Individualizing Learning Pathways with Adaptive Learning Strategies: Design, Implementation and Scale

Abstract

Individual undergraduate learners have heterogeneous knowledge backgrounds and undergo diverse learning experiences during their university studies. Consequently, designs of virtual learning environments should adjust to learners’ needs and competencies, especially in the current pandemic crisis. This paper discusses pedagogical aspects of personalized and self-regulated learning and situates its focus on design, implementation, and scale of e-content and e-activities for individualized learning pathways (ILP). Characteristics of ILP such as shape, length, and turning points enabled through adaptive features of existing Learning Management Systems (LMS) have seldom been discussed in the literature. We tackle this issue from a didactical perspective of microlearning with regards to three adaptive learning strategies: 1) Feedback Adaptations, 2) Task Design, and 3) Task Sequence Design. Within a first phase of a complete initial Design Research (DR) cycle, we have collected and analysed data which enable us to generate, cluster and label queries and differentiated items for each of the three strategies. Further on, we offer a visualization of possible ILP illustrated with contextual examples of productive, technology-based task and feedback designs applicable and scalable in higher education settings.