https://www.unisa.ac.za/sites/corporate/default/Colleges/Agriculture-&-Environmental-Sciences

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

3. 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

4. http://ideaspartnership.org/
Twitter: @ESRC_IDEAS
#ESRCIDEAS
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5. 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

6. 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

7. 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.
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8. Multi-disciplinary approach
Education
Human
Geography
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9. 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
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10. 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

11. 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.

12. 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.

14. 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

17. ■ 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

19. What do you want your students to say
about your module?
Source: PHDCOMICS: http://phdcomics.com/comics.php

21. 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.

22. Student profiles

24. Stages of the LD Process
■ Analysis
■ Design
■ Development
■ Implementation
■ Evaluation

25. 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

26. 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.

27. 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.

28. (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)

29. 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.

30. 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.

31. It’s everywhere
31

32. Boyer, A., & Bonnin,G. (2016). Higher Education and the Revolution of Learning Analytics: InternationalCouncil for Open and Distance Education.

33. Big Data is messy!!!

34. 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

35. 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

36. 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)

38. 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

39. 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.

41. 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!

42. 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

43. 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

44. 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

45. 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

46. 46
EARLY ALERT INDICATORS
OUVISION

47. 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.

48. 48Fig 1. Anonymised screenshot of the Student Support Tool, containing module probabilities to complete (last column of the table, also shown enlarged)

49. 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?

50. 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

51. 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

52. 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

53. Tea break

55. 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).

56. 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)

58. 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

59. 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

61. 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).

62. 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

63. 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

64. 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.

65. 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.

66. 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).

67. 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.

68. 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.

69. 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.

70. - 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

71. - 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.”

72. 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

73. 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

74. - 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.

75. 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

76. 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

77. 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

78. “Towards the African University shaping futures in the service of humanity”

79. http://ideaspartnership.org/
Twitter: @ESRC_IDEAS
#ESRCIDEAS

80. 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.

82. “DISTANCE EDUCATION AND AFRICAN STUDENTS”
College of Agriculture and Environmental Sciences
College of Science, Engineering and Technology
Thursday 7 March 2019
INTRODUCING THE SPEAKERS