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** Note: This text was largely written by Sonja Fritzsche and Andrew Christlieb based off of conversations at a series of conversations during Fall 2018 with faculty from around the institution who have an interest in thinking about the values we must embody as we consider the technical futures of education.
The MSU Education 2035 initiative holds that technology and AI-enhanced learning must be adopted primarily for education improvement and not as a dollar-saving opportunity. When done well these are not inexpensive tools.
During fall 2018, we met three times to discuss a core values statement. The various participants found the following four values and subcategories/questions to be the guiding principles for successful technology adoption on campus in the area of teaching and learning. Only in this way will we be able to think critically around solving real problems today and into the future, rather than just streamlining and improving teaching. All activity of the Education 2035 initiative embodies these values.
Community – conversation, collaboration, shared decision making from community teams/brains that include faculty, students, staff, and administrators.
Diversity, Equity, and Inclusion – diversity of users reflected in the diversity of work groups, open source/open access, algorithmic literary, digital literacy, continual recognition of and measures taken to combat implicit data bias that reinforce existing cultural power structures
Transparency – student and faculty data usage ethics, data privacy rights, a culture of data usage consent, data implicit bias, decision making and policies, transparency of data, algorithm, process
- Data collection (D2L issues?): what gets collected and how long is it stored?
- Data ownership: who owns it?
- Data control: who gets control? How can students edit/adjudicate?
- Data access: who sees what, how is the data displayed, how do I ask for it?
- What happens when the company is sold?
- Data use (D2L): how does D2L use the data, how does MSU use the data, how to instructors use the data, how is any of this communicated to the students?
- Data communication: how is data shown?
Accountability – equity audit process, development of a university code of ethics for data ownership, data ethics MSU data privacy statement and syllabus information, assessment of compliance with first three values (Community, DEI, and Transparency), tools must solve real pedagogical problems, not just innovate for innovation’s sake.