Elevating Student Engagement through Real-Time Data Collection, Hands-On Activities, and Self-Directed Learning.

Focus: This study aims to investigate the impact of supportive practices, such as real-time data collection and interactive activities, on enhancing student engagement during drop-in sessions. A blended learning model with pre-recorded lecture segments promotes active participation and self-directed learning (Khan et al., 2022), aligning with contemporary educational trends.
Background/context: Recent literature highlights the importance of flexible learning in higher education, showing that active learning and student autonomy boost academic performance and engagement (Tsai et al., 2023). Research confirms that empowering students to control their learning increases motivation and improves both cognitive outcomes (e.g., learning and knowledge retention) and behavioural outcomes (e.g., engagement and participation) (Ryan and Vansteenkiste, 2023).
Description: This investigation focuses on a statistics unit at the university of XX where students are grouped into on-campus, online, or self-paced categories based on participation in drop-in sessions. Weekly engagement data were collected via online forms and real-time polls to assess trends and their impact on academic performance. This data-driven approach evaluates how supportive practices enhance student engagement during drop-in sessions, providing insights into the effectiveness of various engagement strategies in enhancing learning outcomes.
Method: Quantitative data on attendance and engagement were gathered weekly, along with qualitative feedback from open-ended surveys. Statistical analyses will examine the relationships between participation formats and academic outcomes and explore whether effective feedback practices enhance student experiences.
Evidence: Preliminary findings from the analysis phase are expected to be presented at the HERDSA conference. These results aim to explore patterns between student engagement and academic performance, contributing valuable insights to the field.
Contribution: This study contributes to theoretical understanding of student engagement by proposing a flexible, data-driven model that encourages participation without mandating attendance. By analysing the connection between real-time engagement and academic performance, it provides practical insights for designing enriching and autonomous learning environments in higher education.
Engagement: The presentation will feature dynamic visuals illustrating student engagement trend and conclude with the question: "How do you adapt your teaching practices to promote autonomous learning?"