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Τύπος: Άρθρο σε επιστημονικό περιοδικό
Τίτλος: Student-generated texts as features for predicting learning from video lectures: an initial evaluation
Συγγραφέας: [EL] Καρασαββίδης, Ηλίας[EN] Karasavvidis, Iliassemantics logo
[EL] Παπαδήμας, Χαράλαμπος[EN] Papadimas, Charalampossemantics logo
[EL] Ραγάζου, Βασιλική[EN] Ragazou, Vasilikisemantics logo
Ημερομηνία: 13/05/2022
Περίληψη: The digital trails that students leave behind on e-learning environments have attracted considerable attention in the past decade. Typically, some of these traces involve the production of different kinds of texts. While students routinely produce a bulk of texts in online learning settings, the potential of such linguistic features has not been systematically explored. This paper introduces a novel approach that involves using student-generated texts for predicting performance after viewing short video lectures. Forty-two undergraduates viewed six video lectures and were asked to write short summaries for each one. Five combinations of features that were extracted from these summaries were used to train eight machine learning classifiers. The findings indicated that the raw text feature set achieved higher average classification accuracy in two video lectures, while the combined feature set whose dimensionality had been reduced resulted in higher classification accuracy in two other video lectures. The findings also indicated that the Gradient Boost, AdaBoost and Random Forest classifiers achieved high average performance in half of the video lectures. The study findings suggest that student-produced texts are a very promising source of features for predicting student performance when learning from short video lectures.
Γλώσσα: Αγγλικά
Σελίδες: 25
ISSN: 2585-3856
Θεματική κατηγορία: [EL] Ανώτατη εκπαίδευση[EN] Higher educationsemantics logo
Λέξεις-κλειδιά: machine learningraw text featuresengineered text featuresvideo lecturesvideo learning analytics
Κάτοχος πνευματικών δικαιωμάτων: © 2022 The Author(s)
Διατίθεται ανοιχτά στην τοποθεσία: https://files.eric.ed.gov/fulltext/EJ1356021.pdf
https://www.researchgate.net/publication/361218115_Student-generated_texts_as_features_for_predicting_learning_from_video_lectures_An_initial_evaluation
Ηλεκτρονική διεύθυνση περιοδικού: http://ouranos.edu.uoi.gr/tel/index.php/themeselearn/about
Τίτλος πηγής δημοσίευσης: Themes in e-Learning
Τεύχος: 15
Σελίδες τεκμηρίου (στην πηγή): 21-45
Σημειώσεις: This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme "Human Resources Development, Education and Lifelong Learning 2014-2020" in the context of the project “Planning, Development and Deployment of an Intelligent Feedback System Using Supervised and Unsupervised Machine Learning Methods” (MIS 5048955).
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