The Covid pandemic prompted a significant move to online training, even within universities. Telematic universities, skilled in emergency response, became valuable models for creating inventive and personalized curricula. Despite the lack of consistent use of online platforms in teaching, the urgency to address the crisis frequently hindered reflective methodologies. Notably, the IUL Telematic University had the chance to consider these aspects since its inception in the 2000s, when discussions on quality standards for telematic universities were prevalent [1]. The teaching model introduced by IUL is grounded in the theoretical framework of the Community of Inquiry (CoI), embodying a collaborative-constructivist approach to learning [2]. Community of Inquiry framework [3], is one of the most widespread in the field of online teaching and emphasizes the importance of interaction between students and teachers during the learning process. It is not only a pedagogical tool, but also research one [4].
The educational success of university students can be assessed in various ways, through grades in exams, through the educational credits obtained, the drop-out of studies, the time elapsed between the end of studies and obtaining the first job.
This study takes place in IUL Telematic University on first-year students of the bachelor's degree program in psychology of the academic year 2020/2021 (n=127). The aim of this work is to verify which variables have the greatest influence on students' educational success. To this end, a multiple binary logistic regression model [5] was used to relate the event to take the exam within the first year, which can be considered as an event that heralds a drop out, with some explanatory variables such as gender, age, number of activities carried out in synchronous and asynchronous mode.
The aim of this study is to demonstrate the importance of interactive activities also within telematic contexts. The results are presented and discussed.
Keywords |
Community Of Inquiry, Learning Analytics, Educational Success, Binary Logistic Regression. |
References |
[1] Ardizzone, P. & Rivoltella, P.C. (2003). Didattiche per l'e-learning. Metodi e strumenti per l'innovazione dell'insegnamento universitario. Carocci. [2] Benedetti, F. (2018). Designing an effective and scientifically grounded e-learning environment for Initial Teacher Education: The Italian University Line Model. Journal of E-Learning and Knowledge Society, 14(2), 97-109. [3] Garrison, D.R. (2009). Communities of inquiry in online learning. Encyclopedia of distance learning, Second edition. IGI Global. (pp. 352-355). [4] Gunawardena, C.N., Flor, N.V., Gomez, D. & Sanchez, D. (2016). Analyzing social construction of knowledge online by employing interaction analysis, learning analytics, and social network analysis. The Quarterly Review of Distance Education, 17(3), 35. [5] Prada Núñez, R., Hernández Suárez, C., Solano-Pinto, N. & Fernández-Cézar, R. (2023). Predictor Variables Of Academic Success In Mathematics Under A Binary Logistic Regression Model. Journal of Positive Psychology and Wellbeing, 7(1), 551-575. |