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Data Mining and Computational Intelligence for E-Learning and Education—2nd Edition
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Data Mining and Computational Intelligence for E-Learning and Education—2nd 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: closed (20 April 2024) | Viewed by 6326

Special Issue Editor

Prof. Dr. Antonio Sarasa Cabezuelo

E-Mail Website
Guest Editor
Facultad de Informática, Universidad Complutense de Madrid, 28040 Madrid, Spain
Interests: NonSQL databases; 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 can be found aimed at analyzing the data of the learning-teaching activity, both in the face-to-face environment and in distance-learning environments, through intelligent algorithms with the aim of extracting information about the educational process. From 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 with the aim of automating 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 relating to the application of artificial intelligence to problem solving in education. The objective of this Special Issue is to bring together works that show the latest advances in the application of artificial intelligence to the educational field, as well as those describing specific experiences and applications to certain problems.

The objective of this Special Issue is to serve as a meeting point for all researchers working in these fields, both theoretically and applied. The topics of interest include but are not limited to:

  • Machine learning applied to e-learning and education;
  • Artificial intelligence applied to e-learning and education;
  • Big data and e-learning;
  • Intelligent learning systems;
  • Data analysis applied to e-learning and education;
  • Intelligent systems for e-learning;
  • Ethical aspects of the application of AI in education;
  • E-learning analytics;
  • Data mining for e-learning and education;
  • Chatbots for education.

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

Prof. Dr. Antonio Sarasa Cabezuelo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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

Related Special Issue

Published Papers (6 papers)

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Research

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25 pages, 686 KiB  
Article
Tuning Data Mining Models to Predict Secondary School Academic Performance
by William Hoyos and Isaac Caicedo-Castro
Data 2024, 9(7), 86; https://doi.org/10.3390/data9070086 - 26 Jun 2024
Viewed by 278
Abstract
In recent years, educational data mining has emerged as a growing discipline focused on develo** models for predicting academic performance. The primary objective of this research was to tune classification models to predict academic performance in secondary school. The dataset employed for this [...] Read more.
In recent years, educational data mining has emerged as a growing discipline focused on develo** models for predicting academic performance. The primary objective of this research was to tune classification models to predict academic performance in secondary school. The dataset employed for this study encompassed information from 19,545 high school students. We used descriptive statistics to characterise information contained in personal, school, and socioeconomic variables. We implemented two data mining techniques, namely artificial neural networks (ANN) and support vector machines (SVM). Parameter optimisation was conducted through five–fold cross–validation, and model performance was assessed using accuracy and F1–Score. The results indicate a functional dependence between predictor variables and academic performance. The algorithms demonstrated an average performance exceeding 80% accuracy. Notably, ANN outperformed SVM in the dataset analysed. This type of methodology could help educational institutions to predict academic underachievement and thus generate strategies to improve students’ academic performance. Full article
(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—2nd Edition)
27 pages, 1874 KiB  
Article
Predicting Academic Success of College Students Using Machine Learning Techniques
by Jorge Humberto Guanin-Fajardo, Javier Guaña-Moya and Jorge Casillas
Data 2024, 9(4), 60; https://doi.org/10.3390/data9040060 - 22 Apr 2024
Viewed by 1643
Abstract
College context and academic performance are important determinants of academic success; using students’ prior experience with machine learning techniques to predict academic success before the end of the first year reinforces college self-efficacy. Dropout prediction is related to student retention and has been [...] Read more.
College context and academic performance are important determinants of academic success; using students’ prior experience with machine learning techniques to predict academic success before the end of the first year reinforces college self-efficacy. Dropout prediction is related to student retention and has been studied extensively in recent work; however, there is little literature on predicting academic success using educational machine learning. For this reason, CRISP-DM methodology was applied to extract relevant knowledge and features from the data. The dataset examined consists of 6690 records and 21 variables with academic and socioeconomic information. Preprocessing techniques and classification algorithms were analyzed. The area under the curve was used to measure the effectiveness of the algorithm; XGBoost had an AUC = 87.75% and correctly classified eight out of ten cases, while the decision tree improved interpretation with ten rules in seven out of ten cases. Recognizing the gaps in the study and that on-time completion of college consolidates college self-efficacy, creating intervention and support strategies to retain students is a priority for decision makers. Assessing the fairness and discrimination of the algorithms was the main limitation of this work. In the future, we intend to apply the extracted knowledge and learn about its influence of on university management. Full article
(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—2nd Edition)
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14 pages, 563 KiB  
Data Descriptor
Evaluation of Online Inquiry Competencies of Chilean Elementary School Students: A Dataset
by Luz Chourio-Acevedo and Roberto González-Ibañez
Data 2024, 9(7), 85; https://doi.org/10.3390/data9070085 - 25 Jun 2024
Viewed by 524
Abstract
In the age of abundant digital content, children and adolescents face the challenge of develo** new information literacy competencies, particularly those pertaining to online inquiry, in order to thrive academically and personally. This article addresses the challenge encountered by Chilean students in develo** [...] Read more.
In the age of abundant digital content, children and adolescents face the challenge of develo** new information literacy competencies, particularly those pertaining to online inquiry, in order to thrive academically and personally. This article addresses the challenge encountered by Chilean students in develo** online inquiry competencies (OICs) essential for completing school assignments, particularly in natural science education. A diagnostic study was conducted with 279 elementary school students (from fourth to eighth grade) from four educational institutions in Chile, representing diverse socioeconomic backgrounds. An instrument aligned with the national curriculum, featuring questions related to natural sciences, was administered through a game named NEURONE-Trivia, which integrates a search engine and a logging component to record students’ search behavior. The primary outcome of this study is a dataset comprising demographic information, self-perception, and information-seeking behaviors data collected during students’ online search sessions for natural science research tasks. This dataset serves as a valuable resource for researchers, educators, and practitioners interested in investigating the interplay between demographic characteristics, self-perception, and information-seeking behaviors among elementary students within the context of OIC development. Furthermore, it enables further examination of students’ search behaviors concerning source evaluation, information retrieval, and information utilization. Full article
(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—2nd Edition)
17 pages, 1331 KiB  
Data Descriptor
Beyond the Classroom: An Analysis of Internal and External Factors Related to Students’ Love of Learning and Educational Outcomes
by Charles M. Burke, Lori P. Montross and Vera G. Dianova
Data 2024, 9(6), 81; https://doi.org/10.3390/data9060081 - 16 Jun 2024
Viewed by 666
Abstract
This study explores the multifaceted factors influencing student learning motivations and educational outcomes. Utilizing a diverse student body from Franklin University Switzerland, the study emphasizes the impact of internal factors, such as the psychological state of flow and a self-reported love of learning, [...] Read more.
This study explores the multifaceted factors influencing student learning motivations and educational outcomes. Utilizing a diverse student body from Franklin University Switzerland, the study emphasizes the impact of internal factors, such as the psychological state of flow and a self-reported love of learning, alongside GPA and student cohort influences like year of study, academic discipline, country of origin, and academic travel. Through a cross-sectional survey of 112 students, the study evaluates how these factors correlate with and diverge from each other and student GPAs, aiming to dissect the influences of intrinsic motivations, demographic variables, and educational experiences. Our analysis revealed significant correlations between students’ self-reported love of learning, experiences of flow, and academic performance. Conversely, academic travel did not show a significant direct impact, suggesting that while such experiences are enriching, they do not necessarily translate into a greater love of learning, flow, or higher academic achievement in the short term. However, demographic factors, particularly discipline of study and country of origin, significantly influenced the students’ love of learning, indicating varied motivational drives across different cultural and educational backgrounds. This study provides valuable insights for educational policymakers and institutions aiming to cultivate more engaging and fulfilling learning environments. Full article
(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—2nd Edition)
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15 pages, 1153 KiB  
Data Descriptor
EEG and Physiological Signals Dataset from Participants during Traditional and Partially Immersive Learning Experiences in Humanities
by Rebeca Romo-De León, Mei Li L. Cham-Pérez, Verónica Andrea Elizondo-Villegas, Alejandro Villarreal-Villarreal, Alexandro Antonio Ortiz-Espinoza, Carol Stefany Vélez-Saboyá, Jorge de Jesús Lozoya-Santos, Manuel Cebral-Loureda and Mauricio A. Ramírez-Moreno
Data 2024, 9(5), 68; https://doi.org/10.3390/data9050068 - 15 May 2024
Viewed by 852
Abstract
The relevance of the interaction between Humanities-enhanced learning using immersive environments and simultaneous physiological signal analysis contributes to the development of Neurohumanities and advancements in applications of Digital Humanities. The present dataset consists of recordings from 24 participants divided in two groups (12 [...] Read more.
The relevance of the interaction between Humanities-enhanced learning using immersive environments and simultaneous physiological signal analysis contributes to the development of Neurohumanities and advancements in applications of Digital Humanities. The present dataset consists of recordings from 24 participants divided in two groups (12 participants in each group) engaging in simulated learning scenarios, traditional learning, and partially immersive learning experiences. Data recordings from each participant contain recordings of physiological signals and psychometric data collected from applied questionnaires. Physiological signals include electroencephalography, real-time engagement and emotion recognition calculation by a Python EEG acquisition code, head acceleration, electrodermal activity, blood volume pressure, inter-beat interval, and temperature. Before the acquisition of physiological signals, participants were asked to fill out the General Health Questionnaire and Trait Meta-Mood Scale. In between recording sessions, participants were asked to fill out Likert-scale questionnaires regarding their experience and a Self-Assessment Manikin. At the end of the recording session, participants filled out the ITC Sense of Presence Inventory questionnaire for user experience. The dataset can be used to explore differences in physiological patterns observed between different learning modalities in the Humanities. Full article
(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—2nd Edition)
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12 pages, 14369 KiB  
Data Descriptor
An EEG Dataset of Subject Pairs during Collaboration and Competition Tasks in Face-to-Face and Online Modalities
by María A. Hernández-Mustieles, Yoshua E. Lima-Carmona, Axel A. Mendoza-Armenta, **mena Hernandez-Machain, Diego A. Garza-Vélez, Aranza Carrillo-Márquez, Diana C. Rodríguez-Alvarado, Jorge de J. Lozoya-Santos and Mauricio A. Ramírez-Moreno
Data 2024, 9(4), 47; https://doi.org/10.3390/data9040047 - 27 Mar 2024
Viewed by 1593
Abstract
This dataset was acquired during collaboration and competition tasks performed by sixteen subject pairs (N = 32) of one female and one male under different (face-to-face and online) modalities. The collaborative task corresponds to cooperating to put together a 100-piece puzzle, while the [...] Read more.
This dataset was acquired during collaboration and competition tasks performed by sixteen subject pairs (N = 32) of one female and one male under different (face-to-face and online) modalities. The collaborative task corresponds to cooperating to put together a 100-piece puzzle, while the competition task refers to playing against each other in a one-on-one classic 28-piece dominoes game. In the face-to-face modality, all interactions between the pair occurred in person. On the other hand, in the online modality, participants were physically separated, and interaction was only allowed through Zoom software with an active microphone and camera. Electroencephalography data of the two subjects were acquired simultaneously while performing the tasks. This article describes the experimental setup, the process of the data streams acquired during the tasks, and the assessment of data quality. Full article
(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—2nd Edition)
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