Balancing cost, quality and access with care in online education: the role of student dataPresentation at the Centre for Continuing Education, McMaster University, 23 March 2017

Paul Prinsloo (Unisa)14prinspImage credit: http://mcahalane.com/wp-content/uploads/2015/10/Tightrope_walking-By-Wiros-

from-Barcelona-Spain-CC-BY-SA-2.0-via-Wikimedia-Commons.jpg

Doing online education 'well'--defined as that which enables access and is

characterized by caring interactions and quality resources--is a costly and complex process. It is also markedly competitive.

CostAccess

Quality

“Students value our quality instructors with real-world experience and the flexibility and

convenience of courses offered at night, on weekends, and online”

(https://www.mcmastercce.ca/about-us)

Student data

How do we ethically balance this need for data with an equally strong

commitment to respect the lives and identities of our students?

Quality

Access Cost

Ethics

Student data

Balancing between risk, cost and care…We need to ensure the sustainability of higher education in the light of• funding constraints – funding

follows performance/evidence

• increased competition• the socioeconomic downturn• student needs, risks and

demands• increased need for

efficiency/effectiveness• audit & quality assurance

regimes

The fiduciary duty of higher education to• care• create supportive, appropriate and

effective teaching and learning environments

• ethical collection, analysis and use of student data

• transparency• critical interrogate our

assumptions about-- learning, merit, data, our data collection methods, those who do the analyses, and governance of data

Teleological• The potential for harm• The scope of consent • Recourse in cases of

unintended harm are negotiated and agreed upon• Discursive

Deontological• Basis for legal and

regulatory frameworks • Terms and Conditions• By consent and/or

contract• Works well in stable

environments

Two traditional approaches to being ethical

The collection, analysis and use of student data: some pointers

1. What are our (management, administrative, faculty and support staff’s) beliefs about knowledge, learning, assessment, data, and evidence?

2. What student data do we already have, why was it collected, in which format is it stored, who has access to the data, how is the data used by whom, and do students know this, have access to it, and know how it influences our and their choices?

3. What data do students currently have access to about their learning and about our choices pertaining to their learning?

The collection, analysis and use of student data: some pointers (cont.)4. What data do students not currently have, but we have, that

will help them to plan their time and resources in order to maximize their chances of success?

5. What student data that we don’t have, do we need in order to teach better, allocate resources, and support students? Is this data available, under what conditions will we be able to access it, how will we govern its-- storage, combination with other sources of data, who will have access to it and under what conditions?

Some pointers

• 1973 – “Code of fair information practices”• 20 questions by Marx (1998)• 9 Principles by Pounder (2008)• Slade and Prinsloo (2013)

1. There must be no personal-date record-keeping systems whose very existence is secret.

2. There must be a way for an individual to find out what information about him/her is in the record and how it is used.

3. There must be a way for an individual to prevent information obtained about him/her for one purpose for being used or made available for other purposes without his/her consent.

4. There must be a way for an individual to correct or amend a record of identifiable information about him/her.

5. Any organisaton creating, maintaining, using, or disseminating records of indentifiable personal data must assure the reliability of the data for their intended use and must take reasonable precautions to prevent misue of the data.

1973 Code of fair information practices

Code of fair information practices. (1973). Retrieved from http://simson.net/ref/2004/csg357/handouts/01_fips.pdf

20 questions by Marx (1998)Means: Harm, boundaries, trust, personal relationships, validityData collection contexts: Awareness, consent, golden rule, minimisation, public decision making, human review, right of inspection, right to challenge, redress and sanction, stewardship and protection, unintended precedents, etc.Uses: Beneficiary, proportionality, alternative means, consequences of inaction, protections, etc.

Marx, G.T. (1998). Ethics for the new surveillance. The Information Society: An International Journal, 14(3), 171-185. DOI: org/10.1080/019722498128809

9 Principles by Pounder (2008)Principle 1:The justification principlePrinciple 2:The approval principlePrinciple 3:The separation principlePrinciple 4:The adherence principlePrinciple 5:The reporting principlePrinciple 6: The independent supervision principlePrinciple 7: The privacy principlePrinciple 8: The compensation principlePrinciple 9: The unacceptability principle

Pounder, C.N.M. 2008. Nine principles for assessing whether privacy is protected in a surveillance society. Identity in the Information Society, 1, 1-22. DOI 10.1007/s12394-008-0002-2.

6 Principles for learning analytics as moral practice (Slade & Prinsloo, 2013)

1. Learning analytics as moral practice2. Students as agents3. Student identity and performance as temporal

dynamic constructs4. Student success is a complex and multidimensional

phenomenon5. Transparency6. Higher education cannot afford not to use data

Slade, S., & Prinsloo, P. 2013. Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, XX(X), 1-20. DOI: 10.1177/0002764213479366

A number of considerations for an ethics architecture for learning analytics

1.Who benefits and under what conditions?2.Conditions for consent, de-identification, and

opting out – including considerations regarding vulnerability and harm

3.Data collection, analyses, access and storage4.Governance and resource allocation

Slade, S., & Prinsloo, P. 2013. Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, XX(X), 1-20. DOI: 10.1177/0002764213479366

So what do students need/want?• A functioning platform – with 24/7 assistance• Don’t waste their time – design carefully• The tensions between flexibility and structure – the

myth of “anywhere, any time, at your own pace”• Feedback – how am I doing? What should I do to

improve? Am I at risk?• Do they need human feedback?• Can they trust us with their data? (The privacy calculus

– privacy, trust, and control)• Do not track them without their permission – opt out?

So what’s next?

What data do we currently have and who does what with the data, under what conditions, how

effective are these interventions, what impacts on their effectiveness and do students know?

So what’s next? (cont.)What data do we/they need in order to teach/learn better? How will we/they obtain the data? Who will

have access to the data, under what conditions, and for how long? Do we/they have the resources to act on the data and what are the implications if

we/they know and don’t act or don’t have the resources to act?

Page credit: https://eric.ed.gov/?id=EJ1117302

A detailed map “can lead to the erroneous conclusion that ethical

directions can be easily reached or to a statement so far in the stratosphere that

only angels can see and apply it” (Marx, 1998, p. 182)

Ethics in learning analytics: Selected examples 2013-2017

Thank you

Paul Prinsloo (Prof)Research Professor in Open Distance Learning (ODL)College of Economic and Management Sciences, Office number 3-15, Club 1, Hazelwood, P O Box 392Unisa, 0003, Republic of South Africa

T: +27 (0) 12 433 4719 (office)T: +27 (0) 82 3954 113 (mobile)

prinsp@unisa.ac.za Skype: paul.prinsloo59

Personal blog: http://opendistanceteachingandlearning.wordpress.com

Twitter profile: @14prinsp