Download to read offline
![(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](https://image.slidesharecdn.com/finalprinslooskukuza2018-181026114955/75/Mapping-teaching-and-learning-as-dis-location-re-location-the-role-of-student-data-28-2048.jpg)
Uploaded byUniversity of South Africa (Unisa)
Mapping teaching and learning as (dis)location/(re)location: the role of student data
Paul Prinsloo's keynote presentation discusses the crucial role of student data in enhancing teaching and learning in mathematics, science, and technology within higher education. It emphasizes understanding students' diverse backgrounds and the importance of data in informing pedagogical decisions and curriculum design. The presentation also highlights the increasing complexity of data collection and analysis, including ethical considerations and the impact of socio-economic factors on student success.
More Related Content
Similar to Mapping teaching and learning as (dis)location/(re)location: the role of student data
More from University of South Africa (Unisa)
Mapping teaching and learning as (dis)location/(re)location: the role of student data
- 1. Paul Prinsloo University ofSouth 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 notown 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 • Thispresentation 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 thepresentation • 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: Astudy 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 torecognise 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 bordersand 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 hypesurrounding 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.
- 13. More than halfof 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.
- 15.
- 16.
- 17.
- 18. (Higher) education hasalways collected, analysed and used student data for a variety of purposes Image credit: https://en.wikipedia.org/wiki/Scholasticism
- 19. IN THE PASTAT 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 PASTAT 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/ “Learninganalytics 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, takeinto 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.
- 24. Student learning isnot 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/ Imagecredit: 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 studentjourney – 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 sharedwith 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.
- 30.
- 31. In general, wehave 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 Instructionalcontext 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 Instructionalcontext 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/ Knowingmore also means having greater responsibility to make informed, tentative, appropriate and ethical decisions
- 37. Image credit: https://pixabay.com/en/escalator-transportation-public-1245905/ Howcan 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 bordersand the data student data it generates What happens here? What happens here?
- 39. Orienting context Instructional context Transfer context Students crossing bordersand 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 teachingcan 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 oftransition (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 Potentialscientists: 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 inscience classrooms is also about the struggle for identities and representations” (Prinsloo, 2008, p. 274)
- 44. • What datacan 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 bordersand 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 HigherEducation 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 studyfrom 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 doesnot, 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 cannotresolve 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 weknow 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 Howdo 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 dothey 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 andusing 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/ Crossingover, 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) [email protected] Skype: paul.prinsloo59 Personal blog: http://opendistanceteachingandlearning.wordpress.com Twitter profile: @14prinsp