Understanding dropout in distance and online learning by taking into account multiple factors

While extensive research has investigated why students drop out of university, most of this research has focused on campus-based training in the first year of university, or on some of the many elements that influence a student's life and learning pathway. Based on theoretical models of distance education dropout, we identified similar variables to those for on-campus learning but with effects that differ in importance. The objective of this research was to determine whether socio-demographic characteristics (e.g., age, gender, marital and family status), academic variables (e.g., study regime, parents’ levels of education), environmental characteristics (e.g., support from family and friends, financial and work situations), learning strategies (e.g. planning, performance, and reflection), the pedagogical organization of courses (e.g. technological tools, learning activities, and learning aids) and support for learning (e.g. interactions with tutors and peers) influenced students’ propensity to drop a course or their program of study in distance and online learning (DOL). This study used a questionnaire, a course analysis grid, and focus groups. For our sample of 791 students enrolled in a francophone DOL institution in Quebec, Canada, socio-demographic and academic variables largely explained their propensity to drop out. Learning strategies did not seem to be associated with dropping out of the course but were associated with not re-enrolling in the institution. For students who did not re-enrol after two sessions of study, the analysis of learning strategies in relation to socio-demographic, academic, and environmental variables identified thirteen predictive variables. The fewer learning strategies used by a student, as reported in the reflection phase of the study, the greater the likelihood that the student would drop out of their institution. Analyzing courses’ pedagogical organization allowed us to group the courses into five course models; the course model, when taken out of context, could not explain the propensity of students to drop out of a course, but it did contribute when we controlled for the socio-demographic and academic variables of the sample. For example, the study found that marital status and family status are two student-specific factors associated with the risk of course drop-out, but only in courses closer to course type 2 (oriented to formative assessment activities and Web site visits) and 4 (oriented to formative assessment activities and video viewing). For the other types of courses (1, 3 and 5), which are oriented towards reading text and practical exercises, these variables do not play a determining role in explaining dropout.Analyzing learning support showed that the support received is, on the whole, appropriate for the students. However, they are not fully satisfied. Some of the students would like to have more opportunities to interact with tutors in the form of individualized support and with their peers to reduce isolation and study stress. These exchanges would encourage greater perseverance, depending on the family and professional situation of certain students. For example, students who work full time and have a family have less need for interaction in their courses than those who do not work.

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