ESRC International Distance Education and African Students Advisory Panel Meeting
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ESRC International Distance Education and African Students Advisory Panel Meeting
- 1. Working together on using learning analytics and learning design to improve (international) student outcomes UNISA 24 January 2017 Ashley Gunter, Clare Madge, Jenna Mittelmeier, Paul Prinsloo, Parvati Raghuram, Katharine Reedy, Jekaterina Rogaten, Bart Rienties
- 2. (Social) Learning Analytics “LA is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” (LAK 2011) Social LA “focuses on how learners build knowledge together in their cultural and social settings” (Ferguson & Buckingham Shum, 2012)
- 3. Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and Implementation of a Learning Analytics Toolkit for Teachers. Journal of Educational Technology & Society, 15(3), 58-76.
- 4. Performance (e.g., Grade, Adjustment, GPA) Time A-student B-student C-student A vast body of research shows that Affective, Behavioural, and Cognitive factors (Searle and Ward, 1990; Jindal- Snape & Rienties, 2016) influence academic and social adjustment over time, which in turn predicts learning outcomes (Crede et al. 2012; Rienties et al. 2012). Some students develop appropriate ABC and ac + soc. Adjustment strategies and become “A-students”, others progress reasonably well (B-student) and some students drop out over time (C-student).
- 5. What predicts (international) student progression? Input Process Output Learner characteristics (incl. prior education, gender, cultural background) Academic adjustment (incl. personal-emotional adjustment, attachment to institute) Social adjustment (incl. study support, satisfaction with social Environment, financial support) Family characteristics (incl. support, finance, child- care) Learning design (incl. assessment, learning materials, communication) Engagement with learning (incl. VLE engagement, attending sessions, submitting assignments, social media) Academic performance over time (incl. grades, credits, GPA) Degree outcomes (incl. Employment, migration, etc)
- 6. Three-level Growth Curve Model Level 1 Level 2 Level 3 TMA1 Student1 TMA3 TMA1 TMA2 TMA3 TMA1 TMA2 TMA3TMA2 Student2 Student3 Module1 Module2 TMA1 TMA2 TMA3 Student4 TMA1 TMA2 TMA3 Student5 Module3 Rogaten, J., Rienties, B, Whitelock, D. (2016). Assessing learning gains, TEA Conference, Tallinn, Estonia VLE VLEVLE
- 7. Participants 11,909 Social Science students of whom 72% were females and 28% were males with average age of M = 30.6, SD = 9.9 5,791 Science students of whom 58.2% were females and 41.8% were males with average age of M = 29.8, SD = 9.6. Measures Tutor Marked Assessments (TMA) Socio demographics (gender, ethnicity, prior educational qualification) Across 111 modules Rogaten, J., Rienties, B, Whitelock, D. (2016). Assessing learning gains, TEA Conference, Tallinn, Estonia
- 8. Descriptive statistics: Social Science Trellis plot Students’ growth-curve Modules’ growth-curve Rogaten, J., Rienties, B, Whitelock, D. (2016). Assessing learning gains, TEA Conference, Tallinn, Estonia
- 9. Descriptive statistics: Science Trellis plot Students’ growth-curve Modules’ growth-curve Rogaten, J., Rienties, B, Whitelock, D. (2016). Assessing learning gains, TEA Conference, Tallinn, Estonia
- 10. The 3-level model accounted for total of: 6% and 33% of variance in students initial scores 19% and 26% of variance in students subsequent learning gains Socio-demographic variables are strong predictors of variance in initial achievements and also in subsequent learning gains Main effect of socio-demographic variables and Interaction between TMAs socio-demographic variables showed that single most important predictors of initial achievements and growth were ethnicity and prior education level (White students with A levels show high initial achievements and subsequent high learning gain) Rogaten, J., Rienties, B, Whitelock, D. (2016). Assessing learning gains, TEA Conference, Tallinn, Estonia
- 12. Toetenel, L. & Rienties, B. (2016). Analysing 157 Learning Designs using Learning Analytic approaches as a means to evaluate the impact of pedagogical decision-making. British Journal of Educational Technology.
- 14. Method – data sets • Combination of four different data sets: • learning design data (189 modules mapped, 276 module implementations included) • student feedback data (140) • VLE data (141 modules) • Academic Performance (151) • Data sets merged and cleaned • 111,256 students undertook these modules
- 15. Constructivist Learning Design Assessment Learning Design Productive Learning Design Socio-construct. Learning Design VLE Engagement Student Satisfaction Student retention 150+ modules Week 1 Week 2 Week30 + Rienties, B., Toetenel, L., (2016). The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across 151 modules. Computers in Human Behavior, 60 (2016), 333-341 Communication
- 16. Performance (e.g., Grade, Adjustment, GPA) Months A-student B-student C-student 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Interviews Interviews Interviews 1 2 2 2 1 = Instrument on students’ first weeks (e.g. welcoming, internet access) 2= Measurement of ac + soc. Adjustment (e.g., SACQ) 3= Fine-grained interviews unpacking why some students become A, others B, and others C 3 3 3
- 17. Working together on using learning analytics and learning design to improve (international) student outcomes UNISA 24 January 2017 Ashley Gunter, Clare Madge, Jenna Mittelmeier, Paul Prinsloo, Parvati Raghuram, Katharine Reedy, Jekaterina Rogaten, Bart Rienties
Editor's Notes
- Level 1 – TMA: repeated measures on students and tell us about students learning trajectory Level 2 – student: between students variations Level 3 – module: between course variation
- Based on the results of these two basic three-level growth-curve models, there is a potential ceiling effect on Student and Module levels in Science, and on Module level in Social Science. Thus, comparing Figures 2 and 3 it is noticeable that in Social Science there is a fanning out in students’ predicted growth curves, which indicates that over a period of a semester students with initial high achievements showed increase in their subsequent achievements, while students with initial low achievements showed drop in their subsequent achievements. That was not the case for Science students where students with initial high achievements had lower subsequent achievements than students who had initially low achievements and gradually obtained better grades. On the module level, Social Science modules showed strong fanning in, whereas it was less noticeable in Science modules. This indicates that Social Science students vary much stronger in their assessment results that Science students do.
- Learning Design Team has mapped 100+ modules
- For each module, the learning design team together with module chairs create activity charts of what kind of activities students are expected to do in a week.
- 5131 students responded – 28%, between 18-76%
- Cluster analysis of 40 modules (>19k students) indicate that module teams design four different types of modules: constructivist, assessment driven, balanced, or socio-constructivist. The LAK paper by Rienties and colleagues indicates that VLE engagement is higher in modules with socio-constructivist or balanced variety learning designs, and lower for constructivist designs. In terms of learning outcomes, students rate constructivist modules higher, and socio-constructivist modules lower. However, in terms of student retention (% of students passed) constructivist modules have lower retention, while socio-constructivist have higher. Thus, learning design strongly influences behaviour, experience and performance. (and we believe we are the first to have mapped this with such a large cohort).