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Electronics
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Practical Usage of Artificial Intelligence within Online Educational Systems
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Practical Usage of Artificial Intelligence within Online Educational Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 11005

Special Issue Editors

Dr. Cristian Marian Mihaescu

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Guest Editor
Department of Computer Science and Information Technology, University of Craiova, Craiova, Romania
Interests: recommender system; information filtering; Applied Machine Learning; educational data mining
Dr. Vicente Julian Inglada

E-Mail Website
Guest Editor
Valencian Research Institute for Artificial Intelligence (VRAIn), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
Interests: multi-agent systems; agreement technologies; ambient intelligence; affective computing; intelligent transport systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The usage of AI in online educational environments opened the way to many practical implementations and features. Among the most well-known tackled issues, there are predictions of the final grade, prognosis of drop-out, various classification and recommender systems and many other applications. A specific area consists of Natural Language Processing tasks, which started to fully benefit from the latest Deep Learning architectures in the shape of Transformers. Another particular area regards Image Processing, which offers tremendous power to analyze specific data. Most of the AI facets found a way towards specific implementation into the application area of online educational environments to provide a personalized learning experience, better learning outcome, and a shorter and steeper learning curve. We have observed that the latest algorithmic AI progress has been developed into high-quality open-source libraries continuously improved, tested, and integrated into various application domains. This approach opens the way for significant improvements of existing features on even design and implementation of new ones within existing learning environments.

This Special Issue focuses on state-of-the-art AI methods integration into practical applications or features that run into online educational environments. We invite high-quality paper submissions of theoretical and experimental nature on topics including, but not limited to, the following:

  • Intelligent Tutoring Systems
    • Tutoring and self-regulated learning educational technologies
    • Interfaces for Interactive Educational Systems
  • Interpretability in AI for Education
    • Ethics in AI for Education
    • AI for Formative Learning
  • Learning Object Recommendation
  • Personalized Curriculum Generation
    • Deep Learning in Learning Sciences
    • Artificial Intelligence in Remote Learning
  • Experiences with AI Educational Systems
  • Agent-based learning systems
    • Emotional and cognitive agents
    • Storytelling/Narrative Engines
  • Big data analytics for education
  • Immersive learning and multimedia applications
    • Self-adaptive learning
  • Virtual instructors and peers in mixed reality environments

Dr. Cristian Marian Mihaescu
Prof. Dr. Vicente Julian
Guest Editors

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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.

Published Papers (4 papers)

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Research

19 pages, 4745 KiB  
Article
A New Methodological Framework for Project Design to Analyse and Prevent Students from Dropping Out of Higher Education
by Vaneza Flores, Stella Heras and Vicente Julián
Electronics 2022, 11(18), 2902; https://doi.org/10.3390/electronics11182902 - 13 Sep 2022
Viewed by 1293
Abstract
The problem of university dropout is a recurring issue in universities that affects students, especially in the first year of studies. The situation is aggravated by the COVID-19 pandemic, which has imposed a virtual education, generating a greater amount of data in addition [...] Read more.
The problem of university dropout is a recurring issue in universities that affects students, especially in the first year of studies. The situation is aggravated by the COVID-19 pandemic, which has imposed a virtual education, generating a greater amount of data in addition to historical information, and thus, a greater demand for strategies to design projects based on Educational Data Mining (EDM). To deal with this situation, we present a framework for designing EDM projects based on the construction of a problem tree. The result is the proposal of a framework that merges the six phases of the CRISP-DM methodology with the first stage of the Logical Framework Methodology (LFM) to increase university retention. To illustrate this framework, we have considered the design of a project based on data mining to prevent students from dropping out of a Peruvian university. Full article
(This article belongs to the Special Issue Practical Usage of Artificial Intelligence within Online Educational Systems)
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21 pages, 1241 KiB  
Article
Electroencephalogram Signals for Detecting Confused Students in Online Education Platforms with Probability-Based Features
by Talal Daghriri, Furqan Rustam, Wajdi Aljedaani, Abdullateef H. Bashiri and Imran Ashraf
Electronics 2022, 11(18), 2855; https://doi.org/10.3390/electronics11182855 - 9 Sep 2022
Cited by 12 | Viewed by 3051
Abstract
Online education has emerged as an important educational medium during the COVID-19 pandemic. Despite the advantages of online education, it lacks face-to-face settings, which makes it very difficult to analyze the students’ level of interaction, understanding, and confusion. This study makes use of [...] Read more.
Online education has emerged as an important educational medium during the COVID-19 pandemic. Despite the advantages of online education, it lacks face-to-face settings, which makes it very difficult to analyze the students’ level of interaction, understanding, and confusion. This study makes use of electroencephalogram (EEG) data for student confusion detection for the massive open online course (MOOC) platform. Existing approaches for confusion detection predominantly focus on model optimization and feature engineering is not very well studied. This study proposes a novel engineering approach that uses probability-based features (PBF) for increasing the efficacy of machine learning models. The PBF approach utilizes the probabilistic output from the random forest (RF) and gradient-boosting machine (GBM) as a feature vector to train machine learning models. Extensive experiments are performed by using the original features and PBF approach through several machine learning models with EEG data. Experimental results suggest that by using the PBF approach on EEG data, a 100% accuracy can be obtained for detecting confused students. K-fold cross-validation and performance comparison with existing approaches further corroborates the results. Full article
(This article belongs to the Special Issue Practical Usage of Artificial Intelligence within Online Educational Systems)
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10 pages, 363 KiB  
Article
Building and Using Multiple Stacks of Models for the Classification of Learners and Custom Recommending of Quizzes
by Marian Cristian Mihăescu, Paul Ştefan Popescu and Mihai Lucian Mocanu
Electronics 2022, 11(9), 1316; https://doi.org/10.3390/electronics11091316 - 21 Apr 2022
Viewed by 1041
Abstract
Recommending quizzes in e-Learning systems always represents a challenging task, as the quality of recommendations may have a high impact on the student’s progress. We propose a data analysis workflow based on building multiple stacks of models that use information from former students’ [...] Read more.
Recommending quizzes in e-Learning systems always represents a challenging task, as the quality of recommendations may have a high impact on the student’s progress. We propose a data analysis workflow based on building multiple stacks of models that use information from former students’ taken quizzes. The current implementation uses the RandomForest algorithm for building the models on a real-world dataset that has been obtained in a controlled environment. As preprocessing techniques, we have used normalization and discretization such that training data have been used for classification and regression tasks. At run-time, the models are queried for classifying the student and inferring an optimal quiz that is personalized for the student. We have evaluated the accuracy parametrized on the previous number of quizzes and found that a possible optimal timeframe for each class of students should be used and may provide more helpful quizzes. Full article
(This article belongs to the Special Issue Practical Usage of Artificial Intelligence within Online Educational Systems)
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15 pages, 1040 KiB  
Article
Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques
by William Villegas-Ch., Joselin García-Ortiz and Santiago Sánchez-Viteri
Electronics 2021, 10(10), 1192; https://doi.org/10.3390/electronics10101192 - 17 May 2021
Cited by 17 | Viewed by 4448
Abstract
Education is one of the sectors that improves the future of societies; unfortunately, the pandemic generated by coronavirus disease 2019 has caused a variety of problems that directly affect learning. Universities have found it necessary to begin a transition towards remote or online [...] Read more.
Education is one of the sectors that improves the future of societies; unfortunately, the pandemic generated by coronavirus disease 2019 has caused a variety of problems that directly affect learning. Universities have found it necessary to begin a transition towards remote or online educational models. To do so, the only method that guarantees the continuity of classes is using information and communication technologies. The transition in the foreground points to the use of technological platforms that allow interaction and the development of classes through synchronous sessions. In this way, it has been possible to continue developing both administrative and academic activities. However, in effective education, there are factors that create an ideal environment where the generation of knowledge is possible. By moving from traditional educational models to remote models, this environment has been disrupted, significantly affecting student learning. Identifying the factors that influence academic performance has become the priority of universities. This work proposes the use of intelligent techniques that allow the identification of the factors that affect learning and allow effective decision-making that allows improving the educational model. Full article
(This article belongs to the Special Issue Practical Usage of Artificial Intelligence within Online Educational Systems)
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