caselets - invitation (and request) to contribute

[Artur Dubrawski]

We got very little in terms of response to this request.

So, before taking any other measures ;)  let me suggest that we have a 
1/2 day working session
with nice lunch or dinner food provided courtesy of the Auton Lab at the 
end, so that we could
help Karen produce useful and robust caselets.

Please indicate your availability thorough this doodle poll:
https://doodle.com/poll/upcwwz367gnpridu

Thanks
Artur

On 1/24/2018 4:02 PM, Artur Dubrawski wrote:
> Team,
>
> Karen has been researching how to bridge an important gap in data 
> science training curricula.
> Everyone who tried to teach (or learn) data science will probably 
> agree that lectures and hands-on
> homework do not fully prepare students to be successful data 
> scientists in the real-world right
> after graduation. Some of that stems from the lack of self-confidence 
> due to little to none practical
> experience. Karen hypothesizes that this could be partially remedied 
> via self-assessment, if
> the students were exposed to real-world challenges in small doses, 
> chunk-by-chunk, and practice
> how to resolve them in an exercise.
>
> She has designed a little experiment (and won a rather humble amount 
> of funding for running it)
> and she will use the ongoing course on Applied Data Science as a 
> platform to give it a try. For the sake
> of comprehensiveness of problem coverage and to ensure that this 
> attempt is in fact meaningful,
> she is seeking our help with producing "caselets" - a little 
> interactive assignments - to help populate
> her engine.
>
> See below for more info and please reach out to Karen if you could 
> (and I truly hope you would) help.
>
> Cheers,
> Artur
>
> =======================================
> We are building a repository of caselets (caselet = lightweighted case 
> study) to help the beginner data scientists to build up data science 
> problem solving skills using authentic problems and data sources. The 
> caselets will be deployed in an online learning environment where 
> timely feedback and explanation will be provided when users work 
> through the problems. This is part of the CMU Simon Initiative funded 
> project on “Accelerated Apprenticeship” with the goal to teach data 
> science problem solving skills at scale.
>
> You're invited to contribute to the repository given your experience 
> in solving tough real world problems and/or the mentoring students or 
> interns in our lab.
>
> Here are the steps to get started:
>
> 1. Pick a domain and dataset. You may refer to a list here, but feel 
> free to use your own data sources; https://tinyurl.com/ybdowtn3
>
> 2. Pick a subset of skills you want to target. This list gives you 
> some idea to start with. It will be helpful to reflect on the tricky 
> problems you’ve encountered yourself in your project or those observed 
> when mentoring students; https://tinyurl.com/y888mxru
>
> 3. Author caselet. A caselet a) problem context; b) data description 
> (in the form of tabular summary or plots) ; c) a list of questions 
> (5-7)  multiple choices questions with correct answers and 
> explanations provided; a sample caselet write up can be found 
> here.https://tinyurl.com/y7y8bnnu <https://tinyurl.com/y7y8bnnu>
>
>
> We are aiming to have the first batch of caselets ready to be used by 
> Artur’s students right after spring break. So we need to have drafts 
> ready by Feb 16th and followed by internal review.  Please send Karen 
> an email (karenchen at cmu.edu <mailto:karenchen at cmu.edu>) if you’re 
> interested in being part of it.
>
> Thanks in advance!
>
> Karen (Lujie) Chen
>
> Ph.D. Candidate in Information Systems, Heinz College
> PIER Fellow (Program of Interdisciplinary Educational Research)
> Member of Auton Lab, Robotics Institute
> Newell-Simon Hall 3124
> Carnegie Mellon University
> Pittsburgh, PA 15213
>
>

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