Taking adaptive learning in educational settings to the next level: leveraging natural language processing for improved personalization

Abstract

Educational technology plays an increasingly significant role in supporting Self-Regulated Learning (SRL), while the importance of Adaptive Learning Technology (ALT) grows due to its ability to provide personalized support for learners. Despite recognizing the potential of ALT to be influential in SRL, effectively addressing pedagogical concerns about using ALT to enhance students’ SRL remains an ongoing challenge. Consequently, learners can develop perceptions that ALT is not customized to their specific needs, resulting in critical or dismissive attitudes towards such systems. This study therefore explores the potential of combining Natural Language Processing (NLP) to enhance real-time contextual adaptive learning within an ALT to support learners’ SRL. In addressing this question, our approach consisted of two steps. Initially, we focused on developing an ALT that incorporates learners’ needs. Subsequently, we explored the potential of NLP to capture pertinent learner information essential for providing adaptive support in SRL. In order to ensure direct applicability to pedagogical practice, we engaged in a one-year co-design phase with a high school. Qualitative data was collected to evaluate the implementation of the ALT and to check complementary possibilities to enhance SRL by potentially adding NLP. Our findings indicate that the learning technology we developed has been well-received and implemented in practice. However, there is potential for further development, particularly in terms of providing adaptive support for students. It is evident that a meaningful integration of NLP and ALT holds substantial promise for future enhancements, enabling sustainable support for learners SRL.