risk students but also provides actionable feedback for instructors and administrators seeking evidence-based interventions. Full article
(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—3rd Edition)
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14 pages, 770 KiB  
Article
Stress Factors in Higher Education: A Data Analysis Case
by Rodolfo Bojorque, Fernando Moscoso, Fernando Pesántez and Ángela Flores
Data 2025, 10(2), 22; https://doi.org/10.3390/data10020022 - 7 Feb 2025
Viewed by 1337
Abstract
This study investigates stressors in higher education, focusing on their impact on students and faculty at Universidad Politécnica Salesiana (UPS) and using eight years of comprehensive data. Employing data mining techniques, the research analyzed enrollment, retention, graduation, employability, socioeconomic status, academic performance, and [...] Read more.
This study investigates stressors in higher education, focusing on their impact on students and faculty at Universidad Politécnica Salesiana (UPS) and using eight years of comprehensive data. Employing data mining techniques, the research analyzed enrollment, retention, graduation, employability, socioeconomic status, academic performance, and faculty workload to uncover patterns affecting academic outcomes. The study found that UPS exhibits a stable educational system, maintaining consistent metrics across student success indicators. However, the COVID-19 pandemic presented unique stressors, evidenced by a paradoxical increase in student grades during heightened faculty stress levels. This anomaly suggests a potential link between academic rigor and faculty well-being during systemic disruptions. Stressors affecting students directly correlated with reduced academic performance, highlighting the importance of early detection and intervention. Conversely, faculty stress was reflected in adjustments to grading practices, raising questions about institutional pressures and faculty motivation. These findings emphasize the value of proactive data analytics in identifying stress-induced anomalies to support student success and faculty well-being. The study advocates for further research on faculty burnout, motivation, and institutional strategies to mitigate stressors, underscoring the potential of data-driven approaches to enhance the quality and sustainability of higher education ecosystems. Full article
(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—3rd Edition)
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11 pages, 1930 KiB  
Data Descriptor
Towards a Datatset of Digitalized Historical German VET and CVET Regulations
by Thomas Reiser, Jens Dörpinghaus, Petra Steiner and Michael Tiemann
Data 2024, 9(11), 128; https://doi.org/10.3390/data9110128 - 3 Nov 2024
Viewed by 997
Abstract
The digitization of historical documents has gained particular interest in recent years in the digital humanities. The goal is to digitize historical documents by extracting and structuring text from scanned images. Here, we focus on the processing of historical German VET (vocational education [...] Read more.
The digitization of historical documents has gained particular interest in recent years in the digital humanities. The goal is to digitize historical documents by extracting and structuring text from scanned images. Here, we focus on the processing of historical German VET (vocational education and training) and CVET (continuing vocational education and training) regulations to support educational research. This dataset contains data from 1908 to the present and includes 2125 documents as PDF, 983 fully converted XML documents, and additional metadata for 7090 documents from the archive. We present an overview of the historical background and the challenges of processing different historical documents from three different federal states. Full article
(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—3rd Edition)
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