Employers want to know what credentials are credentialing, and they want to hear it in their own language. The temptation will be to convince faculty and others to revise descriptions, however the opportunity is in leaving that and instead seeking their consensus and comfort with interpreting their descriptions into the languages of employers.

• Page 17: Top 5 most important factors for creating an effective teaching and learning ecosystem: Having a strong leadership and vision (45%) is the #1 (next highest is 15%)
• Page 20: *83% of higher education respondents said that it was important for institutions to provide studens with skills-based learning alongside their academic education. *
• Page 26: Participants identified several challenges in fostering a a culture of lifelong learning for professionals, including: 89% Clear learning objectives
• Page 7: Real-world experiential and work-based learning are no longer fringe; 4 in 5 see these as essential.

Opportunity to go deep here. Bread crumb is to check out the issuer directory with Credential Engine, where they are inviting institutions to publish details about their credentials in a standardized format (CTDL) that will hopefully one day be consumed by connections like those hinted at here.

This could play into Credential Quality Assurance work in Higher Ed.

Seeds of doubt on MC quality

The Lancet. (2021, April 16). Quantity > quality? The magnitude of #COVID19 research of questionable methodological quality reveals an urgent need to optimise clinical trial research—But how? A new @LancetGH Series discusses challenges and solutions. Read https://t.co/z4SluR3yuh 1/5 https://t.co/94RRVT0qhF [Tweet]. @TheLancet. https://twitter.com/TheLancet/status/1383027527233515520

Why crowded meetings and conference rooms make you so, so tired—The Washington Post. (n.d.). Retrieved July 19, 2021, from https://www.washingtonpost.com/business/2019/06/06/why-crowded-meetings-conference-rooms-make-you-so-so-tired/

Moss, A. J., Rosenzweig, C., Jaffe, S. N., Gautam, R., Robinson, J., & Litman, L. (2021). Bots or inattentive humans? Identifying sources of low-quality data in online platforms [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/wr8ds

Calster, B. V., Wynants, L., Riley, R. D., Smeden, M. van, & Collins, G. S. (2021). Methodology over metrics: Current scientific standards are a disservice to patients and society. Journal of Clinical Epidemiology, 0(0). https://doi.org/10.1016/j.jclinepi.2021.05.018

Special Track 3. (n.d.). Data for Policy CIC. Retrieved 8 March 2021, from https://dataforpolicy.org/data-fof-policy-2021/special-track-3/

NISO Plus 2021: Quality and reliability of preprints. (n.d.). Retrieved 19 February 2021, from https://nisoplus21.sched.com/event/fMlf/quality-and-reliability-of-preprints

https://www.reddit.com/r/BehSciMeta/comments/jkzirk/ideas_for_discussion_how_to_manage_online/?utm_source=share&utm_medium=web2x&context=3

Lo, C., Mani, N., Kartushina, N., Mayor, J., & Hermes, J. (2021, February 11). e-Babylab: An Open-source Browser-based Tool for Unmoderated Online Developmental Studies. https://doi.org/10.31234/osf.io/u73sy

Mambrini. A. Baronchelli. A. Starnini. M. Marinazzo. D. De Domenico, M. (2020) .PRINCIPIA: a Decentralized Peer-Review Ecosystem. Retrieved from: chrome-extension://bjfhmglciegochdpefhhlphglcehbmek/pdfjs/web/viewer.html?file=https%3A%2F%2Farxiv.org%2Fpdf%2F2008.09011.pdf

Soderberg, C. K., Errington, T., Schiavone, S. R., Bottesini, J. G., Thorn, F. S., Vazire, S., Esterling, K. M., & Nosek, B. A. (2020). Research Quality of Registered Reports Compared to the Traditional Publishing Model. MetaArXiv. https://doi.org/10.31222/osf.io/7x9vy

Fiona Fidler: Misinterpretations of evidence, and worse misinterpretations of evidence (Video). (n.d.). Metascience.com. Retrieved 29 October 2020, from https://metascience.com/events/metascience-2019-symposium/fiona-fidler-misinterpretations-of-evidence/

Scientist, N. (n.d.). How to sniff out the good coronavirus studies from the bad. New Scientist. Retrieved July 1, 2020, from https://www.newscientist.com/article/2242835-how-to-sniff-out-the-good-coronavirus-studies-from-the-bad/

van Smeden, M., Lash, T. L., & Groenwold, R. H. H. (2020). Reflection on modern methods: Five myths about measurement error in epidemiological research. International Journal of Epidemiology, 49(1), 338–347. https://doi.org/10.1093/ije/dyz251

Lancet, T. (2020). Research and higher education in the time of COVID-19. The Lancet, 396(10251), 583. https://doi.org/10.1016/S0140-6736(20)31818-3

Nieto, I., Navas, J. F., & Vazquez, C. (2020). The quality of research on mental health effects of the COVID-19 pandemic: A note of caution after a systematic review. https://doi.org/10.31234/osf.io/ndgkj

Project background – Critical Analysis Project. (n.d.). Retrieved July 10, 2020, from http://critical-analysis.org/project-background/

An analyst’s job is never done – GSS. (n.d.). Retrieved July 3, 2020, from https://gss.civilservice.gov.uk/blog/an-analysts-job-is-never-done/

BehSciMeta repost. (2020, June 17). "Reproducibility scores for behavioural science: what are the merits and drawbacks?" Reddit. https://www.reddit.com/r/BehSciMeta/comments/har2np/reproducibility_scores_for_behavioural_science/

Lindsay, D. S. (2020, May 29). Enhancing Peer Review of Scientific Reports. Psychonomic Society Featured Content. https://featuredcontent.psychonomic.org/enhancing-peer-review-of-scientific-reports/

Lakens, D. (2020). Pandemic researchers—Recruit your own best critics. Nature, 581(7807), 121–121. https://doi.org/10.1038/d41586-020-01392-8