Reports from an adventurous learning journey (Part 1)

journey1Beginning with December – when in a blog entry I recommended a book on Machine Learning with R – I initiated a new personal enterprise: Learning the statistical programming language R to acquire competencies of a data scientist. With „learning enterprise“ I do not mean to get just interested in a new subject, to read from time to time a relevant book and to look into some web-based tutorials. No, with „enterprise“ I mean a much bigger undertaking, to focus and concentrate several months on a systematic study for a new set of qualifications. 

I started with a nine-course introduction to data science, taught by three professors of  John Hopkins University and offered via the MOOC-platform Coursera. After I finished successfully two courses with certificates I turned impatient with teaching style and course philosophy.  My dissatisfaction was not so much caused by these professors or by this special course design but had more abstract reasons: As an experienced adult learner with a very attractive job I had no need to provide evidence of some acquired general qualifications as most of the course participants did. I was rather looking for some kind of Coursera-Certificate-ToolboxPersonal Knowledge which I could combine with my own life and working experiences.  After a long time again in the learner role, I found it very strange that I had to spend much time on material arranged by other people which I  could not need and had, on the other hand, to learn material superficially or to skip altogether, which was very useful for me. 

To exemplify the situation more in detail: In the third course on Getting and Cleaning Data we had to learn different ways to get data into the R environment: To download it with URLs, to scrap it from web pages, to load it from Excel sheets, to collect it via an API. But – for instance – we didn’t learn how to get data from an SQL database. I appreciate Coursera-Certificate-R-Programmingthat all these different methods are potentially important and as a data scientist one should know how to apply them. I also understand that it is impossible to learn all the different cases in detail. But I disagree that the best teaching method is an exemplary journey through some of these potentially important methods. A teaching strategy which would have better suited me would haven been a systematic and complete list of all methods with their pointers to the relevant R packages and supplemented with some prototypical program snippets to get started with. After getting a systematic overview and helpful material for future usage, one should have the opportunity to choose two or three methods and learn the nitty-gritty in practical assignments.

How to support the self-determined learner?


Consequently, I stopped my course attendance and started to look for tutorials, courses and other learning material on my own. I have to confess that I didn’t know how exciting but also how difficult this self-paced and self-determined learning approach was respectively is. Between us educators, we are talking routinely about self-determined learning , but it is quite a different thing to experience related problems yourself.


On a non-regular basis, I will share my experiences from this adventurous learning journey in a series of blog comments. This was the first one.


Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology/Psychologie Canadienne, 49(3), 182–185.
Deci, E. L., & Ryan, R. M. (2008). Handbook of Self-Determination Research.
Ryan, R. M., & Deci, E. L. (2008). From Ego Depletion to Vitality: Theory and Findings Concerning the Facilitation of Energy Available to the Self. Social and Personality Psychology Compass, 2(2), 702–717.
Deci, E. L., & Ryan, R. M. (2012). Overview of self-determination theory. The Oxford Handbook of Human Motivation, 85.
Polanyi, M. (1974). Personal Knowledge: Towards a Post-critical Philosophy (Corr. Ed). University of Chicago Pr.
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