Posts tagged ‘Excel’

Developer Bootcamps and Computing Education: Tech Done Right Podcast

I was so excited to be invited to do this podcast with Noel Rappin (my first PhD student) and Jeff Casimir who runs the Turing Academy bootcamp. I learned a lot about bootcamps from Jeff, whom I was pleased to learn is a data geek and measures things pretty carefully.  Two of my favorite insights:

  • Female students are more likely to graduate from the bootcamp. They are more likely than male graduates to leave before six months on the job.
  • Students who skip college and go straight to bootcamp (as Peter Thiel encourages students to do) have a harder time graduating and getting a job. That latter part might be ageism, bias against younger job-seekers.

I recommend the podcast — we had a fun discussion.

How do people learn computing? Who learns best from traditional computer science education and who from bootcamps? How can we teach people who are not developers but who need to learn some programming to do their jobs? Jeff Casimir, the founder of Turing academy, and Georgia Tech’s Mark Guzdial, one of the founders of the International Computing Education Research conference, join Noel to answer these questions and also explain why Excel is both the best and the worst thing in the world.

Source: Tech Done Right Episode 20: Developer Bootcamps and Computing Education with Jeff Casimir and Mark Guzdia

September 29, 2017 at 7:00 am 3 comments

Surveying Media Computation Students: Self-Efficacy, Worked Examples, Python, and Excel

I’m back from Oxford, after an intense six weeks of teaching “Computational Freakonomics” and “Media Computation.” Since I did new things in Media Computation this term, I put together a little survey to get students’ feedback on what I did — not for research publication, but to inform me as a teacher.

It’s complicated to interpret their responses.  Only 11 of my 22 students completed my survey, so the results may not be representative of the whole class.  (The class was 10 males and 12 females. I didn’t ask about gender on the survey, so I don’t know gender of the respondents.) The first thing I was wondering was whether the worked examples was perceived by students as helping them learn. “I found it useful to type in Python programs and figure them out at the start of class.” 4 strongly agree, 6 agree, 1 neutral.

That seems generally positive — students thought that the worked examples were useful.  How about helping with Python syntax?  “Getting the characters exactly right (the syntax of Python) was difficult.” 2 agree, 1 neutral, 8 disagree.  That’s in the right direction.

In the written portion, several students commented that they liked being able to focus on “understanding” programs “rather than just executing them.”  One student even suggested that I could have questions about the program after they studied them, or I could have them make a change to the program afterward, to demonstrate understanding.  I loved this idea, and particularly loved that it was suggested by a student.  It indicates seeing a value in understanding programming, even before doing programming, while seeing value in that, too.  This worked examples approach really does lead to a different way of thinking about introductory computer science: Programs as something to study first, before designing and engineering them.

When I asked students what their favorite part of the course was, and what their least favorite part of the course was, Excel showed up on both lists (though more often on the least favorite part).  Here’s one of the questions that stymied me to interpret: “Python is harder to learn and use than Excel.”  Could not be a more perfect bell curve — what does that mean?!?

“I wish I could have learned more Excel in this course.”  An almost perfectly uniform distribution!

Their reaction to Excel is so interesting.  On the written parts of the survey, they told me how important it was for them to learn Excel, that it was very important for their careers.  But they did not really like doing something as inauthentic (my word, not their’s) as pixel manipulation in Excel.  They wished they could have done something more useful, like computing “expenses.”

The responses above suggest to me a hypothesis: The students don’t really know how to think about Excel in relation to Python. It’s as if they’re two different things, not two forms of the same thing.  I was hoping for more of the latter, by doing pixel manipulations in both Python and Excel. This may be someplace where prior understanding influences the future understanding.  I suspect that the students classify these things as.

  • “Excel is for business. It’s not for computing.  Doing pixel manipulations in Excel is just weird and painful.”
  • “Python is for computing.  I have to go through it, but it doesn’t really have much to do with my future career.”  On the statement, “Learning programming as we have in this course is not useful to me,” 3 were neutral, and 8 disagreed.  I read that as, “It’s okay. Sorta.”

Something that I always worry about: Are we helping students to develop their sense of self-efficacy in an introductory course, especially for non-majors?

“I am more confident using computers now, after taking this course.”  Quite positive: 10 agree, 1 neutral.

“I think differently about computers and how they work since taking this class.”  Could not get much more positive: 8 strongly agree, 6 agree!

And yet, “I am not the kind of person who is good with computers.”  Mostly, students agree with that: 3 strongly agree, 4 agree, 1 neutral, 3 disagree.  One average, my students still don’t see themselves as among the people who are “good” with computers.

There was lots for me to be happy about.  Some students said that the lectures on algorithmic complexity and the storage hierarchy were among their favorites; that they would have liked to have learned more about the “big questions” of CS; and they they liked writing programs.  On the statement, “I learned interesting and useful computer science in this course,” 3 students strongly agreed, and 8 agreed.  They got that this was about computer science, and some of them even found that useful.

Even in a class of only 22, even seeing them every day for hours, even with grading all their papers — I’m still surprised, intrigued, and confounded by how they think about all of this.  That’s fine by me. As a teacher and a researcher, my job isn’t done yet.

August 8, 2012 at 9:43 am 8 comments

Pixel Spreadsheet in a Media Computation class: Exposing data abstraction with Excel

I mentioned awhile ago that some undergraduates built for me a new tool for converting from images to spreadsheets, and back again.  It allows us to do image manipulations via spreadsheet tools like Excel.  More importantly, it exposes the data abstractions in picture files (turning JPEGs into columns of x,y and RGB), and makes the lower level data malleable.

I’m using this tool in the Media Computation course that I’m teaching this summer.  Normally, CS1315 (the course I’m teaching) includes labs on Word, Excel, and Powerpoint, but there’s no sense of “lab” in these compressed courses.  And I bet that most of my students know a lot about Office applications already.  So I asked them at the start of class: What did they want to learn about Office applications?  Several students said that they’d like to learn to use formulas in interesting ways in Excel.

I’ve come up with a homework assignment where students do Media Computation using unusual Excel formulas (e.g., using IF, AND, and COUNTIF).  I lectured on Excel on Thursday in support of this assignment, and it was rough.  Things that I had worked out in Windows Excel failed or worked differently when doing a live coding session in MacOS Excel (e.g., the FREQUENCY function worked differently, or not at all — hard to tell).  Fortunately, we figured it out, but I got a new appreciation of how non-portable the edge of Excel functions can be.

My students are working on this assignment this week, and I’ll let you know how it goes.  Based on the questions I’m getting already, it’s challenging for the students.  Excel functions are hidden, invisible when you look at a spreadsheet until you click on the right cell.  Much of how you do things in Excel, the process, is invisible from watching the screen, e.g., shift-clicking to select a range. So, they’re having a hard time discerning exactly how I did what I did in class.

Maybe they’re learning a greater appreciation for doing all this in Python, rather than Excel.

July 24, 2012 at 1:36 am 9 comments

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