Posts tagged ‘BPC’

Summarizing the Research on Designing Programming Languages to be Easier to Learn: NSF CS Ed Community Meeting

I’m at the NSF STEM+Computing and Broadening Participation in Computing Community Meeting.  At our ECEP meeting on Saturday, we heard from White House Champion of Change Jane Margolis.  She did a great job of getting our states to think about how to change their state plans to emphasize diversity and equity — more on that in a future blog post.

12657757_10101604211025169_5607395108039854037_o

I moderated a panel yesterday on how to integrate computing education into schools of education.  Here’s the description of the session — again, more later on this.

Integrating Computing Education into Preservice Teacher Development Programs  

(Mark Guzdial (moderator), Leigh Ann DeLyser, Joanna Goode, Yasmin Kafai, Aman Yadav)

For computing education to become ubiquitous and sustainable in US K-12 schools, we need schools of Education to teach computing.
  • ​What should we be teaching to preservice teachers?
  • Where should we teach CS methods in preservice teacherdevelopment?
  • How do we help schools of Ed to hire and sustain faculty who focus on computing education?
Panelists will talk about how CS Ed is being integrated into their preservice teacher development programs, and about alternative models for addressing these questions.

Yesterday, our other computing education research Champion of Change, Andreas Stefik presented a summary of the empirical evidence on how to design programming languages to make them easier to learn.  Follow the link below to get to the two-page PDF pamphlet he produced for his presentation — it’s dense with information and fascinating.

This pamphlet is designed to provide an overview of recent evidence on human factors evidence in programming language design. In some cases, our intent is to dispel myths. In others, it is to provide the result of research lines.

from Programming Languages and Learning by Andreas Stefik

February 2, 2016 at 8:58 am 5 comments

On Imposter Syndrome: The week I made Forbes’ 30 Under 30 Science List

Sarah Guthals, a CS Education Researcher, was identified by Forbes Magazine as one of the “30 under 30” scientists to watch in years to come.  Congratulations to Sarah! She wrote an interesting blog post on imposter syndrome and the nomination.

I have suffered from imposter syndrome for at least a decade. I have worked hard, but it’s really hard for me to believe that I deserve what I have, or that the accomplishments that I’ve made are valid. I recognized my imposter syndrome when I was in my first year of grad school and since then I have been really trying to combat it — but I think instead I have just been ignoring it. Let’s see if I can explain it in the context of this weeks events.

When I found out I was nominated, I was very happy, but already feeling like a fraud. Am I really the one that should be nominated? What have I done to deserve it? I haven’t done anything alone (always had a team or partner).

Source: The week I made Forbes’ 30 Under 30 Science List — Medium

January 27, 2016 at 8:01 am Leave a comment

AP Computer Science Demographics Report for 2015 completed #CSEdWeek

Barbara Ericson, with the help of Phil Sands at Purdue, has now finished tabulating the demographic data for AP Computer Science for 2015 — see link here.  We don’t yet have the statistical tests that Kevin Karplus asked for (see post here), but Barbara did list the percentage of Hispanic exam takers with their proportion of the population.

hispanic-exam-takers

Our blog posts on AP CS have been picked up by Audrey Watters in her 2015 Top Ed-Tech Trends summary, in a decidedly negative light.

I’ll look at the whole “learn-to-code” push in an upcoming post, but I will note here: “Nationally, 37,327 students took the AP CS A exam in 2014,” Mark Guzdial observed. “This was a big increase (26.29%) from the 29,555 students who took it in 2013.” “Barbara Ericson’s 2015 AP CS demographics analysis: Still No African-Americans Taking the AP CS Exam in 9 States.” And Code.org teamed up with the College Board: because everyone needs to learn to code and then hand over money to the College Board for an AP test on the subject. Boom.

We don’t analyze AP CS A in order to market for the College Board.  We analyze AP CS A exam demographics because it’s the only operational definition we have found of the state of computing education across the United States.  From our work in “Georgia Computes!” we know that AP CS A tracks closely all other computing education in Georgia.  AP CS A is a dipstick to get a sense for who’s in the high school CS population.

 

December 11, 2015 at 7:10 am 2 comments

A Call for Corporate Action to Meet Labor Needs and Diversify Computing

Valerie Barr wrote a recent blog post about the state of the labor pool for STEM workers, especially in computing.  I particularly liked her point about the need to provide learning opportunities to bring women back who have left the tech industry.  Caroline Simard’s report on the needs of female mid-level tech managers (see blog post here) is what got me thinking about ebooks originally.  Caroline’s female mid-level tech managers needed to learn about new technologies, while still balancing a demanding job and more family responsibilities than their male counterparts.  That’s where I saw a need for something like our ebooks, to provide computing learning opportunities that fit into busy lives (see ebook post).  I see Valerie calling for something similar — we need more pathways to learn about computing for adults (see blog post here), and those pathways might help us to broaden participation in computing.

It is true that the industry changes quickly in some ways, with new tools, new approaches, and new languages. But there is a rich pool of potential employees who are being completely overlooked. The many women who have left tech positions could be brought back in and given training to bring them up to speed on the newest languages and development practices.  But this is a reasonable approach only if, at the same time, the tech industry makes a commitment to improving climate. There is no point in bringing back people who left tech if they are simply going to want to leave again in another 5 years. In fact, I imagine that bringing back a group of tech veterans who have greater maturity and experience could do wonders to improve climate in some of the tech companies.  But the companies have to commit. And they have to recognize that you can still be a cutting edge agile company even if the average age of your employees ticks up a bit.

Source: Thoughts on ‘The Frenzy About High-Tech Talent’ and a Call for Corporate Action | blog@CACM | Communications of the ACM

December 2, 2015 at 7:59 am Leave a comment

It’s not about “fixing women”! How Lucy Sanders tackles gender inequity: Data, research, humor

Lucy Sanders is one of my heroes, so I’m always happy to link to articles about her.  The point she’s making below is particularly interesting, and relates to previous posts about “grit” (see link here), and to the “lean in” phenomenon.

NCWIT isn’t just about getting women into tech jobs. It’s about getting women to share their perspective and knowledge. It’s about making sure women are not avoiding those leadership jobs or shirking from innovation because of something called unconscious bias.”There’s a big conversation going on now with what we call ‘fixing women.’ You hear things like ‘If women were just more confident.’ Or ‘If women were only better risk takers.’ We don’t subscribe to that. And we don’t subscribe to men being the biased, evil ones because research shows that all of us have this bias about who does technology,” Sanders said. “The ultimate goal, of course, is to make sure women and men are innovating equally in technology.”

Source: How Lucy Sanders tackles gender inequity: Data, research, humor – The Denver Post

November 23, 2015 at 8:46 am Leave a comment

A CS Education Research Class Syllabus

I’m teaching a graduate special-topics course on Computer Science Education Research this semester.  Several folks have asked me about what goes into a class like that.  Here’s the syllabus (from our “T-Square” Sakai site).  The references to “Guzdial” below are to my new book, Learner-Centered Design for Computing Education that I just turned in to Morgan & Claypool on Nov. 15. Should be available by the end of the year.

This class would look different if it was in Education, rather than in Computer Science.  For example, there might be less on tools.  The sessions where we consider how CS Ed Research appears at CHI and IDC may no longer be relevant.  Instead, I could imagine work contextualizing CS Education Research in mathematics education or science education.  I would expect to see sessions on equity, on teacher development, and on computing in schools.

 

CS8803: Computer Science Education Research

College of Computing Building Room 52, 9:35-10:55 T/Th

Teacher: Mark Guzdial, guzdial@cc.gatech.edu, TSRB 324/329

Office Hours:: By appointment

Course Overview: Introduction to computing education research (CER). History and influential early work. Learning goals for different populations, with particular attention to broadening participation in computing. Connections to research in learning sciences, educational psychology, science education. Design of research studies in CER, including Multi-Institutional Multi-National, laboratory, and classroom studies.

Textbook: We’ll be using readings from the ACM Digital Library (feely available on campus), and Guzdial’s new monograph Learner-Centered Design of Computing Education (draft available here in Resources, and eventually at the Morgan & Claypool site http://www.morganclaypool.com/toc/hci/1/1). We’ll use other readings that are available on the Web or via the Resources folder on T-Square.

Grades

  • 30%: Do 5 Reading Reflections. There are 6 opportunities for reading assignments. Students can skip one. Reading reflections are marked check or minus (something needs to be fixed). All reading reflections should be typed, with font >= 11 pt. No reading reflection should be longer than 3 pages typed and single spaced.
  • 15%: Class participation. Class time will be interactive, with little lecture. It’s a significant part of the learning in the class to participate. (The programming assignment is part of class participation.)
  • 10%: Research Study Re-Design. Redesign a research study from a published paper (referenced in Guzdial or published in ICER, SIGCSE, RESPECT, or ITICSE), to improve on the scope and findings. Due Oct 20.
  • 10% Where would you use this?. Try out any of Scratch, Alice, App Inventor, Snap, StarLogo, NetLogo, Blockly, or Pencil Code. Knowing what you know from class, would you recommend this environment? When? For whom? To learn what? Write a short (2-3 page) paper. Due Nov. 19.
  • 10%: Research Question White Paper. Write a short (3-4 pages) white paper defining a research question that’s worth exploring in CER. Explain why it’s an important, interesting, and answerable question. Identify the research community that you are speaking to with this research question. Think first section of an NSF proposal. Due Nov 12.
  • 25%: Research Study Design. Propose a study to explore the your unique research question. Think NSF proposal. Plan on 6-10 pages. 15% on paper due Nov 24. 10% on 10 minute presentation (5 minute Q&A) during last week of class.

Syllabus

Week 1

Aug 18: Introduction to class

  • Who are you and what is your experience with computing education?
  • Small Group Discussion: What do you want to know about computing education research? What do you think is unknown and worth exploring?

Aug 20: Computing for Everyone. Read Chapter 1 of Guzdial.

  • Come in with a quote that’s “interesting”
  • Pro/Con Debate: “We should teach computing to everyone.”

Week 2

Aug 25: Learning Sciences

Aug 27: The Challenges of Learning Programming. Read Chapter 2 of Guzdial.

  • Come in with a quote that’s “interesting”
  • Small group activity: What’s your hypothesis for why programming is hard? How would you test your hypothesis?
  • Reading Reflection: Using ideas and quotes from Chapter 1 and 2 of “How People Learn” to explain what’s hard about learning to program.

Week 3

Sep 1: Read Multi-institutional, multi-national studies in CSEd Research: some design considerations and trade-offs (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Compare and contrast: Randomized-control trials (see definition) vs. longitudinal studies (see definition) vs. MIMN studies.
    • What are each good for?
    • Why not use more RCT and longitudinal studies in computing education?

Sep 3: Read Computational Thinking and Using Programming to Learn in Guzdial

  • Generate a list: What are examples of computational thinking?
  • Small group activity: Have you ever used programming to help you learn something else? What are the characteristics of when programming helps and when it gets in the way?

Week 4

Sep 8: Read the first Chapter of Changing Minds at this link and Weintrop and Wilensky from ICER 2015 (ACM DL link)

  • Generate a list: What are characteristics of programming environments that support learning?
  • Small group activity: How do characteristics of programming for software development and for learning differ?
  • Reading Reflection: Identify some testable claims about Boxer in diSessa’s chapter. How would you test that claim?

Sep 10: Read Media Computation and Contextualized Computing Education in Guzdial

  • Come in with a quote that’s “interesting”
  • A mini-lecture with peer instruction and prediction using Media Computation.
  • Reading Reflection: When might contextualized computing help, and where might it not?

Week 5

Sep 15: Write a program to create something of interest or answer a question of interest before coming to class.

  1. Either download JES (from Github link) and create a picture or sound that you find interesting.
  2. Or Download Python (recommend using the Enthought install) and use the Computational Freakonomics website and course notes to answer a question of interest.
  3. Or use the CSPrinciples Ebook Data Chapters to answer a question about pollution in states.

Be prepared to show what you made or what you learned in class.

Come to class ready to answer two questions:

  • Did this motivate you to learn more about CS or the context?
    • Where did programming get in the way, and where did it help?

Sep 17: Read Adults as Computing Learners in Guzdial.

  • Come in with a quote that’s “interesting”
  • Small group activity: What’s similar and dissimilar between the teachers and the graphic designers? Identify another class of adults who might need to learn computing. Which group are they more like?

Week 6

Sep 22: Read The state of the art in end-user software engineering (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Build two lists: Features of a programming environment that support end-user programming and those that support learning about computing by end-user programmers.

Sep 24: Read Learner-Centered Computing Education for CS Majors by Guzdial

  • Come in with a quote that’s “interesting”
  • Small group activity: Come up with examples from your own experience of (a) CS education that you see as learner-centered and (b) CS education that was not learner-centered.
  • Reading Reflection: Contrast the adults in Chapter 5 and the non-majors in Chapter 6 with the CS majors in Chapter 7. What’s similar and what’s different about their learning and the support that they need?

Week 7

Sep 29: Read one of:

  • Spatial Skills Training in Introductory Computing (see ACM DL link)
  • Subgoals, Context, and Worked Examples in Learning Computing Problem Solving (see ACM DL link)
  • Boys’ Needlework: Understanding Gendered and Indigenous Perspectives on Computing and Crafting with Electronic Textiles (see ACM DL link)

Come to class ready (a) to summarize your paper and (b) to support/refute these three hypotheses:

  • We ought to add spatial skills training in all introductory CS courses.
  • We ought to use subgoal-labeled worked examples in all introductory CS courses.
  • We have to consider gender and cultural relevance in designing all introductory CS courses.
  • Reading Reflection: You are the Director of Georgia Tech’s Division of Computing Instruction. You may implement one change across all of your introductory courses, and you have very little budget. What will you change?

Oct 1: Read Towards Computing for All in Guzdial.

  • Come in with a quote that’s “interesting”
  • BIG list: What do we most need to know to advance computing for all? Where are the research gaps?
  • Everyone leave with a personal list of the top three research gaps that you find most interesting.
  • Reading Reflection: Pick any paper referenced in Guzdial that we did not read separately in this class. Read it and summarize it for me.

Week 8

Oct 6: Read Margulieux and Madden’s “Educational Research Primer” (in class Resources)

  • Small group activity: For your favorite research gaps, what research methods would you use to fill some of that gap?
  • Group activity list: What are the research methods that we need to learn more about?

Oct 8: RESEARCH METHODS: Based on the Oct 6 discussion, we’ll pick a paper or two to read here to inform our knowledge of research methods.

Newer Research

Week 9

Oct 13: No class! Fall Break.

Oct 15: RESEARCH METHODS: Based on the Oct 6 discussion, we’ll pick a paper or two to read here to inform our knowledge of research methods.

  • Discussion of Research Project: You don’t have to do it. You do have to design it.
    • First step: Define your question (due Nov 10), and make it answerable.
    • Second step: Tell us how you’d answer it.

Older Research

Week 10

Oct 20: Research Re-Design Due Here By 5 pm.

Oct 22: Read CE21 and IUSE proposals in Resources. (Note: They both weren’t funded in this form.)

  • Group Dissection:
    • What are the research questions?
    • What are the hypotheses?
    • What are the research methods?
  • Small group: Is this do-able? Would you give it a thumbs-up or a thumbs-down?

Week 11

Oct 27: What’s involved in reaching and studying populations at large-scale? Large scale: Read 37 Million Compilations: Investigating Novice Programming Mistakes in Large-Scale Student Data (ACM DL link) and Programming in the wild: trends in youth computational participation in the online scratch community (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Two lists: What can we know from looking at these kinds of data, and what can’t we know?

Oct 29: What’s involved in reaching and studying populations at small-scale? Small scale interviews/phenomenography: Read Graduating students’ designs: through a phenomenographic lens (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Small group discussion: What can we answer with a phenomengraphic approach that we can’t learn (easily) in other ways?

Week 12

Nov 3: What’s involved in reaching and studying populations in high school? In the High School: Read A Crafts-Oriented Approach to Computing in High School: Introducing Computational Concepts, Practices, and Perspectives with Electronic Textiles (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Storytime: Sharing stories about getting into K-12 schools.

Nov 5: CS Education Research in CHI. Read Learning on the job: characterizing the programming knowledge and learning strategies of web designers (ACM DL link) and Programming in the pond: a tabletop computer programming exhibit (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Group list: What makes a CHI paper different from an ICER paper?

Week 13

Nov 10: CS Education Research in IDC. Read Strawbies: explorations in tangible programming (ACM DL link) and “Let’s dive into it!”: Learning electricity with multiple representations (ACM DL link)

  • Come in with a quote that’s “interesting”
  • Group list: What makes an IDC paper different?

Nov 12: Research White Paper Due Here

CS Ed Research at Georgia Tech. Read one of Betsy DiSalvo’s papers — your choice.

  • Come in with a quote that’s “interesting”
  • Small group: Contrast Betsy’s research questions and methods with those of Mark’s and his students.

Week 14

Nov 17: CS Ed Research at Georgia Tech. Read Engaging underrepresented groups in high school introductory computing through computational remixing with EarSketch (ACM DL link) and EarSketch: A Web-based Environment for Teaching Introductory Computer Science Through Music Remixing (ACM DL link)

  • Group list:
    • What are the research questions for EarSketch?
    • What are the research hypotheses?
    • What are the research methods?

Nov 19: Try it out! Hand in your Where would you use this? papers before class. Come to class prepared to demo the environment you picked.

  • Debate: For a set of audiences and learning goals that we define in class, argue for your environment to meet that need.

Week 15

Nov 24: Research Design Paper Due Here.

Nov 26: No Class! Eat Turkey.

Week 16

Dec 1: Present Research Designs

Dec 3: Present Research Designs

November 18, 2015 at 8:22 am 2 comments

You Don’t Have to Be Good at Math to Learn to Code – The Atlantic

It’s an interesting and open question.  Nathan Ensmenger suggests that we have no evidence that computer scientists need a lot of mathematics (math background has been correlated with success in CS classes, not in success in a CS career), but the emphasis on mathematics helped computing a male field (see discussion here).  Mathematics has both been found to correlate with success in CS classes, and not correlate with success in object-oriented programming (excellent discussion of these pre-requisite skill studies in Michael Caspersen’s dissertation).  It may be true that you don’t have to be good at mathematics to learn to code, but you may have to be good at mathematics to succeed in CS classes and to get along with others in a CS culture who assume a strong math background.

People who program video games probably need more math than the average web designer. But if you just want to code some stuff that appears on the Internet, you got all the math you’ll need when you completed the final level of Math Blaster. (Here’s a good overview of the math skills required for entry-level coding. The hardest thing appears to be the Pythagorean theorem.)

Source: You Don’t Have to Be Good at Math to Learn to Code – The Atlantic

November 16, 2015 at 8:10 am 16 comments

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