MOOCs as LIS Professional Development Platforms: Evaluating and Refining SJSU's First Not-for-Credit MOOC
Bibliographic record
Abstract
IntroductionPundits and mass media have argued that massive open online courses (MOOCs) can transform education in the 21st century, presenting an opportunity for global, open learning. Many have greeted MOOCs with enthusiastic acclaim and described them with such words as storming (Clarke, 2013, p. 403), unprecedented (Schwartz, 2013, p. 1), and revolutionar' (Morrison, 2013, p. 1). Understanding this new, evolving landscape and its potential to make learning more accessible and affordable should be a priority for educators, and is already being scrutinized by faculty, administrators, and librarians.Beyond for-credit offerings, some schools are exploring MOOCs as a means to promote lifelong learning and professional development. In their MOOCs, LIS schools have offered timely content and learning opportunities for practitioners far and wide. Either on the sideline or on the frontline, LIS administrators and faculty are beginning to address the potential and pitfalls of MOOCs in a spirit of risk-taking and environmental scanning of future opportunities, especially in the broad area of online education.In this paper, we begin to empirically address if MOOCs can, for LIS programs, fill a role and serve new populations of learners within large-scale learning environments. To do so, we use a MOOC we designed, built, and instructed as a test bed.In Fall 2013, the SJSU School of Library and Information Science (SLIS) offered its first large-scale, open online course, the Hyperlinked Library (HL) MOOC. The MOOC was intended to serve as a professional development opportunity for students working in LIS environments. Unlike SJSU's partnership with Udacity, the SLIS's HL MOOC was offered free of charge, not for academic credit, and was taught in a bespoke learning environment.For this paper, the following research questions frame our inquiry:* Is there potential for LIS programs to serve new populations of learners using MOOC environments?* Can this MOOC model help LIS practitioners develop professionally?This paper begins with a brief review of the literature on MOOCs and large-scale professional development, before providing background about the HL MOOC. Next, we detail the findings of our analysis of preand post-course online survey responses about expectations and motivations for enrolling in the MOOC, opinions regarding the course design, course content, and perceptions regarding the course's value as a professional development venue. The paper finishes with a discussion regarding our takeaways for refining the platform and course design, as well as insights regarding the use of large-scale learning environments for professional development in LIS.Literature ReviewA Brief History of MOOCsMOOC-what the acronym describes is open to interpretation. While the M in MOOC stands for massive, there exists no hard-and-fast rule that defines what size a course needs to be to fit the name (Fasimpaur, 2013, p. 13). While openness is the primary means of differentiating MOOCs from other online courses (Fasimpaur, 2013), varying interpretations of how open a course needs to be, also confuse the issue. MOOCs are most often free and open to anyone, but some courses may restrict class enrollment on a first-come firstserved basis. Some may also require fees. The second O in MOOC is a given-the course must be offered online. Even then, however, many students have taken it upon themselves to meet in-person with others taking the same MOOC, as evinced by the nearly 30,000 people who have signed up to join one of the Coursera communities on Meetup.com, a site dedicated to helping groups with similar interests meet face-to-face (Meetup.com, 2013).The term MOOC was first used in 2008, by George Siemens and Stephen Downes, to describe a free, online course of 2,300 students taught at the University of Manitoba (Educause, 2011). Since then, largescale learning opportunities have multiplied, including varying forms and sizes of for-profit and for-credit MOOCs along with strategic partnerships with organizations like Coursera and Instructure. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".