Keynote, 9th Annual International Conference on Mathematics, Science and Technology Education, 25 October 2018, South Africa

1. Paul Prinsloo
University of South Africa (Unisa)
@14prinsp
Mapping teaching and learning as
(dis)location/(re)location: the role of
student data
Keynote, 9th Annual International Conference on Mathematics,
Science and Technology Education, 25 October 2018, South Africa
Imagecredit:https://pixabay.com/en/falling-suicide-man-jump-2245869/

2. Acknowledgement
I do not own the copyright of any of the images in this
presentation. I therefore acknowledge the original copyright
and licensing regime of every image used.
This presentation (excluding the images) is licensed
under a Creative Commons Attribution 4.0 International
License.

3. Important notes
• This presentation mainly focuses on the collection,
analysis and use of student data in the context of the
teaching of mathematics, sciences and technology in
higher education
• There are, however, ample evidence that primary and
secondary school students are targeted by a range of
stakeholders, including, but not limited to their
institutions.
• The nature and scope of the data collected, analysed
and used range from analogue to digital and often
depend on the extent of the use of online technologies

4. Overview of the presentation
• Crossing borders: (Dis)location/(re)location
• Student data: an update
• Mapping teaching/learning in mathematics, science
and technology as border crossing, as
(dis)location/(re)location
• How can the collection, analysis and use of student
data help us to make better/more informed
pedagogical decisions, inform curriculum design,
student support, resource allocation
• Pointers for consideration
• (In)conclusion

5. Crossing borders – preparing for the visa process,
facing the border officials and coping with the
strangeness of the ‘other’ side

6. Crossing borders: A study of fear in three acts
Preparing for the
visa application
Facing the border
official
Discovering the
‘other’
• Do I meet the
requirements?
• What will count?
• What will not be
acceptable?
• What I hope will not
be taken into
account…
• The cost of the
application
• I hope I have
everything
• I have a visa so why is
s/he is not friendly?
• What if there is
something wrong with
the visa?
• Why do they need extra
information?
• I hope s/he will not
request the password of
my social media
accounts
• Yet another set of
fingerprints?
• Where do I find the
train?
• What does that sign
mean?
• Ooops, I am lost – who
looks friendly enough
to ask for directions?
• What do you mean
you don’t speak
English…
• Ooops, wrong way
• Where am I?
• This tastes weird

7. We need to recognise and understand ‘the
inherent border crossings between students’
life-world subcultures and the subculture of
science, and … to develop curriculum and
instruction with these border crossings explicitly
in mind”
(Aikenhead, 1996, p. 4)

8. Students crossing borders: A study of
(dis)location/(re)location
Orienting context Instructional context Transfer context
• Socio-economic
circumstances
• Congruence with the
world of science
• Demographics
• Dispositions, locus of
control, self-efficacy
• Learning journeys
• Support networks
• Rationale for choosing
science, mathematics
or technology
• School leaving marks
• Higher education
• Form of delivery – full-
time, blended, online
• On-campus/off-campus
• Institutional character and
(in)efficiencies
• Disciplinary context – signs,
rules and terminology
• Educator: student ratios
• Semester/year course
• Number of assignments
• Format of assignments/
summative assessment
• The world of work
• Finding employment
• ‘Fitting in” or not
• This is (not) for me
• Why did they not tell
me?
• Things I should have
learned
• Things they should
have taught me
• The world of
workplace politics,
ladders, fences, and
tribes
Tessmer, M., & Richey, R. C. (1997). The role of context in learning and instructional design.
Educational technology research and development, 45(2), 85-115.

9. Students crossing borders and student
data
Orienting
context
Instructional
context
Transfer
context
• What data do we already have? Where is it? Who has access to it? How
useable are the data?
• What data do we need in order to make more informed, appropriate
and ethical decisions?
• What data do they (students)need to make more informed, appropriate
and ethical decisions?
• What data do we/they have about the transitions that may help us and
them?

10. Putting the collection, analysis and
use of student data into perspective

11. Imagecredit:http://blog.ceo.ca/wp-content/uploads/2015/02/oil-rigs.jpg
The current hype surrounding the collection, analysis and use of
student data is informed by (1) the availability of data; (2) the
reach of surveillance; (3) data mining scope and methods; (4) data
capitalism; (5) evidence-based management and an obsession
with metrics; and (6) an authentic concern about increasing the
effectiveness and appropriateness of teaching and learning

12. Source credit: https://www.washingtonpost.com/news/the-switch/wp/2015/12/28/google-is-tracking-students-
as-it-sells-more-products-to-schools-privacy-advocates-warn/?noredirect=on&utm_term=.6c0bfa947fc2
Example 1

13. More than half of K-12 laptops or tablets purchased by U.S.
schools in the third quarter were Chromebooks, cheap laptops that
run Google software. Beyond its famed Web search, the company
freely offers word processing and other software to schools. In
total, Google programs are used by more than 50 million
students and teachers around the world, the company says.
But Google is also tracking what those students are doing on its
services and using some of that information to sell targeted
ads, according to a complaint filed with federal officials by a
leading privacy advocacy group.
And because of the arrangement between Google and many public
schools, parents often can’t keep the company from
collecting their children’s data, privacy experts say.
Source credit: https://www.washingtonpost.com/news/the-switch/wp/2015/12/28/google-is-tracking-students-
as-it-sells-more-products-to-schools-privacy-advocates-warn/?noredirect=on&utm_term=.6c0bfa947fc2

14. Source credit:https://www.insidehighered.com/news/2018/10/09/coming-soon-ivy-league-campuses-free-coffee-
privacy-not-included
Example 2

15. Source credit: https://www.theguardian.com/higher-education-network/2016/aug/03/learning-analytics-universities-data-track-students
2016

16. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
Page credit: https://www.edsurge.com/news/2017-06-13-from-high-school-to-harvard-students-urge-for-clarity-on-privacy-
rights?utm_content=buffer8dd71&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
3 June 2017

17. Source credit: https://campustechnology.com/articles/2018/05/02/when-learning-analytics-violate-student-privacy.aspx
2018

18. (Higher) education has always collected, analysed
and used student data for a variety of purposes
Image credit: https://en.wikipedia.org/wiki/Scholasticism

19. IN THE PAST AT PRESENT
Data sources Demographic and learning data
at specific points in the learning
journey: data application,
registration, class registers,
assignments, summative
assessment, personal
communication
Continuous, directed, gifted
and automated collection of
data from a range of data
sources – student
administration, learning
management system (LMS),
sources outside of the LMS
Data use Reporting purposes, operational
planning on cohort, group level
by management, institutional
researchers
Descriptive, diagnostic,
predictive and prescriptive on
group/cohort level
Plus individualised, often
real-time analysis and use of
data to inform pedagogy,
curriculum, assessment,
student support by faculty,
students and support staff
Who used
the data
(officially)?
Management, institutional
researchers, planners, quality
assurance and HR departments
Plus researchers, faculty,
students, support staff,
special departments
dedicated for at-risk students

20. IN THE PAST AT PRESENT
Who did the
collection,
analysis and
who used the
data
Humans Increasingly humans in
combination with algorithmic
decision-making processes
Temporal aim Retrospective/historical data to
make predictions with regard to
budget, future enrollments &
resource allocation on
institutional level
Plus real-time data for real-
time interventions
Default Forgetting Remembering
Personal
identifiers
Anonymised, aggregated data Plus re-identifiable data
Personal/ised data
Oversight/
data
governance
Broad institutional oversight.
Ethical Review Board (ERB)
approval for research purposes
Approval, oversight and
governance highly complex
and contested

21. Source credit: https://tekri.athabascau.ca/analytics/
“Learning analytics 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”.

22. We know, take into account and we
measure: age, gender, race, street address
and zip code, occupation, pre-enrolment
educational data, registration data,
engagement data, academic data, library
data, financial aid data, behavioural data,
location data, who-are-in-their-networks-
data, their chances of failing, dropping out,
stopping out…
Image credit: https://pixabay.com/en/side-profile-black-male-student-1440176/
And we use this data to…
Image credit: https://pixabay.com/en/girl-library-education-student-1721436/

23. Source credit: http://timoelliott.com/blog/2013/02/gartnerbi-emea-2013-part-1-analytics-moves-to-the-core.html
Uses of student data

24. Student learning is not a linear, closed process but an open,
recursive process emerging from and entangled in various,
often mutually constitutive factors in the nexus between
1. students’ habitus, dispositions and life-worlds;
2. the institutional character, (in)efficiencies, disciplinary
domains and departmental and institutional resource
allocation and planning;
3. macro-societal factors impacting on both students and
their life-worlds and the institution and its networks;
and
4. the unfolding of a personal, compromised, situated,
learning journey
Subotzky, G., & Prinsloo, P. (2011). Turning the tide: A socio-critical model and framework for
improving student success in open distance learning at the University of South Africa. Distance
Education, 32(2), 177-193.

25. Image credit: https://pixabay.com/en/side-profile-black-male-student-1440176/
Image credit: https://pixabay.com/en/girl-library-education-student-1721436/
Macro-societal factors, e.g. economic, political, social,
technological, environmental and legal factors.
Institutional/lecturer/student inactions, inefficiencies, or
lack of control impacting and shaping students’ behaviour,
chances of failing, dropping out, stopping out…

26. Processes
Inter & intra-
personal
domains
Modalities:
• Attribution
• Locus of control
• Self-efficacy
Processes
Modalities:
• Attribution
• Locus of
control
• Self-efficacy
Domains
Academic
Operational
Social
TRANSFORMED INSTITUTIONAL IDENTITY & ATTRIBUTES
THE STUDENT AS AGENT
IDENTITY, ATTRIBUTES, HABITUS
Success
THE INSTITUTION AS AGENT
IDENTITY, ATTRIBUTES, HABITUS
SHAPING CONDITIONS: (predictable as well as uncertain)
SHAPING CONDITIONS: (predictable as well as uncertain)
Choice,
Admission
Learning
activities
Course
success
Gradua-
tion
THE STUDENT WALK
Multiple, mutually constitutive
interactions between student,
institution & networks
F
I
T
FIT
F
I
T
FIT
Employ-
ment/
citizenship
TRANSFORMED STUDENT IDENTITY & ATTRIBUTES
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
Retention/Progression/Positive experience
(Subotzky & Prinsloo, 2011)

27. Tracking the student journey – Who watches?
Why? Who cares?
• Considering the complexity and inherent dynamic and
multi-layered nature of student learning, how do we
identify specific points or data that may alert us, or prompt
us to inquire further?
• What systems, resources and networks should we put in
place to pick up signals AND to distinguish signals from
noise?

28. (1)
Humans
perform the
task
(2)
Task is
shared with
algorithms
(3)
Algorithms
perform task:
human
supervision
(4)
Algorithms
perform task:
no human
input
Seeing Yes or No? Yes or No? Yes or No? Yes or No?
Processing Yes or No? Yes or No? Yes or No? Yes or No?
Acting Yes or No? Yes or No? Yes or No? Yes or No?
Learning Yes or No? Yes or No? Yes or No? Yes or No?
Danaher, J. (2015). How might algorithms rule our lives? Mapping the logical space of algocracy. [Web log post]. Retrieved from
http://philosophicaldisquisitions.blogspot.com/2015/06/how-might-algorithms-rule-our-lives.html
Human-algorithm interaction in the collection, analysis and
use of student data

29. Source credit: https://qz.com/1318758/schools-are-using-ai-to-track-what-students-write-on-their-computers/

30. Source credit: https://www.fool.com/investing/2018/10/17/future-education-online-free-and-with-ai-teachers.aspx

31. In general, we have access to:
• More (student) data than ever before (volume)
• A greater variety of student data from a variety of
sources
• Greater granularity of data
• Synchronous data allowing for synchronous
responses
• Increasing human and algorithmic decision-making
In online teaching environments, we have more
opportunity than ever before to make (more)
informed decisions regarding student needs,
misunderstandings, and progress (or lack of)

32. The amount, quality, format, accessibility and
use of student data depends on:
• Institutional data infrastructure - integration of data
sources – Seamless, decentralized system?
Centralisation - data warehouse?
• The use of technology – offline, online, blended
• Intention to collect, analyse and use student data
• Dedicated departments/staff to oversee the collection,
analysis and use of student data
• Policy and regulatory environment
• Faculty, admin and support staff understanding of the
complexities of student learning and competencies

33. Orienting context
• Demographic
• Previous learning
experiences
• Correspondence
• Telephonic inquiries
• Registration form
Instructional context
• Assignments
(formative)
• Class participation
• Peer-to peer
• Student-instructor
• Student-content
Transfer context
• Summative
assessment
• Re-registration
• Employment
Location of data
• Student Admissions/
Information
• Library
• Student support
• Learning
management system
Who has access the the
data
• Lecturers
• Managers
• Administration
• Student support
• Institutional research
• Researchers
• Students?
Uses of the data
• Operational planning
• Reporting purposes
• Resource allocation
• Departmental planning
and staff allocation
• Curriculum?
• Pedagogical decisions?
• Student choices?
What data do we (already) have?

34. Orienting context Instructional context Transfer context
What data will we
need to understand
the (in)congruence of
their worlds with the
world of science?
Where will the data be
stored, under what
conditions, and how
will access be
governed?
What alerts can we build
in to warn us that they
missed a crucial threshold
concept? Who will need
to have access the the
data, under what
conditions, and what
ethical oversight will
there be for how we
collect the data?
How can students
provide us with
data that will
inform curriculum
and instructional
design once they
are employed and
realise what they
missed/achieved?
What data do we need in order to make more
informed, appropriate and ethical decisions?

35. Orienting context Instructional context Transfer context
What data will they
need to orientate
themselves towards
their chosen field of
study? How can we
help them understand
what crossing the
border will entail?
What data will they
need to adjust their
learning behavior?
What data will be
helpful to them to help
them understand where
we are and how that
impacts on their
learning?
What data will
they need in
order to re-
register or to
enter the world of
work?
What data do they need in order to make more
informed, appropriate and ethical decisions?

36. Image credit: https://pixabay.com/en/travel-sculpture-man-stone-move-3034459/
Knowing more also means having greater
responsibility to make informed, tentative,
appropriate and ethical decisions

37. Image credit: https://pixabay.com/en/escalator-transportation-public-1245905/
How can we use student data to assist students in
their crossing over into the worlds mathematics,
science and technology?

38. Orienting
context
Instructional
context
Transfer
context
Students crossing borders and the data
student data it generates
What happens here?
What happens here?

39. Orienting
context
Instructional
context
Transfer
context
Students crossing borders and the data
student data it generates
• Socio-economic circumstances
• Congruence with the world of science
• Demographics
• Dispositions, locus of control, self-efficacy
• Learning journeys
• Support networks
• Rationale for choosing science, mathematics or technology
• School leaving marks

40. “…science and mathematics
teaching can never be divorced
from the socioeconomic context in
which it is taught”
(Maree, Aldous, Hatting, Swanepoel & Van der Linde, 2006, p. 238)
Maree, K., Aldous, C., Hattingh, A., Swanepoel, A., & Van der Linde, M. (2006).
Predictors of learner performance in mathematics and science according to a large-
scale study in Mpumalanga. South African Journal of Education, 26(2), 229-252.

41. Four types of transition (Phelan, Davidson & Cao, 1991)
Congruent worlds
A smooth transition
Different worlds
Transition to be managed
Diverse worlds
Hazardous transitions
Discordant worlds
Transition is virtually impossible
Phelan, P., Davidson, A., & Cao, H.(1991). Students' multiple words: Negotiating the boundaries of family, peer, and school cultures.
Anthropology and Education Quarterly, 22(2), 224-250.

42. The world of
science
Potential scientists: worlds of family
and friends are congruent with the
worlds of higher education and
science
Other smart kinds: worlds of
family and friends are congruent
with the worlds of higher
education but inconsistent with
the world of science
“I don’t know” students: worlds
of family and friends are
inconsistent with the worlds of
higher education and of science
Outsiders: worlds of family
and friends are discordant
with the worlds of higher
education and of science
Inside outsiders: worlds of family and
friends are irreconcilable with the world
of higher education, but potentially
compatible with the world of science
Costa, V.B. (1995). When science is 'another world': Relationships between worlds of family, friends,
school, and science. Science Education, 79(3), 313 333.

43. “… pedagogy in science
classrooms is also about the
struggle for identities and
representations”
(Prinsloo, 2008, p. 274)

44. • What data can students contribute to help us to understand
their worlds and what it would take to make a successful
transition into the world of science?
• What data do we have to help students make a successful
transition into the world of science?
• How/when will we/they get access to that data?

45. Orienting
context
Instructional
context
Transfer
context
Students crossing borders and the data
student data it generates
• Higher education
• Form of delivery – full-time, blended, online
• On-campus/off-campus
• Institutional character and (in)efficiencies
• Disciplinary context – signs, rules and terminology
• Educator: student ratios
• Semester/year course
• Number of assignments
• Format of assignments/ summative assessment

46. Department of Higher Education and Training. (2014). Policy for the provision of distance education in South African universities in the context of
an integrated post-school system. Retrieved from http://www.saide.org.za/sites/default/files/37811_gon535.pdf
OfflineOnline Fully online
Fully offline
Digitally supported
Internet supported
Internet dependent
Campus-based Blended/hybrid Remote
A
BC
Distance, digitally supported
Distance, fully onlineCampus-based,
fully online

47. A case study from Mathematics
Bohlmann, C. A., & Pretorius, E. J. (2002). Reading skills and mathematics: the practice of higher
education. South African Journal of Higher Education, 16(3), 196-206.
Reading mathematical texts involve both decoding and
comprehension
“Decoding involves those aspects of a reading activity
whereby written signs and symbols are translated into
language. “Comprehension” refers to an overall
understanding process whereby meaning is assigned to the
whole text. The interaction between decoding and
comprehension in skilled readers happen rapidly and
simultaneously” (Bohlmann & Pretorius, 2002, p. 196)

48. Successful decoding does not, however, imply
comprehension. Mathematical texts are also
“hierarchical and cumulative, in the sense that
understanding each statement or proposition is
necessary for understanding subsequent
statements” (Bohlmann & Pretorius, 2002, p. 277)
Their research results indicate “that the weaker
students regularly miss vital clues that aid in
constructing and keeping track of a meaning in a text”
(Bohlmann & Pretorius, 2002, p. 205)

49. If students cannot resolve an anaphor, conditional or
constrastive relations, they will miss the point
Also see:
Pretorius, E. J., & Bohlmann, C. A. (2003). A reading intervention programme for
mathematics students: the practice of higher education. South African Journal of
Higher Education, 17(2), 226-236.
Bohlmann, C. A., & Fletcher, L. (2008). Diagnostic assessment for mathematics in a
distance learning context. South African Journal of Higher Education, 22(3), 556-
574.
Feedback, and timely feedback is crucial in student
learning. What data do we have available that they
may miss a crucial point? What will it take to notice
the ‘miss’, respond and assist in them realising what
they (don’t) know

50. • If we know what they miss, what alert systems can
we build in to alert us and them?
• If they don’t know what they don’t know, what does
this mean for admission requirements, student
support, and the costing of student support?

51. Pointers for consideration
1. Data are framed according to their context (e.g.,
economically, in time, etc.), and do not exist independently
of the “ideas, instruments, practices, contexts and
knowledges used to generate, process and analyse them”
(Kitchin, 2014, p. 2)
2. More data do not, necessarily, mean more accuracy or more
understanding
3. Our student data will never provide us with the whole
picture. At most students’ data and our analyses may provide
us glimpses of the complexities of their learning and our
teaching, and opportunities for engagement
Kitchen, R. (2014). The data revolution. Big data, open data, data infrastructures and
their consequences. London, UK: SAGE.

52. Pointers for consideration
4. It is crucial that we interrogate our own and our institutions’
assumptions and beliefs about student learning
5. Much of the data harvested from students flow from a
deficit understanding of student identity and learning.
Students learning is much more than what they don’t have,
but helping them to achieve their full potential and to
engage independently and competently in a particular
discourse and context

53. Source credit: https://www.achievementnetwork.org/anetblog/eduspeak/equity-in-education
How do we collect, analyse and use student data to achieve liberation,
where students can participate fully in a particular discourse their full
potential?

54. What data do they need to realise their full potential?
What data do we need to help them realise their full potential?
Source credit: https://www.achievementnetwork.org/anetblog/eduspeak/equity-in-education

55. Collecting, analysing and using student data is,
however, a part of a broader ecology consisting of
the institutional character, (in)efficiencies, resource
allocation, student: instructor ratios, support for
students and faculty, and understanding student
retention and success as a complex phenomenon
(In)conclusions

56. Image credit: https://pixabay.com/en/bridge-path-railing-risk-1709849/
Crossing over, entering the unknown is never easy.
We should not forget how disorientating crossing a
border is.

57. Image credit: https://pixabay.com/en/stairs-boots-old-shoes-1683118/
Collecting, analysing and using student data may
assist them and us, to make more appropriate, and
responsible choices

58. THANK YOU
Paul Prinsloo (Prof)
Research Professor in Open Distance Learning (ODL)
College of Economic and Management Sciences, Samuel Pauw
Building, Office 5-21, P.O. Box 392
Unisa, 0003, Republic of South Africa
T: +27 (0) 12 433 4719 (office)
prinsp@unisa.ac.za
Skype: paul.prinsloo59
Personal blog:
http://opendistanceteachingandlearning.wordpress.com
Twitter profile: @14prinsp