Democratizing higher education: Exploring MOOC use among those who cannot afford a formal education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>Massive open online courses (“MOOCs”) provide free access to higher education for anyone with Internet access. MOOCs are considered a means for democratizing education. These courses will hopefully provide an opportunity for individuals to learn from the best educators in the world, as well as help expand their personal networks, and facilitate their career development. However, research thus far shows that the majority of people taking advantage of these courses are already employed, have post-secondary degrees, and have encountered few barriers related to the affordability of higher education. Little is known about MOOC learners with financial constraints and who do not fit the typical profile of MOOC learners. This paper presents the results of the analysis of data from six Coursera courses offered by the University of Michigan from fall 2012 through winter 2013. In this analysis learners who self-identified as being unable to afford to pursue a formal education (the target group) were contrasted to other learners (the comparison group) in terms of demographics, motivations, course enrollment, engagement and performance. Learners in the target group were primarily male and over 25 years old. A statistically significant portion of the target group held less than a 4-year college degree than the comparison group. Target learners were also significantly underrepresented in the enrollment of the courses examined here. Although the comparison group had a significantly higher completion rate overall than the target group, the target group had a statistically significant higher rate of completing courses with certificates of distinction. This article provides a discussion of these results and suggests how MOOCs could be adapted to better address the needs of learners who feel financially unable to pursue a more traditional path to a post-secondary education. </p>
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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 it