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Ηλεκτρονική μάθηση και αξιολόγηση στην εκπαίδευση μηχανικών: καινοτόμες προσεγγίσεις μέσω τεχνητής νοημοσύνης και εξόρυξης εκπαιδευτικών δεδομένων ; E-learning and e-assesment in engineering education: innovative approaches through artificial intelligence and educational data mining

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Bibliographic Details
Title: Ηλεκτρονική μάθηση και αξιολόγηση στην εκπαίδευση μηχανικών: καινοτόμες προσεγγίσεις μέσω τεχνητής νοημοσύνης και εξόρυξης εκπαιδευτικών δεδομένων ; E-learning and e-assesment in engineering education: innovative approaches through artificial intelligence and educational data mining
Authors: Charitopoulos, Angelos, Χαριτόπουλος, Άγγελος
Publisher Information: Democritus University of Thrace (DUTH)
Δημοκρίτειο Πανεπιστήμιο Θράκης (ΔΠΘ)
Publication Year: 2025
Collection: National Archive of PhD Theses (National Documentation Centre Greece)
Subject Terms: Ηλεκτρονική μάθηση, Ηλεκτρονική αξιολόγηση, Εκπαίδευση μηχανικών, Σύστημα Διαχείρισης Μάθησης moodle, Soft Computing, Αλγόριθμοι μηχανικής μάθησης, Τεχνητά νευρωνικά δίκτυα, Εξόρυξη Εκπαιδευτικών Δεδομένων, Εξόρυξη Εκπαιδευτικού Κειμένου, Αλγόριθμος Latent Dirichlet Allocation, E-learning, e-assessment, Engineering education, LMS moodle, Machine learning algorithms, Artificial neural networks, Educational data mining, Educational Text Mining, Latent Dirichlet Allocation Algorithm, Επιστήμη Ηλεκτρολόγου Μηχανικού, Ηλεκτρονικού Μηχανικού, Μηχανικού Η/Υ, Επιστήμες Μηχανικού και Τεχνολογία, Ηλεκτρική και Ηλεκτρονική μηχανική, Electrical Engineering, Electronic Engineering, Information Engineering, Engineering and Technology, Electrical and Electronic Engineering
Description: This PhD thesis examines the affordances and opportunities that digital educational technologies offer for higher quality and more effective education, with emphasis on the introduction and utilization of Artificial Intelligence (AI) and Machine Learning (ML) methods and algorithms. The thesis focuses on higher education, with a specific, though not exclusive, interest in engineering education. First, the use of AI/ML methods in the field of educational technologies is explored through a literature review. On the basis of results of this review, the thesis focuses on specific educational problems. To address these problems, experimental educational activities are designed, developed and evaluated using real data. Finally, the thesis concludes by suggesting directions for future research. From the point of view of the AI/ML algorithms selected and used in the thesis, the following can be distinguished (a) data mining algorithms, which analyse educational data in their more classical form (numerical, quantitative, time stamps, student-platform interaction event counts, user activity counts, etc.) and (b) text mining algorithms, which analyse educational texts (of sufficient length; not short texts such as social network posts) based on Natural Language Processing (NLP) methods. The thesis consists of seven chapters. The first chapter presents the evolution of e-learning and e-assessment, the basic learning theories (behaviourism, constructivism, socio-cultural theories) and the platforms that support e-learning and e-assessment, such as Moodle and Massive Open Online Courses (MOOCs). It also examines the advantages, disadvantages and challenges of e-learning and e-assessment. The second chapter reviews the international literature on the use of Educational Data Mining (EDM) and Learning Analytics (LA) methods in e-learning and e-assessment. The algorithms of the broader family of Soft Computing, used for educational data analysis, are reviewed, along with research trends and future challenges in the field. Chapter ...
Document Type: doctoral or postdoctoral thesis
Language: Greek, Modern (1453-)
Relation: https://hdl.handle.net/10442/hedi/59341
DOI: 10.12681/eadd/59341
Availability: https://hdl.handle.net/10442/hedi/59341
https://doi.org/10.12681/eadd/59341
Accession Number: edsbas.DC133177
Database: BASE
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