The Tensions Between Student Dropout and Flexibility in Learning Design: The Voices of Professors in Open Online Higher Education

Abstract

Flexibility is typical of open universities and their e-learning designs. While this constitutes their main attraction, promising learners will be able to study “anytime, anyplace,” this also demands more self-regulation and engagement, a cause for student dropout. This case study explores professors’ experiences of flexibility in e-learning design and continuous assessment and their perception of the risks and opportunities that more flexibility implies for student persistence and dropout. In-depth interviews with 18 full professors, who are the e-learning designers of undergraduate courses at the Open University of Catalonia (UOC), were analyzed, employing qualitative content analysis. According to the professors, the main causes for dropout are student-centered, yet they are connected to learning design: workload and time availability, as well as students’ expectations, profiles, and time management skills. In the professors’ view, flexibility has both positive and negative effects. Some are conducive to engagement and persistence: improvement of personalized feedback, formative assessment, and module workload. Others generate resistance: more flexibility may increase workload, procrastination, dropout, and risk of losing professorial control, and may threaten educational standards and quality. Untangling the tensions between dropout and flexibility may enhance learning design and educational practices that help prevent student dropout. Stakeholders should focus on measures perceived as positive, such as assessment extension, personalized feedback and monitoring, and course workload calibration. As higher education is globally turning to online delivery due to the COVID-19 viral pandemic, such findings may be useful in both hybrid and fully online educational contexts.

Student performance in online learning higher education: A preliminary research

“…It can be interpreted that students who attend lectures using the online learning method view all exogenous constructs as essential to improving their performance. Our results align with previous literature, which explains that the performance of students participating in online learning programs is still less than optimal , and there are still students who are unwilling to continue their studies (Xavier and Meneses, 2021). Students are unfamiliar with online learning systems and are used to traditional pedagogical styles (Maheshwari, 2021).…”

Predicting Drop-Out Intentions From Students' Perceptions of Academic Feedback and Procrastination Tendencies. 'Can Personalized Feedback Sometimes Be Too Much of a Good Thing?'

“…Finally, our research had several methodological and conceptual limitations. First, methodologically, as mentioned above, our questionnaire did not capture the many intricacies of personalized feedback or the sensitive nature of properly balanced AF (Xavier & Meneses, 2021). Second, conceptually, and with relevance for the research design, future development will need to account for learning disabilities, as well as for more precise separation (i.e., operationalization) of the various facets of feedback.…”

Personalized Education for All: The Future of Open Universities

“…This diverse group of students in OUs reports a range of difficulties related to their individual characteristics, lack of prior knowledge and technical skills, work and life environments, unsuitable course requirements, and inadequate tutor support (Kara et al, 2019). While OUs have strived to address this wide range of difficulties of their students, their students still report a mismatch between levels of their academic readiness and curriculum difficulty (Muljana & Luo, 2019), a mismatch between personal career objectives and curriculum (Gunduz & Karaman, 2020), lack of motivation and engagement in the learning process (Eliasquevici et al, 2017), dissatisfaction with insufficient and nontailored support services (Aberra & Davids, 2022), and new challenges faced by disadvantaged students (Lee et al, 2023), all resulting in low rates of retention and completion in ODE (Gunduz & Karaman, 2020;Xavier & Meneses, 2021). Several studies (e.g., Eliasquevici et al, 2017;Martínez-Carrascal et al, 2023;Nichols, 2010;Stewart et al, 2013) reveal that customized learning trajectories coming with individualized support and attention and considering learners' unique demographics and needs are the answer to address the above-reported difficulties of ODE students, which are all features of personalized education.…”