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“DISTANCE EDUCATION AND AFRICAN STUDENTS”
College of Agriculture and Environmental Sciences
College of Science, Engineerin...
Prof Parvati Raghuram
Parvati Raghuram is Professor in Geography and Migration at the Open
University. With an h-index of ...
http://ideaspartnership.org/
Twitter: @ESRC_IDEAS
#ESRCIDEAS
Join our discussions at pollev.com/bartrienties552
IDEAS
International Distance Education and African Students
■ Newton Fund Grant to explore the role of Distance Education ...
UK BasedTeam SA BasedTeam
Prof Parvati Raghuram Prof Ashley Gunter
Prof Bart Rienties Prof Clare Madge Mrs Katharine Reedy...
Project aims:
1. To examine how far international distance education (IDE) in
South Africa offers equitable access to stud...
Multi-disciplinary approach
Education
Human
Geography
Join our discussions at pollev.com/bartrienties552
Project methods:
• Learning analytics data from UNISA courses
• Learning design mapping of UNISA courses
• Questionnaires ...
Prof Bart Rienties
Dr. Bart Rienties is Professor of Learning Analytics at the Institute of Educational
Technology at the ...
Prof Ashley Gunter
Ashley Gunter is an Associate Professor in Geography at the University of South Africa. A
Y2 rated rese...
What is LD
Learning design aims to enable reflection, refinement, change and communication by
focusing on forms of represe...
IDEAS and Learning Design
■ Open University UK LD process
– Learning andTeaching Innovation @ the OU
– 6 month intensive m...
■ Assimilative: Read,Watch, Listen,Think about, Access, Observe, Review,Consider, Study
■ Finding and handling information...
What do you want your students to say
about your module?
Source: PHDCOMICS: http://phdcomics.com/comics.php
Learning design in diverse institutional and cultural contexts:
Suggestions from a participatory workshop with higher
educ...
Student profiles
Stages of the LD Process
■ Analysis
■ Design
■ Development
■ Implementation
■ Evaluation
and Learning Design
■ UniversityTeaching and Learning Development (DUTLD)
■ Multiple courses offered on thinking about mod...
Prof Bart Rienties
Dr. Bart Rienties is Professor of Learning Analytics at the Institute of Educational
Technology at the ...
Ferguson, R., Coughlan,T., Egelandsdal, K., Gaved, M., Herodotou, C., Hillaire, G., Jones, D., Jowers, I., Kukulska-Hulme,...
(Social) LearningAnalytics
“LA is the measurement, collection, analysis and reporting of data about learners and
their con...
Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and Implementation of a Learning ...
Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and Implementation of a Learning ...
It’s everywhere
31
Boyer, A., & Bonnin,G. (2016). Higher Education and the Revolution of Learning Analytics: InternationalCouncil for Open an...
Big Data is messy!!!
Prof Paul Kirschner (OU NL)
“Learning analytics: Utopia or dystopia”, LAK 2016 conference
■ Prof Paul Kirschner (OU NL)
■ ...
1. Increased availability of learning data
2. Increased availability of learner data
3. Increased ubiquitous presence of t...
Assimilative Finding and
handling
information
Communication Productive Experiential Interactive/
Adaptive
Assessment
Type ...
Merging big data sets
• Learning design data (>300 modules mapped)
• VLE data
• >140 modules aggregated individual data we...
Toetenel, L., Rienties, B. (2016). Analysing 157 Learning Designs using Learning Analytic approaches as a means to evaluat...
Nguyen, Q., Rienties, B.,Toetenel, L., Ferguson, R.,Whitelock, D. (2017). Examining the designs of computer-based assessme...
Constructivist
Learning Design
Assessment Learning
Design
Productive
Learning Design
Socio-construct.
Learning Design
VLE ...
So how can learning analytics be applied
at UNISA?
■ Many thanks to Dion van Zyl and his team for this continued support t...
Data used for multi-level modelling
Total %
Female 131042 47.8
Male 141292 51.6
Unknown 1626 .6
Total 273960 100.0
African...
Three-level Growth Curve Model
Level 1
Level 2
Level 3
G1
Student1
G3 G1 G2 G3 G1 G2 G3G2
Student2 Student3
Qualification1...
46
EARLY ALERT INDICATORS
OUVISION
47
What are they?
STUDENT PROBABILITIES
Herodotou, C., Rienties, B., Verdin, B., Boroowa, A. (2019). Predictive learning a...
48Fig 1. Anonymised screenshot of the Student Support Tool, containing module probabilities to complete (last column of th...
49
Based on an email from a tutor regarding a tutor group with a October 2017 (Feb 2018)
IDENTIFYING STRUGGLINGSTUDENTS – ...
50
Story of aTutorGroup
STUDENT PROBABILITIES – PATTERNS
CONCLUSIONS:
• A student’s performance can be anticipated even be...
51
What does it do?
It produces predictions as to
whether students are at risk of
failing their studies.
The model predict...
Lessons learned: Learning analytics data
UNISA
■ Large amount of data present at UNISA: great opportunity to identify whic...
Tea break
Performance
(e.g., Grade,
Adjustment,
GPA)
Time
A-student
B-student
C-student
■ A vast body of research shows that Affecti...
What are success factors for UNISA students?
Input Process Output
Learner characteristics
(incl. prior education, gender,
...
SACQ Questionnaire
■ Student Adaptation to College Questionnaire
• measures how well students manage the educational deman...
Data collection
1. First, in our initial study (Mittelmeier et al. 2019) we sampled 2634 students from a
first-year level ...
Dr Markus Roos Breines
Dr Markus Roos Breines joined the Open University as a Postdoctoral Research
Associate on the IDEAS...
Interview methods
■ 165 interviews with UNISA students:
Zimbabwe (85), Namibia (40), South Africa (30), and Nigeria (10).
...
Why UNISA?
‘Because Unisa is one of the best universities in Africa’ (Zimbabwean student).
■ Strong reputation inAfrica
■ ...
Social media
■ WhatsApp the most important tool for many students (for learning,
socialising, support, etc.)
■ Zimbabwean ...
Expanding horizons
Zimbabwean student:
■ I: So do you think you would have the same career prospects if you had studied in...
Challenges of studying at UNISA
■ Finding time (often start out with 5 modules per term, but then reduce because of
work/f...
International students’ suggestions for
improvement
– Better administrative support (esp. call centre, response to emails ...
Still positive
Despite some challenges, the international students generally had a very positive
experience
Namibian stude...
Dr Reuben Lembani
Dr Reuben Lembani is a Post-Doctoral Research Associate for the IDEAS
project. Reuben completed his MSc....
- A total of 162 universities (NUC
accredited), federal universities (40), state
universities (47) and private universitie...
- The av. enrolment of 450, 000 students at NOUN > the total enrolments in 75 private
universities. The model of ODE is di...
Namibia: The landscape of university education
- The country consists of two state or public universities, and one accredi...
The landscape of Zimbabwean universities
• Has ten state universities and several private
universities.
• A geographical b...
- The Zimbabwe Open University (ZOU) largest university with over 22, 000 students
- Most of its students are in Harare
- ...
UNISA: IDE and African students
- The four country reports: Highlighted the importance of individual country’s to deliver ...
Outcomes
■ Learning Design can have a significant impact on learning for students
■ Social Media plays a key role in stude...
Recommendations
■ Enhance the LD process already in place at UNISA
■ Phone line to direct queries – UNISA has already resp...
“Towards the African University shaping futures in the service of humanity”
http://ideaspartnership.org/
Twitter: @ESRC_IDEAS
#ESRCIDEAS
Dr Eeva Rapoo
Dr Eeva Rapoo has an MSc in Applied Mathematics and Mathematics, a PhD in Mathematics and is one of the
UNIS...
“DISTANCE EDUCATION AND AFRICAN STUDENTS”
College of Agriculture and Environmental Sciences
College of Science, Engineerin...
DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering...
DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering...
DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering...
DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering...
DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering...
DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering...
DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering...
DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering...
DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering...
DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering...
DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering...
DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering...
DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering...
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DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering and Technology Thursday 7 March 2019

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DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering and Technology Thursday 7 March 2019

  1. 1. “DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering and Technolog Thursday 7 March 2019 Thamsanqa Kambule Auditorium, Florida Science Campus
  2. 2. Prof Parvati Raghuram Parvati Raghuram is Professor in Geography and Migration at the Open University. With an h-index of 32, she is a leading international scholar who publishes widely on gender, skilled migration and postcolonial theory. She co- edits the journal South Asian Diaspora and the Palgrave Pivot series Mobility and Politics with Martin Geiger and William Walters both at Ottawa. Her most recent book is Gender, Migration and Social Reproduction (Palgrave). Join our discussions at pollev.com/bartrienties552
  3. 3. http://ideaspartnership.org/ Twitter: @ESRC_IDEAS #ESRCIDEAS Join our discussions at pollev.com/bartrienties552
  4. 4. IDEAS International Distance Education and African Students ■ Newton Fund Grant to explore the role of Distance Education in Africa ■ The grant is jointly managed by the ESRC and the NRF and is valued at approximately R10 Million ■ October 2016 – June 2019 ■ The project uses UNISA as a case study and students in four countries, South Africa, Namibia, Nigeria and Zimbabwe as case countries. ■ Student Adaptation College Questionnaire (CSET and UNISA students) ■ Learning Design (CSET collaboration) ■ LearningAnalytics (CSET subjects) ■ Social Media (UNISA students) ■ Student interviews (UNISA students) Join our discussions at pollev.com/bartrienties552
  5. 5. UK BasedTeam SA BasedTeam Prof Parvati Raghuram Prof Ashley Gunter Prof Bart Rienties Prof Clare Madge Mrs Katharine Reedy Dr Markus Roos Breines Prof Paul Prinsloo Dr Reuben Lembani Dr Mwazvita Dalu Dr J Rogaten Dr J Mittelmeier Dr M Cin Dr A Chisalle Dr Dianne LongDr G Sondhi
  6. 6. Project aims: 1. To examine how far international distance education (IDE) in South Africa offers equitable access to students in Africa through both supply side and demand side analysis. 2. To assess and improve the quality of IDE and see how it varies among students. 3. To advance theoretical understandings of IDE though a postcolonial framework and produce impactful findings that contribute towards Sustainable DevelopmentGoal 4 regarding equal access to quality education. Join our discussions at pollev.com/bartrienties552
  7. 7. Multi-disciplinary approach Education Human Geography Join our discussions at pollev.com/bartrienties552
  8. 8. Project methods: • Learning analytics data from UNISA courses • Learning design mapping of UNISA courses • Questionnaires of 1295 domestic and international students • Interviews with 164 domestic and international students • Interviews with 20 African distance education experts • Interviews with academics and policy makers in South Africa, Namibia, Zimbabwe and Nigeria Join our discussions at pollev.com/bartrienties552
  9. 9. Prof Bart Rienties Dr. Bart Rienties is Professor of Learning Analytics at the Institute of Educational Technology at the Open University UK. He is programme director Learning Analytics within IET and head of Data Wranglers, whereby he leads of group of learning analytics academics who conduct evidence-based research and sense making of Big Data at the OU. His primary research interests are focussed on Learning Analytics, Computer-Supported Collaborative Learning, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He has successfully led a range of institutional/national/European projects and received a range of awards for his educational innovation projects. Join our discussions at pollev.com/bartrienties552
  10. 10. Prof Ashley Gunter Ashley Gunter is an Associate Professor in Geography at the University of South Africa. A Y2 rated researcher, his research interests lie in the neoliberal state of education in the post-apartheid South African system as well as infrastructure and development. He is a council member of the South African Geographical Society, has been a Research Fellow at Oxford University and the University of Edinburgh and is an Associate Member of the OpenSpace Research Centre. He is on the editorial board of InterEspaço: Revista de Geografia e Interdisciplinaridade, and Cogent: Social Science. He has published and presented on development issues in South Africa.
  11. 11. What is LD Learning design aims to enable reflection, refinement, change and communication by focusing on forms of representation, notation and documentation.This can: ■ make the structures of intended teaching and learning – the pedagogy – more visible and explicit thereby promoting understanding and reflection ■ serve as a description or template, which can be adaptable or reused by another teacher to suit his/her own context ■ add value to the building of shared understandings and communication between those involved in the design and teaching process ■ promote creativity.
  12. 12. IDEAS and Learning Design ■ Open University UK LD process – Learning andTeaching Innovation @ the OU – 6 month intensive mapping and design process – 5 CSET modules at UNISA – Aim was a comprehensive course map and LD process with guidance from expert ■ Open University OER LD workshop – The OU LD workshop – Adapted for the AfricanContext – Run in South Africa; Namibia; Zimbabwe; Kenya and Nigeria
  13. 13. ■ Assimilative: Read,Watch, Listen,Think about, Access, Observe, Review,Consider, Study ■ Finding and handling information: List, Analyse, Collate, Plot, Find, Discover, Access, Use, Gather, Order, Classify, Select, Assess, Manipulate ■ Communication: Communicate, Debate, Discuss,Argue, Share, Report,Collaborate, Present, Describe, Question ■ Productive: Create, Build, Make, Design,Construct,Contribute,Complete, Produce,Write, Draw, Refine,Compose,Synthesise, Remix ■ Experiential: Practice, Apply, Mimic, Experience, Explore, Investigate, Perform, Engage ■ Interactive/adaptive: Explore, Experiment,Trial, Improve, Model, Simulate ■ Assessment: Write, Present, Report, Demonstrate,Critique, Peer-review, Self-assess, Receive feedback
  14. 14. What do you want your students to say about your module? Source: PHDCOMICS: http://phdcomics.com/comics.php
  15. 15. Learning design in diverse institutional and cultural contexts: Suggestions from a participatory workshop with higher education leaders in Africa 1. Collect information about student demographics 2. Develop a student needs assessment 3. Provide design flexibility for diverse student working patterns 4. Create teacher profiles in addition to student profiles 5. Assess university infrastructure needs 6. Build human resources for module design and data literacy 7. Diversify learning methods and activity types 8. Incorporate locally-relevant content 9. Collaborate with other universities 10. Evaluate learning designs after modules have run Mittelmeier, J., Long, D., Melis Cin, F., Reedy, K., Gunter, A., Raghuram, P., Rienties, B. (2018). Learning design in diverse institutional and cultural contexts: Suggestions from a participatory workshop with higher education leaders in Africa. Open Learning. 33(3), 250-266.
  16. 16. Student profiles
  17. 17. Stages of the LD Process ■ Analysis ■ Design ■ Development ■ Implementation ■ Evaluation
  18. 18. and Learning Design ■ UniversityTeaching and Learning Development (DUTLD) ■ Multiple courses offered on thinking about module design ■ DUTLD rep works with module leaders to develop modules ■ No systematic approach
  19. 19. Prof Bart Rienties Dr. Bart Rienties is Professor of Learning Analytics at the Institute of Educational Technology at the Open University UK. He is programme director Learning Analytics within IET and head of Data Wranglers, whereby he leads of group of learning analytics academics who conduct evidence-based research and sense making of Big Data at the OU. His primary research interests are focussed on Learning Analytics, Computer-Supported Collaborative Learning, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He has successfully led a range of institutional/national/European projects and received a range of awards for his educational innovation projects.
  20. 20. Ferguson, R., Coughlan,T., Egelandsdal, K., Gaved, M., Herodotou, C., Hillaire, G., Jones, D., Jowers, I., Kukulska-Hulme, A., McAndrew, P., Misiejuk, K., Ness, I. J., Rienties, B., Scanlon, E., Sharples, M.,Wasson, B.,Weller, M. and Whitelock, D. (2019). Innovating Pedagogy 2019: Open University Innovation Report 7. Milton Keynes:TheOpen University.
  21. 21. (Social) LearningAnalytics “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)
  22. 22. 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.
  23. 23. 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.
  24. 24. It’s everywhere 31
  25. 25. Boyer, A., & Bonnin,G. (2016). Higher Education and the Revolution of Learning Analytics: InternationalCouncil for Open and Distance Education.
  26. 26. Big Data is messy!!!
  27. 27. Prof Paul Kirschner (OU NL) “Learning analytics: Utopia or dystopia”, LAK 2016 conference ■ Prof Paul Kirschner (OU NL) ■ “Learning analytics: Utopia or dystopia”, LAK 2016 conference
  28. 28. 1. Increased availability of learning data 2. Increased availability of learner data 3. Increased ubiquitous presence of technology 4. Formal and informal learning increasingly blurred 5. Increased interest of non-educationalists to understand learning (Educational Data Mining, 4profit companies) 6. Personalisation and flexibility as standard
  29. 29. Assimilative Finding and handling information Communication Productive Experiential Interactive/ Adaptive Assessment Type of activity Attending to information Searching for and processing information Discussing module related content with at least one other person (student or tutor) Actively constructing an artefact Applying learning in a real-world setting Applying learning in a simulated setting All forms of assessment, whether continuous, end of module, or formative (assessment for learning) Examples of activity Read,Watch, Listen,Think about,Access, Observe, Review, Study List,Analyse, Collate, Plot, Find, Discover, Access, Use, Gather, Order, Classify, Select, Assess, Manipulate Communicate, Debate, Discuss, Argue, Share, Report, Collaborate, Present, Describe, Question Create, Build, Make, Design, Construct, Contribute, Complete, Produce,Write, Draw, Refine, Compose, Synthesise, Remix Practice, Apply, Mimic, Experience, Explore, Investigate, Perform, Engage Explore, Experiment,Trial, Improve, Model, Simulate Write, Present, Report, Demonstrate, Critique Conole, G. (2012). Designing for Learning in an OpenWorld. Dordrecht: Springer. 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 Open University Learning Design Initiative (OULDI)
  30. 30. Merging big data sets • Learning design data (>300 modules mapped) • VLE data • >140 modules aggregated individual data weekly • >37 modules individual fine-grained data daily • Student feedback data (>140) • Academic Performance (>140) • Predictive analytics data (>40) • Data sets merged and cleaned • 111,256 students undertook these modules
  31. 31. Toetenel, L., Rienties, B. (2016). Analysing 157 Learning Designs using Learning Analytic approaches as a means to evaluate the impact of pedagogical decision-making. BritishJournal of EducationalTechnology, 47(5), 981–992.
  32. 32. Nguyen, Q., Rienties, B.,Toetenel, L., Ferguson, R.,Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior. DOI: 10.1016/j.chb.2017.03.028. 69% of what students are doing in a week is determined by us, teachers!
  33. 33. 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 Nguyen, Q., Rienties, B.,Toetenel, L., Ferguson, R.,Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior. DOI: 10.1016/j.chb.2017.03.028. Communication
  34. 34. So how can learning analytics be applied at UNISA? ■ Many thanks to Dion van Zyl and his team for this continued support to our data sharing collaboration. ■ Large amount of data present at UNISA: great opportunity to identify which students are doing well, and which students might need more support
  35. 35. Data used for multi-level modelling Total % Female 131042 47.8 Male 141292 51.6 Unknown 1626 .6 Total 273960 100.0 African 193347 70.6 Coloured 14495 5.3 Indian 16311 6.0 White 47339 17.3 Unknown 2468 .9 Total 273960 100.0 Total % SA living in SA 239682 87.5 SA living in another country 1809 .7 International living in SA 14158 5.2 International living in another country 7490 2.7 SA living in unknown country 8309 3.0 International living in unknown country 838 .3 Unknown living in SA 43 .0 Unknown living in another country 7 .0 Unknown living in unknown country 1624 .6 Total 273960 100.0 Rogaten, J., Rienties, B., Van Zyl, D. (2018). Calculations by the authors. Undergraduate - honours 245187 89.5 Non-Formal - Occasional 19494 7.1 Postgraduate - Masters - PG below M 2563 .9 Unknown 6716 2.5 Total 273960 100.0
  36. 36. Three-level Growth Curve Model Level 1 Level 2 Level 3 G1 Student1 G3 G1 G2 G3 G1 G2 G3G2 Student2 Student3 Qualification1 Qualification2 G1 G2 G3 Student4 G1 G2 G3 Student5 Qualification3 Rogaten, J., Rienties, B, Whitelock, D. (2016). Assessing learning gains, TEA Conference, Tallinn, Estonia
  37. 37. 46 EARLY ALERT INDICATORS OUVISION
  38. 38. 47 What are they? STUDENT PROBABILITIES Herodotou, C., Rienties, B., Verdin, B., Boroowa, A. (2019). Predictive learning analytics ‘at scale’: Guidelines to successful implementation in higher education. Journal of Learning Analytics.
  39. 39. 48Fig 1. Anonymised screenshot of the Student Support Tool, containing module probabilities to complete (last column of the table, also shown enlarged)
  40. 40. 49 Based on an email from a tutor regarding a tutor group with a October 2017 (Feb 2018) IDENTIFYING STRUGGLINGSTUDENTS – ATUTOR GROUP EXAMPLE Out of seventeen students, only four were deemed ‘on track’, with further two a ‘maybe’.The rest were either already withdrawn or were not engaged. Could we have seen this from the student data?
  41. 41. 50 Story of aTutorGroup STUDENT PROBABILITIES – PATTERNS CONCLUSIONS: • A student’s performance can be anticipated even before module start • In the vast majority of cases, students with low probabilities end up withdrawing from the module • Students who are consistently on ‘green’ are generally doing well and are well engaged • ‘At risk’ status can happen at any stage of the module • Timing of updates seem to accurately capture major milestones and module events
  42. 42. 51 What does it do? It produces predictions as to whether students are at risk of failing their studies. The model predicts on a weekly basis whether or not a given student will submit theirTMA. It uses a traffic light system to pinpoint in red students at risk, in amber those with a moderate probability of failing and in green those who are unlikely to fail. OUANALYSE
  43. 43. Lessons learned: Learning analytics data UNISA ■ Large amount of data present at UNISA: great opportunity to identify which students are doing well, and which students might need more support ■ Your My UNISA already gives a lot of data about your students ■ Substantial opportunities for (predictive) learning analytics ■ Teacher essential for learning design and support
  44. 44. Tea break
  45. 45. 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).
  46. 46. What are success factors for UNISA students? 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)
  47. 47. SACQ Questionnaire ■ Student Adaptation to College Questionnaire • measures how well students manage the educational demands of the university experience. Academic Adjustment • measures how well students deal with interpersonal experiences at the university (e.g., making friends, joining groups) Social Adjustment • measures how well students maintain emotional equilibrium (particularly in the face of adjustment stressors), and indicates whether the student experiences general psychological distress or shows somatic symptoms of distress Personal Emotional Adjustment • assesses the degree of identification with and commitment towards the university Attachment
  48. 48. Data collection 1. First, in our initial study (Mittelmeier et al. 2019) we sampled 2634 students from a first-year level course unit in the College of Science, Engineering andTechnology: 320 (11.77%) students (IaH = 270, IaD = 36) responded. 2. In the second phase, we broadened our sampling approach to additional STEM qualifications, whereby we specifically sampled 5273 IaD and IA students using MIS data. 3. Students received individualised feedback on their responses
  49. 49. Dr Markus Roos Breines Dr Markus Roos Breines joined the Open University as a Postdoctoral Research Associate on the IDEAS project in April 2018. His doctoral research demonstrated how urban-urban migration in Ethiopia transformed people’s values, knowledge and status, and led to the formation of a loosely defined group that can be described as being middle class in Ethiopia. Markus has been trained in qualitative research methods and has conducted extensive fieldwork in Ethiopia for his PhD in Social Anthropology (University of Sussex).
  50. 50. Interview methods ■ 165 interviews with UNISA students: Zimbabwe (85), Namibia (40), South Africa (30), and Nigeria (10). ■ Skype to mobile phone to reach students in various locations. ■ Today’s focus:African international students
  51. 51. Why UNISA? ‘Because Unisa is one of the best universities in Africa’ (Zimbabwean student). ■ Strong reputation inAfrica ■ UNISA offering high quality education ■ The flexibility of study ■ Relatively low cost ■ International degree
  52. 52. Social media ■ WhatsApp the most important tool for many students (for learning, socialising, support, etc.) ■ Zimbabwean student: Now there are many classrooms on your smartphone, you have a mini community.Without social media, I won't have people to ask questions, and to share materials.
  53. 53. Expanding horizons Zimbabwean student: ■ I: So do you think you would have the same career prospects if you had studied in Zimbabwe? ■ P: I don’t think so. I wouldn’t get to know the other students, from Malawi, from Namibia, Botswana, South Africa. So the open distance learning really helped me to get to meet other people from different backgrounds. I really learnt a lot. ■ I: What have you learnt from meeting all these people? ■ P: By just talking to some of them, some of them will share the same backgrounds, they are from the rural areas, they are trying to develop themselves, just like I am doing, so it really motivates.And other guys are from families that are well off and you get to know those differences. I really get knowledge about what kind of people are out there in the world.
  54. 54. Challenges of studying at UNISA ■ Finding time (often start out with 5 modules per term, but then reduce because of work/family commitments). ■ Fees (affording fees, the foreign levy, and making international payments). Zimbabwean student: At times I'm experiencing some economic challenges.We are having problems in accessing foreign currency. I decided to take just a few modules so that I can be able to actually find the money to pay for the module. ■ Dealing with administrative issues (registration, reaching UNISA from abroad, expensive international phone calls, lack of email responses).
  55. 55. International students’ suggestions for improvement – Better administrative support (esp. call centre, response to emails – also from academic staff). – Study centres in different countries. – Digital communication; lecturers onWhatsApp and video recordings of lectures available online.
  56. 56. Still positive Despite some challenges, the international students generally had a very positive experience Namibian student: I would love to go abroad! And because UNISA’s education is on a very high level, you can actually work anywhere.You can go to Zambia, you can go other countries… The UNISA degree a key to transform their lives.
  57. 57. Dr Reuben Lembani Dr Reuben Lembani is a Post-Doctoral Research Associate for the IDEAS project. Reuben completed his MSc. in Environmental Science and has since completed his PhD in Geography and Environmental Studies from University of the Witwatersrand, South Africa.
  58. 58. - A total of 162 universities (NUC accredited), federal universities (40), state universities (47) and private universities (75) - NOUN, with av. of 100,000 first year admissions per year and a total of 450,000 students is the largest university in Nigeria - The rate of admission into universities range between 5% and 32% (1999-2016) - 1 university: 592, 522 NG aged 15-24 years old > 1 university: 371, 464 SA aged 15-24 years old - Universities in 70 countries absorbs approx. 71, 351 of Nigerian students (UNESCO, 2017) Nigeria: The landscape of university education Fig. 1a: University types Fig. 1b: Demand and supply
  59. 59. - The av. enrolment of 450, 000 students at NOUN > the total enrolments in 75 private universities. The model of ODE is different, NOUN charges more than double the tuition fee (economically disadvantaged? - UNISA continues to embrace Nigerians who fail to get a university - A BTech Mechanical Engineering Nigerian student with UNISA: “I decided to study at UNISA to advance academically in my field. Also UNISA allows me to do Distance Learning which schools in my country do not allow me to do.The flexibility of combining work and studying makes me choose to study with UNISA.”
  60. 60. Namibia: The landscape of university education - The country consists of two state or public universities, and one accredited privately-funded university - Between 19-25% of leaners studying at local universities via DE students, but due to geographical dispersion of localities, many Namibian opt to study with UNISA, e.g., 743_ST40: “I stay in the southwest coast of Namibia, it’s a small mining town so we don’t have any facilities. Our universities are in the capital city and in a few surrounding towns in the country, but it’s a bit far because I stay about nine hours’ drive from the capital. So it’s more convenient to study with UNISA because South Africa is about 20 kilometers away from where I stay now” Fig. 2a: University types Fig. 2b: NUST example
  61. 61. The landscape of Zimbabwean universities • Has ten state universities and several private universities. • A geographical bias with distribution of universities • An estimated 43, 000 students are absorbed in Zimbabwean universities • Highly competitive, whilst degree value now greatly compromised Spatial Distribution of Universities in Zimbabwe
  62. 62. - The Zimbabwe Open University (ZOU) largest university with over 22, 000 students - Most of its students are in Harare - UNISA offers a unique mode of delivery that is more accessible - Students are more confident of the value of the qualification, and; - Is less competitive for entry requirements - However, UNISA is slowly becoming less accessible due to persisting currency issues in the country ■ The second reason is that UNISA is well organised to the extent that it is well resourced in terms of giving you the material to study. They give you up to date material and the online material to the extent that you can do it in the comfort of your home. Either after work or at night. Unlike the local university, whereby you have to attend the lectures.
  63. 63. UNISA: IDE and African students - The four country reports: Highlighted the importance of individual country’s to deliver quality education to its people - For varied factors, ranging from geographical dispersion, competition in gaining entry into local universities, ICT facilities and quality of education - The course/ curricula elements should reflect the international diversity of its African students
  64. 64. Outcomes ■ Learning Design can have a significant impact on learning for students ■ Social Media plays a key role in student interaction with one another ■ UNISA LearningAnalytics needs to be utilised by staff to gain deeper understanding of their student cohort ■ African students have significant attachment to UNISA ■ International students perform better than local students
  65. 65. Recommendations ■ Enhance the LD process already in place at UNISA ■ Phone line to direct queries – UNISA has already responded to this need ■ Keep online social media informal ■ Best practice shared and discussed at Department level ■ LearningAnalytics presented at College annually
  66. 66. “Towards the African University shaping futures in the service of humanity”
  67. 67. http://ideaspartnership.org/ Twitter: @ESRC_IDEAS #ESRCIDEAS
  68. 68. Dr Eeva Rapoo Dr Eeva Rapoo has an MSc in Applied Mathematics and Mathematics, a PhD in Mathematics and is one of the UNISA UMUC graduates. She has been at UNISA since 1996, and has been teaching modules at all undergraduate and postgraduate levels, receiving a UNISA Excellence in Teaching award in 2010. She is currently the Chair of the Department of Statistics in the School of Science at CSET. Her research interests include stochastic processes, but also all aspects of Mathematics and Statistics teaching and learning, in particular in the ODL setting. She has been actively involved in various UNISA initiatives and committees for teaching, learning, quality assurance, assessment over the years, and she has been the chair of the Open Distance Learning research flagship of the College of Science, Engineering and Technology since its inception in 2011. In 2018 – 2019, she is also representing UNISA as a participant at the HELTASA TAU fellowship programme.
  69. 69. “DISTANCE EDUCATION AND AFRICAN STUDENTS” College of Agriculture and Environmental Sciences College of Science, Engineering and Technology Thursday 7 March 2019 INTRODUCING THE SPEAKERS

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