SELF-DETERMINED LEARNING IN HIGHER EDUCATION IN KAZAKHSTAN: MULTIPLE CASE STUDY OF ONLINE LIFELONG LEARNING

Abstract

The purpose of this qualitative multiple case study was to investigate the phenomenon of self-determined learning within the implementation of online teaching and learning in two higher education institutions located in one city of Kazakhstan. In this thesis, the terms “Heutagogy” and “Self-determined learning” are used interchangeably to mean the development of learner autonomy and self-directedness within the implementation and integration of emerging technologies and pedagogies in teaching and learning (Blaschke, 2012). Heutagogical theory promotes the development of self-determined lifelong learners based on heutagogical design principles, such as exploration, creation, collaboration, connection, reflection, and knowledge sharing (Blaschke, 2013; Hase, 2016). This multiple case study focuses on the point of intersection between online learning, self-determined learning, and lifelong learning, and comprises two cases: University A with its fully online learning environment and University B with its blended learning environment. Participants were five university faculty members, five instructional designers/instructional technologists, and eight Master’s students. The findings reveal the critical role of learning facilitation in linking online learning, self-determined learning, and lifelong learning. This study makes a theorical contribution by identifying this connection as a core component within self-determined learning. It demonstrates how simultaneous trends in student-centered learning and online learning create a synergy that facilitates heutagogical learning. Additionally, it advances the understanding of lifelong learning in Kazakhstani higher education institutions by fostering learner agency and skills development within facilitative online learning environments. The key contribution is the solution it provides for policy and practice in the field of online learning in higher education. It also suggests the need for future research to explore heutagogical design principles in AI-powered learning environments, involving instructional designers, instructors, and students in higher education.