Sunday, January 21, 2018

(Draft) Blueprint for 1 week data analytics course - for Individual Faculty and Institutional Stakeholders

Purpose

To analyse, and be able to draw / determine real-time; and predictive data about student and trainee participation, engagement, milestone-performance-competency gains.

Outline

Day 1 - meaningful data, milestone and performance metrics, logs and portfolio data and artefacts
Day 2 - what we have
Day 3 - what we need to clean up
Day 4 - dashboards, feedback loops
Day 5 - implementation-review-refine PDSA and rapid prototyping loops

Outcomes

To analyse, and be able to draw / determine real-time; and predictive data about student and trainee participation, engagement, milestone-performance-competency gains.


Faculty

Clinician-Educator
Medical Educator
Instructional Designer
Data Scientist
Educational Technologist
Institutional MedEd Administrator/Leader


Pre-session Survey and Needs Analysis

Who are stakeholders?
What are meaningful metrics?

Familiarity with data analytics concepts and tools - R, Python, Excel

https://datainformedelearning.blogspot.sg/2018/01/bite-or-byte-size-tips-for-using.html

https://edition.cnn.com/2018/03/20/opinions/facebook-privacy-blockchain-opinion-parker/index.html


http://scale.nus.edu.sg/programmes/edp/public-courses/data-sciences.html

     http://scale.nus.edu.sg/documents/EDP/Data-Analytics-Begins-With-Me.pdf

     http://scale.nus.edu.sg/documents/EDP/Data-Science-AI.pdf

     http://scale.nus.edu.sg/documents/EDP/Data-Analytics-for-Senior-IT-Managers.pdf


https://www.edx.org/course/subject/data-analysis-statistics

https://www.coursera.org/browse/data-science/data-analysis?languages=en


https://www.sp.edu.sg/wps/portal/vp-spws/pace.short.course.details?WCM_GLOBAL_CONTEXT=/lib-pace/internet/courses/analytics+for+educators+i

https://www.edx.org/course/data-analytics-learning-utarlingtonx-link5-10x

https://www.tableau.com/solutions/education-analytics

https://www.tableau.com/solutions/education-higher-ed-analytics

https://www.analyticsvidhya.com/learning-paths-data-science-business-analytics-business-intelligence-big-data/tableau-learning-path/

https://www.udemy.com/tableau-data-analytics-must-see-introduction-to-analytics/


https://analytics.jiscinvolve.org/wp/2014/10/22/learning-analytics-using-business-intelligence-systems/

"In terms of evaluation of learners, assessment should be in-process, not at the conclusion of a course in the form of an exam or a test. Let’s say we develop semantically-defined learning materials and ways to automatically compare learner-produced artifacts (in discussions, texts, papers) to the knowledge structure of a field. Our knowledge profile could then reflect how we compare to the knowledge architecture of a domain — i.e. “you are 64% on your way to being a psychologist” or “you are 38% on your way to being a statistician.”
above quote from George Siemens in interview below
https://www.oreilly.com/ideas/education-data-analytics-learning

https://www.crcpress.com/Data-Analytics-Applications-in-Education/Vanthienen-Witte/p/book/9781498769273

https://www.wiley.com/en-us/Data+Mining+and+Learning+Analytics%3A+Applications+in+Educational+Research-p-9781118998236

Martin, Florence and Ndoye, Abdou, Using Learning Analytics to Assess Student Learning in Online Courses, Journal of University Teaching & Learning Practice, 13(3), 2016. Available at:http://ro.uow.edu.au/jutlp/vol13/iss3/7

https://www.jisc.ac.uk/sites/default/files/learning-analytics-in-he-v2_0.pdf


Google image search "machine learning vs deep learning"

https://www.zendesk.com/blog/machine-learning-and-deep-learning/

https://www.digitaltrends.com/cool-tech/deep-learning-vs-machine-learning-explained/


http://www.straitstimes.com/singapore/engineering-the-future-of-medicine

https://www.comscore.com/Insights/Blog/Mobile-Matures-as-the-Cross-Platform-Era-Emerges

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