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Developing analytics in an instructional environment. (Pardo and Siemens…
Developing analytics in an instructional environment. (Pardo and Siemens 2014).
Transparency
What is collected, how it is collected and how it is manipulated (p446)
Palen and Dourish (2003) discourse, identity and temporality.
The use of data after a student has left an institution
Provides data for institutions to reflect on and improve practice over a period of years
Potential for predictive models (temporality)
The knowledge that the data will leave with the student may provide more insightful data during the study and improve trust.
How is it collected, stored and processed.
How long it is stored.
Allowing student access to that data after it has been collected
Potential to 'correct' data can be problematic (p446)
Could reduce value of the data by rendering it inaccurate and unsusable
raises a number of problems. Design research transparently enough that this scenario is unlikely to happen?
Updates to privacy policies after a user has entered an agreement- Google 2012
Student control over data
Zuckerberg/McNealy claim there is no such thing as privacy and
personal data should be available to all
Variation in the way student control over data is implemented
Security
Legislation to control, and potentially capitalise upon, data obtained is continually being updated to attempt to keep pace with emerging technology
Elgesem (1999), the European Union directives state that data must be collected for specific, explicit and legitimate purposes. (p446)
Breaches and misuse happen. Example of LinkedIn used in paper.
Access Vs Benefit
Anonymisation of data: data can be either useful or perfectly anonymous, but never both (Narayanan & Shmatikov, 2010; Ohm, 2010).
Accountability and assessment
Terms of use [by an institution] should be carefully stated as to delimit the processes that are being applied and, at the same time, convey the idea that students must remain in control of their data at every moment. (p446)
Detailed access policy should be implemented at the start of the framework.
Users of the data must be clearly informed of the type of information they are manipulating (p447)
Safeguards robustness of process (p447)
Continually assess fitness for purpose of framework (p447)
Open Student Model
Stakeholders: students, instructors, admin staff