Abstract
This study investigates how autonomy-supportive practices, real-time data collection, and hands-on analysis can enhance student engagement and deepen conceptual understanding in a hybrid statistics unit co-taught to undergraduate and postgraduate students. Conducted over two semesters, the project engaged on-campus, online, and self-paced learners, incorporating student-generated data into weekly learning activities. It also examines whether students’ chosen mode of participation correlates with academic performance, offering insight into how autonomy influences learning outcomes.
Each week during the optional drop-in sessions for the on-campus and online cohorts, students completed an anonymous online questionnaire aligned with the statistical concept being taught. The collected data were cleaned collaboratively at the start of each session and analysed in real time using the week’s topic. This hands-on process allowed students to engage with the full data cycle – from collection to cleaning, analysis, and interpretation. Self-paced students accessed the same materials through recordings and step-by-step resources.
This approach was designed to foster student ownership, motivation, and real-world application of statistical knowledge. In Week 13, 107 out of 210 enrolled students (49% response rate) reflected on the experience. Preliminary analysis of this feedback indicates that students perceived the live data collection and analysis as “interactive”, “transparent”, and “practical”, with many reporting increased confidence in using statistical software and applying common tests. Several students highlighted that seeing their own data analysed helped demystify complex concepts and reinforced their learning through relevance. Suggestions for improvement included diversifying datasets, enhancing prompts to encourage greater participation, and providing clearer access to data.
By integrating autonomy, interactivity, and student voice, this project presents a scalable and inclusive model for teaching quantitative methods. It illustrates how live, student-generated data can serve not only as a pedagogical tool but as a catalyst for deeper engagement, conceptual understanding, and practical skill development across varied learning contexts.
Each week during the optional drop-in sessions for the on-campus and online cohorts, students completed an anonymous online questionnaire aligned with the statistical concept being taught. The collected data were cleaned collaboratively at the start of each session and analysed in real time using the week’s topic. This hands-on process allowed students to engage with the full data cycle – from collection to cleaning, analysis, and interpretation. Self-paced students accessed the same materials through recordings and step-by-step resources.
This approach was designed to foster student ownership, motivation, and real-world application of statistical knowledge. In Week 13, 107 out of 210 enrolled students (49% response rate) reflected on the experience. Preliminary analysis of this feedback indicates that students perceived the live data collection and analysis as “interactive”, “transparent”, and “practical”, with many reporting increased confidence in using statistical software and applying common tests. Several students highlighted that seeing their own data analysed helped demystify complex concepts and reinforced their learning through relevance. Suggestions for improvement included diversifying datasets, enhancing prompts to encourage greater participation, and providing clearer access to data.
By integrating autonomy, interactivity, and student voice, this project presents a scalable and inclusive model for teaching quantitative methods. It illustrates how live, student-generated data can serve not only as a pedagogical tool but as a catalyst for deeper engagement, conceptual understanding, and practical skill development across varied learning contexts.
| Original language | English |
|---|---|
| Pages | 1 |
| Number of pages | 1 |
| Publication status | Accepted/In press - Dec 2025 |
| Event | The Australian Statistical Conference 2025 - Curtin University, Perth, Australia Duration: 1 Dec 2025 → 5 Dec 2025 https://www.asc2025.net/ |
Conference
| Conference | The Australian Statistical Conference 2025 |
|---|---|
| Abbreviated title | ASC2025 |
| Country/Territory | Australia |
| City | Perth |
| Period | 1/12/25 → 5/12/25 |
| Internet address |