Sharon Slade Open University senior lecturer
Development of ethical policies for student data
Mitchell Stevens assoc of Ed at Stanford, director of data policy,
George Siemens prof at UT at Arlington, press of the society of learning analytics
Gene Gloechner School of Ed CSU IRB
What is the purpose of LA:
Sharon:
Use data to influence student outcomes - learning Analytics
Individual targeted interventions to have a positive effect
Predictive modeling
George - practices for a range of areas, not comfortable with business Analytics
LA - any activity involving the learner as the center
LMS, social media, wearables,
Mitchell - learning sciences developed in last 20 yrs. funded by NSF, largely focused in STEM k-12
2012 universities created MOOCS mania. Computer sciences really jumped into MOOCS
Digital mediated instruction - DMI
Gene - reverse of standard research models, analysis data to get research questions . What are the ethical questions involved. How is information going to be used? How do we info the students about the use of data?
Positive usages of LA, how LA can positively effect the interaction of students & faculty and student outcomes:
George: greater sense of autonomy for the student using neuroscience ( not behaviorism 2.0) student should see all data in an accessible and understandable format, what do we value?
Sharon: we don't really understand what the use of LA is in the university. Student vs institutional goals.
Mitchell: Belmont report
Managerial approach (efficiencies)to start with but now moving towards improving instructional interactions
Gene: mood meter app as an example for the use of analytics.
Tom Siller, how do we help students use the LA?
George: How do we get students to next step decisions, complexity since view, don't drop all data to the students, start small and then move forward. Use data to form next steps...
Sean Burns, LA CSU, what kinds of interventions do you suggest, where can we go?
Sharon: how confident are we in the predictive data? Context with explanation, relate to info about the student.
Mitchell: there's a lot of low hanging fruit with which we can use to help with the needs of undergrads. Operations need to be reorganized. How college works? Academically adrift
What are the potential risks in using LA with student/faculty interactions?
Sharon: make sure to keep in mind the student is a person, information gathered isn't the whole picture. Predictive modeling, doesn't necessarily means the next student with the same data is going to behave in the same way. An individual is more than their data.
George: concern that are we going to liberate the learner or dumb down the material?
Mitchell: how hard should college be? What kind of environment do we what college to be? Dangerous to be a "click through" experience
Faculty usage, don't we already have enough?
Mitchell: instant feedback, get more info on a large scale to see what concepts/tasks are being learned or not.
Potential risks of using student data?
Mitchell: people react to situations differently. "Click Through U" data becomes perspective and/or prospective.
George: numbers are precise but learning is a sloppy, social experience.
Student affairs - advisors support structures, what is their roll?
Mitchell: it's a widely distributed enterprise, division of labor
Ethical hazards/issues of learning or academic achievement
George: data is transactional, like FB, who owns the data? Non use, overlook the use of the data to assist students.
Gene: who owns the data?
Mitchell: what should the first principles be compliance? Data are joint ventures, data exists at the intersection of several parties.
Sharon: it's not ethical to ignore the information we have. There's just too much data so we need to pair down which data to use to help students.
Ethical issues regarding privacy issues, consent, etc.
Gene: the goal us to help the students.
The ethical use of student data at open university
Sharon: opt out option, informed consent, prior to registration, transparency
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