Les déterminants technologiques de la persévérance des étudiants dans les cours à distance de niveau collégial : Les modalités de cours jouent-elles un rôle?

Abstract

L’objectif de cette étude est d’identifier et d’analyser les déterminants technologiques de la persévérance dans les cours à distance de niveau collégial (n=61), issus du modèle Unified theory of acceptance and use of technology (UTAUT). Les résultats des analyses par équations structurelles (Partial Least Square) indiquent que parmi ces derniers déterminants, seulement les conditions facilitantes ont un impact significatif et positif sur l’intention comportementale d’utiliser les technologies des cours à distance (R2=54%) définie comme l’intention de l’étudiant de réaliser ce comportement, qui a son tour a un effet significatif et positif sur la persévérance, définie par l’intention de finir le cours à distance auquel l’étudiant est inscrit (R2=14,2%) et par l’intention de s’inscrire dans le futur dans d’autres cours à distance (R2=65%). Les analyses des ANOVA font ressortir des différences significatives entre les groupes d’étudiants assignés à des modalités différentes de cours à distance sur tous les facteurs technologiques laissant présager que des analyses différenciées, selon la modalité de cours, devraient être envisagées dans le futur.
 This study aims to identify and analyze the technological determinants of persistence in college distance education courses (N=61), derived from the Unified theory of acceptance and use of technology (UTAUT) model. The results of the structural equation analyses (Partial Least Square) revealed that among these determinants, only facilitating conditions have a significant and positive impact on the behavioural intention to use distance learning technologies (R2 = 54%) defined as the student’s intention to display this behaviour. Moreover, behavioral intention to use distance learning technologies has a significant and positive effect on persistence, defined as the intention to finish the distance education course in which the student is enrolled (R2 = 14.2 %) and the intention to enroll in other distance education courses in the future (R2 = 65%). The ANOVA analyses revealed significant differences between the groups of students assigned to different courses delivery modes on all the technological factors, suggesting that differentiated analyses, depending on the course delivery mode, should be performed in the future.

Explaining persistence in online courses in higher education: a difference-in-differences analysis

“…This model is selected here because it includes technological factors that are different and complementary to other explicative models for persistence in online courses. Some factors of this model, partially present in the earlier models, have been found to be significant in explaining persistence in online courses: performance expectancy and effort expectancy (Joo et al 2011), social influence (Kember 1989;Park 2007;Rovai 2003) and facilitating conditions (Lakhal, 2019). According to Venkatesh et al (2003), the UTAUT model should be enriched by additional factors such as individual constructs.…”

“…Yet in these models, technological factors are little, or not at all, taken into account. Digital technologies play a significant role in online courses, notably in regards to their implementation, and these technological factors may play an important role in the decision to persist in or drop out from online courses (Hachey et al 2014;Lakhal and Bazinet 2015;Lakhal 2019). Indeed, some studies identified by Lakhal and Bazinet (2015) indicate that the use of technologies (prior experience with the technologies, the variety of technology used, the use of social networks, etc.)…”

“…The model used in this study to encompass factors of student persistence in online courses is that of Venkatesh et al (2003), known as the Unified Theory of Acceptance and Use of Technology model (UTAUT model), which is enriched with other factors borrowed from other models (Kember 1989;Park 2007;Rovai 2003). The UTAUT model represents a synthesis of several older models and theories and was validated in several studies within different contexts (other than persistence in online courses), including that of education (Khechine and Lakhal 2018;Lakhal andKhechine 2016, 2017;Lakhal et al 2013). This model is selected here because it includes technological factors that are different and complementary to other explicative models for persistence in online courses.…”

“…The study's objective is to identify and analyse the factors of persistence in online courses in higher education using Venkatesh et al 's (2003) UTAUT model to which anxiety was added, enriched by factors relating to student characteristics and to the specificities of online courses (Kember 1989;Park 2007;Rovai 2003). Very few studies, to our knowledge, have used the model developed by Venkatesh et al (2003) to identify factors of student persistence in online courses in higher education (Chiu and Wang 2008;Lakhal 2019;Lin 2011;Lin et al 2013). Previous studies has mostly used other models and other factors (Lee and Choi 2011;Park 2007).…”

A Continuum of Blended and Online Learning

“…Thanks to ICT, these delivery modes offer advantages both to students and to postsecondary institutions. On the one hand, depending on their design, blended and online courses have the potential to meet the demands of students who desire flexible course schedules, such as adult students, because of family and/or work responsibilities (Lakhal, 2019). They also give students better access to higher education, especially in large countries such as Canada where distances can be quite significant between students' homes and their colleges or universities (Lakhal et al, 2020), and they allow both synchronous and asynchronous contacts between students and with the instructor, even if students would not need to travel as much to attend face-to-face sessions (Cidral et al, 2018).…”