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https://hdl.handle.net/123456789/1759| 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, Ilias [EL] Παπαδήμας, Χαράλαμπος[EN] Papadimas, Charalampos [EL] Ραγάζου, Βασιλική[EN] Ragazou, Vasiliki |
| 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 education |
| Λέξεις-κλειδιά: | machine learning; raw text features; engineered text features; video lectures; video 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). |
| Εμφανίζεται στις συλλογές: | Ερευνητικές ομάδες |
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