Universities are really keen to employ graduate students at low wages to teach classes, so they're keen to admit people to their programs, but they're rather less keen to employ them once they've graduated and should enjoy a full academic salary, and that's a problem. "After graduation, they often end up as part-time instructors vying for limited tenure-track positions, unsure of how to market their skills in the non-academic job market." This article references a report released yesterday by the Council of Canadian Academies, Degrees of Success (225 page PDF), documenting the problem. It was sponsored by Innovation, Science and Economic Development Canada (ISED). The report looks at the economic return from a PhD, but also at the prevailing sentiment that working outside academia "is a failure and that seeking work outside academia is a betrayal of graduates’ and faculty ideals", their supervisors' disinterest in preparing graduates for non-academic positions, and belief in the private sector that PhD graduates "come from 'another world' and that they lack certain essential professional skills."
Towards Understanding the Students’ Acceptance of MOOCs: A Unified Theory of Acceptance and Use of Technology (UTAUT)
Maryam Muti Altalhi, International Journal of Emerging Technologies in Learning, 2021/01/27
This is a metastudy (17 page PDF) suggesting that an individual's acceptance of a MOOC is "substantially affected by its performance expectancy, effort expectancy, social influence, self-efficacy, attitude, and facilitating conditions," resulting in an acceptance model called UTAUT. All of this makes sense to me, and it stands to reason that evaluating specific influences (for example, using the BIDR scale to evaluate social influence on a person) would offer a more fine-grained analysis of whether a particular MOOC will be accepted (and therefore used) by a particular individual or group of people. The real question (to my mind) is whether these categories are the best categories for this sort of analysis. Yes, things like 'effort' and 'attitude' correspond to intuitive 'natural kinds', but is it best to employ these (as opposed to hitherto undiscovered and unnamed categories discoverable by, say, deep learning; see the Hinton discussion below)?
We've talked in the past about the ability of artificial intelligence to evaluate student performance. "It is believed that artificial intelligence technology can almost completely replace the teacher as the system has a built-in assessment function," write the authors. But should it? This paper (15 page PDF) raises a new question: what is the influence of automated assessment, as compared to the more human version? The study, though small, looked at students in I.M. Sechenov First Moscow State Medical University in Russia and Wuxi Institute of Technology in China. One group took formative tests assessed by a computer system, the other took the same type of tests on paper and assessed by a teacher. Formative evaluations were higher using automated systems, and correspondingly, those using automated assessments did better on summative evaluations as well. The authors suggest the effect is caused by the immediacy of feedback from automated assessment. That said, the differences are small, and not always consistent, and dwarfed by the effect of individualized instruction.
MOOC As an Enabler for Achieving Professional Competence: Problem-Solving Aspect
Anna Berestova, Larisa Kondratenko, Liudmila Lobuteva, Alisa Lobuteva, Iza Berechikidze, International Journal of Emerging Technologies in Learning, 2021/01/27
As always, the sample size is too small to draw quantitative conclusions, but I'm so happy to be reading MOOC research from Russia I'll overlook it for now. This study (13 page PDF) offers a MOOC to a control group "in the traditional way", ie., as an xMOOC, and one to an experimental group adding "collaborative learning and decision-making assignments" in an effort to increase engagement. As the diagram shows, it experiment certainly worked for this group. My feeling is that these enhancements would work in general, which is why we included them in the cMOOC, but of course a much wider and standardized study would be required to confirm this. Meanwhile, if anyone has information about good sources for e-learning news in Russia, please let me know.
This is a transcript of a conversation with deep learning pioneer Geoffrey Hinton and I have to say, it's riveting reading. It begins by discussing capsules: "what capsules are trying to do is recognize whole objects by recognizing their parts and the relationships between the parts." Google recently filed a patent for this idea. Once you have a set of capsules you're confident belong to a single object, you begin assigning names to them (when we're learning a language, we're not learning what things there are, we're learning what names to give things we already know exist). That's just the tip; there's a great section in the middle about the (non-)role of back propagation in human learning and Hinton's own (unsuccessful) theory of perceptual learning. And much much more.
We'll put this item into the category of 'self-organizing networks' though it's really more characteristic of mob behaviour, since it's based on a single source of information available to everyone, namely, stock market prices. And the practice is as old as the market itself: organizing a bull run by selecting a low-cap low-value heavily shorted stock to promote, running its price as high as it will go. The players, though, are not well-heeled Wall Street executives, who have made this sort of thing into a lucrative art form, but members of the Reddit social network. This essentially creates a second source of information available to everyone, namely, the conversation on the r/Wallstreetbets subreddit. It all reminds me of the old BlogShares game that ran from 2003 to 2011.
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