Open Education Resources

Open Education Resources (OERs) are free or low-cost openly licensed educational materials for use in teaching, learning, and research. As long as proper attribution is assigned, they can be revised, edited, and republished to meet various educational needs. The William and Flora Hewlett Foundation (2016) has defined OERs as “teaching, learning, and research resources that reside in the public domain or have been released under an intellectual property license that permits their free use and re-purposing by others. Open educational resources include full courses, course materials, modules, textbooks, streaming videos, tests, software, and any other tools, materials, or techniques used to support access to knowledge.” Another useful definition from the OER Commons (2018) includes the following, “Open Educational Resources are teaching and learning materials that you may freely use and reuse, without charge. OER often have a Creative Commons or GNU license that state specifically how the material may be used, reused, adapted, and shared.”

Specific data regarding how much money institutions have spent to convert courses to OERs are difficult to acquire. Recent articles have claimed that University of Maryland University College, the University of Minnesota, Oregon State, and the Washington State Community College systems have all begun to convert courses to OERs, resulting in substantial savings for students. The University of Connecticut has engaged faculty in an OER conversion project, as has Tidewater Community College in Virginia. Preliminary results have been very positive — the vast majority of students have reported satisfaction with the free materials provided. An OER fellowship program based within the University of Hawaii Community College system has also resulted in savings for students via conversion of existing courses to OERs (Oshiro & Risely, 2016).The State of Michigan’s OER Textbook Initiative has been very successful at reducing costs to students; the British Columbia University system in Canada has achieved similar results. The Ohio governments efforts to convert courses to OERs include grant funding for faculty within the state university system. An OER initiative at UMass Amherst has also been successful. An initiative based within the UCLA library pays faculty small stipends to convert courses to OERs (Salem, 2017).

The New York State University system is investing millions in OER conversion, as is California. When New York and California are on board, the rest of the nation usually follows suit. More and more states are beginning to fund efforts to convert courses to OERs, and the federal government recently announced $5 million in grants for institutions seeking to begin this work (Dimeo, 2014).

While the savings inherent in OER adoption are apparent and is being continually tracked in media such as the International Journal of Open Educational Resources, it is the potential for inclusion in advanced ecosystems that make them exceptionally interesting from a forward-looking perspective.

Convergence

Imagine if you will a point soon, where from the moment an author starts creating an OER, provenance is recorded to a blockchain implementation.

In parallel, the content is being federated and semantically analyzed to provide extensive data about the learning object and the objectives it meets. When completed this resource becomes one object in a multitude of OEKs that have been semantically organized and curated in a manner that is highly searchable by any institution.

Now imagine a student entering into a university with a career pathway custom tailored to their needs by Al. As the student moves from class to class their progress is being monitored in real-time, with suggestions provided to their advisors and faculty to help provide the student with guidance at exactly the right moment. Complementing the advising function, the Al is also sorting through all available resources to provide the student with the best possible materials that are also aligned with their previous performance and personality characteristics. At each step artifacts developed by the student, as well as granular level performance data are written to the blockchain for use by advisors or by employers after graduation. This, we believe, is a vision of the possible in online higher education.

Questions for reflection

• 1. What should universities do when an Al predicts (with a very high degree of accuracy) that a student has virtually no chance of successfully completing an individual course or program of study? Do administrators continue to divert resources to support that student? Or do they shift resources to student who have a higher chance of succeeding?
• 2. How do we use algorithms that predict what majors a student may be most successful in, without giving the impression of pressuring the student to pursue a line of study they may not be interested in?
• 3. How do we reconcile the immutability of blockchain with the right to be forgotten?
• 4. Why might administrators be reluctant to give students control of their own data via blockchain?
• 5. How do we incentivize faculty and universities to produce high quality OEK’s?
• 6. Must OEK’s be completely free or are there models that will allow for commoditizing OEK’s that make them highly affordable, while ensuring that funds exist for continued development?