Acasă Articole RTR Integrarea atributelor emoțional lingvistice în arhitecturile eLearning

Integrarea atributelor emoțional lingvistice în arhitecturile eLearning


Integrating emotional linguistic attributes in eLearning designs

Recent advancements in big data exploration and affective computing open new avenues for the improvement of curriculum design. The paper places forward an argument for measuring emotional attributes within the construct of class forum discussions facilitated by educational platforms. The psycholinguistic features of the user can be translated into key performance indicators and be integrated into a learning analytics business model. Such a design can monitor the optimal threshold levels of academic achievement as well as signal the potential anomalies. The architecture can support the development of adaptive and personalized learning models based on the psycholinguistic profile of the student. Such a design could render competitive advantages both on the student retention level as well as on the student enrollment strategy.

Keywords: personalized learning, adaptive learning, educational data mining, curriculum design, learning analytics, educational performance prediction, key performance indicators


Aiello Luca Maria; R. Alhajj & J. Rokne Eds., Encyclopedia of Social Network Analysis and Mining, Nokia Bell Labs, Springer Science+Business Media LLC, Cambridge, UK, 2017, p. 1-16.
Alismail Sarah, Zhang Hengwei, The Use of Emoji in Electronic User Experience Questionnaire: An Exploratory Case Study, Proceedings of the 51st Hawaii International Conference on System Sciences, 2018, p. 3366 – 3375.
Aulck Lovenoor, Nambi Dev, West Jevin, Using Machine Learning and Genetic Algorithms to Optimize Scholarship Allocation for Student Yield. In SIGKDD ’19: ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 4–8, 2019, Anchorage, AK. ACM, New York, NY, USA, p. 1-9.
Bohne T., S. Rönnau, and U. M. Borghoff, Efficient keyword extraction for meaningful document perception, in Proceedings of the 11th ACM symposium on Document engineering – DocEng ’11, Mountain View, California, USA, 2011, p. 185-190.
Coanca Mariana, Features of Smart Learning, Journal of Information Systems & Operations Management, The Proceedings of Journal ISOM Vol. 11, Nr. 2 p. 328 – 337.
Cosyn Eric Emile, Matayoshi Jeffrey Seiichi (Inventors), Negative Learning Behavior Alert System, United States Patent Application Publication, Pub. No.: US 2019 / 0206271 A1, Jul. 4, 2019, p.1-14.
Drăgulescu Radu, Online Media and New Technologies in Teaching Linguistic Disciplines, Proceedings of the International Conference Globalization, Intercultural Dialogue and National Identity. Section: Language and Discourse, 1, Arhipelag XXI Press, Tîrgu-Mureş, 2014, p. 134-145.
Drăgulescu Radu, Psycholinguistic and Neurolinguistic Approaches on Communicational Distorsions, The Proceedings of the International Conference Globalization, Intercultural Dialogue and National Identity. Section: Language and Discourse, 1, Arhipelag XXI Press, Tîrgu-Mureş, 2014, p. 95-109.
Drăgulescu Radu, Qualitative Research on Learning Romanian as a Foreign Language in Endo-Linguistic Context, Lucian Blaga University of Sibiu, Revista Transilvania, Ianuarie, 2019, p. 73-81.
Du Xiaofeng, Research on the Innovation of Teaching Content Mining under the Background of Informatization, 1st International Education Technology and Research Conference, Francis Academic Press, UK, 2019, p. 780-783.
Gkontzis Andreas, Karachristos Christoforos, Lazarinis Fotis, Stavropoulos Elias, Verykios Vassilios, Assessing Student Performance by Learning Analytics Dashboards, Proceedings of the 9th International Conference in Open & Distance Learning, Nov. 2017, Athens, Greece, p. 101 – 115.
Ipekel Ilknur Izgi, Harun Şahin, Hidden Curriculum Scale in Teacher Education: a Scale Development Study, European Journal of Education Studies – Volume 6, Issue 4, 2019, p. 323-338.
Jayaprakash Sujith, Jaiganesh V., A Survey on Academic Progression of Students in Tertiary
Education using Classification Algorithms, International Journal of Engineering Technology Science and Research, Vol. 5, Iss. 2, February 2018, p. 136-142.
Kratzwald Bernhard, Ilic Suzana, Kraus Mathias, Feuerriegel Stefan, Prendinger Helmut, Decision support with text-based emotion recognition: Deep learning for affective computing, arXiv:1803.06397v3 [cs.CL] 26 Mar. 2018, p. 1 – 34.
Machashtchik P., Britchenko I., Social investments as a contribution to SMEs development – Prospects of innovative technologies into educational system introduction, Researchgate, 2018, p. 161-173.
Mah Dana-Kristin, Ifenthaler Dirk, Students’ perceptions toward academic competencies: The case of German first-year students, Issues in Educational Research, 28(1), 2018, p. 120 – 137.
McRae Karen, Odeh Saad, Diao Mingming, McNeill Margot, Institutional-wide curriculum change in higher education, 40th Annual Conference of the Higher Education Research, Sydney, June, 2017, p. 1 – 23.
Priyambada Satrio Adi, Mahendrawathi ER, Yahya Bernardo Nugroho, Curriculum Assessment of Higher Educational Institution Using Aggregate Profile Clustering, 4th Information Systems International Conference 2017, ISICO 2017, 6-8 November 2017, Bali, Indonesia, Procedia Computer Science, 124 (2017), p. 264 – 273.
Raga Rodolfo C. Jr, Monitoring ClassActivity and Predicting Student Performance Using Moodie Action Log Data, 1st International Conference on Redesigning, Re-engineering Academic Direction for Global Competitiveness, International Journal of Computing Sciences Research, Vol. 1, Nr. 3, p. 1-16.
Rifat Md Rifatul Islam, Educational Performance Analytics of Undergraduate Business Students, I.J. Modern Education and Computer Science, July 2019, Vol. 7, p. 44-53.
Rose S., D. Engel, N. Cramer, and W. Cowley, Automatic Keyword Extraction from Individual Documents, in Text Mining, M. W. Berry and J. Kogan, Eds. Chichester, UK: John Wiley & Sons, Ltd, 2010, pp. 1–20.
Saqr Mohammed, Fors Uno, Tedre Matti, Nouri Jalal, How social network analysis can be used to monitor online collaborative learning and guide an informed intervention, PLOS ONE, Mar. 22, 2018, p. 1 – 22.
Segundo Manuel Paz San, The Digital University: Information Security and Transparency, Journal of Information Systems & Operations Management, Editura Universitară București, The Proceedings of Journal ISOM Vol. 11, Nr. 2 p. 254 – 263.
Sharma Kshitij, Papamitsiou Zacharoula, Giannakos Michail, Building pipelines for educational data using AI and multimodal analytics: A “grey-box” Approach, British Journal of Educational Technology, 2019, p. 1-28.
Tarigan Timanta, Jaya Ivan, Zamzami M. Elvyawati, Hardi Sri Melvani, Keyword Based System to Enhance the Efficiency of Student’s Performance Report in Computer Science Education, The 3rd International Conference on Computing and Applied Informatics, June 20189 IOP Conf. Series: Journal of Physics: Conf. Series 1235 (2019) 012090, p. 1-5.
Tortoreto Giuliano, Stepanov A. Evgeny, Cervone Alessandra, Dubiel Mateusz, Riccardi Giuseppe, Affective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups more Therapeutic than Twitter? Proceedings of the 4th Social Media Mining for Health Applications, Association for Computational Linguistics, Florence, Italy, August 2, 2019, p. 79 – 88.