Posts tagged ‘BPC’

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,, 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 We’ll use other readings that are available on the Web or via the Resources folder on T-Square.


  • 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.


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 1 comment

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

Barbara Ericson’s 2015 AP CS demographics analysis: Still No African-Americans Taking the AP CS Exam in 9 States


Normally, this is the time of the year when Barb writes her guest post about the AP CS exam-taker demographics.  She did the analysis, and you can get the overview at this web page and the demographics details at this web page.

But before we got a chance to put together a blog post, Liana Heitin of EdWeek called her for an interview.  They did a nice job summarizing the results (including interactive graphs) at the article linked below.

Some of the more interesting points (from Liana’s article):

No girls took the exam in Mississippi, Montana, or Wyoming. (Though Montana had no test-takers at all, male included, this year. Wyoming, which previously had no students take the test, had three boys take the exam in 2015).

Hawaii had the largest percentage of female test-takers, with 33 percent.

The overall female pass rate went up 3 percentage points, to 61 percent, from the year before.

Twenty-four girls took the test in Iowa, and 100 percent of them passed.”You don’t usually see 100 percent passing with numbers that big,” said Ericson. “Maybe five out of five pass. But 24 out of 24 is pretty cool.”

No African-American students took the exam in nine states: Idaho, Mississippi, Montana, New Hampshire, New Mexico, North Dakota, South Dakota, Utah, and Wyoming. That’s better than last year, though, when 13 states had no African-American test-takers.

Notably, Mississippi has the highest population of African-Americans—about half of the state’s high school graduates last year were black, according to the Western Interstate Commission for Higher Education. Yet of the five AP computer science test-takers, all were white or Asian and male.

Source: Still No African-Americans Taking the AP Computer Science Exam in Nine States – Curriculum Matters – Education Week

November 9, 2015 at 7:28 am 6 comments

Requirements for a Computing-Literate Society: VL/HCC 2105 Keynote

I gave a keynote talk at VL/HCC 2015 (see the program here) on Tuesday morning.  Here is the abstract, the short form outline, and a link to the slides on

Abstract: We share a vision of a society that is able to express problems and ideas computationally. Andrea diSessa called that computational literacy, and he invented the Boxer Programming Environment to explore the media of computational literacy. Education has the job of making citizens literate. Education systems around the world are exploring the question of what should all citizens know about computing and how do we provide that knowledge. The questions being asked are about public policy, but also about what does it mean to be expressive with computation and what should computing users know. The answers to these questions have implications for the future of human-centric computing.


I. Our Job: The first computer scientists set the goal to achieve a Computing-Literate Society.

II. Challenges to Achieving a Computing-Literate Society
Access and Diversity
Inverse Lake Wobegon Effect
Unanswered research questions of policymakers

III. Inventing New Kinds of Computing Education
Story #1: Contextualized Computing Education.
Story #2: Understanding the Needs of High School CS Teachers.


October 21, 2015 at 8:13 am 2 comments

What can I do today to create a more inclusive community in CS? Guest Post from Cynthia Lee

In July, Cynthia Lee, Leo Porter, Beth Simon, and I held a workshop (funded by the NSF IUSE program) for new faculty at research-intensive universities, to help them to be more effective and efficient teachers. We had eight new faculty attend. We taught them about peer instruction, worked examples, how to create a syllabus, techniques for dealing with plagiarism, how to make time for teaching, and how to create a more inclusive classroom. The response was terrific. As one participant told us, “I can’t believe how much actionable knowledge I picked up about teaching in just a day and a half!”

We’ll be inviting new faculty from research-intensive universities again in Summer 2016.

The below list was created by Cynthia Lee for the workshop participants. I loved it and asked if I could offer it here as a guest post. I’m grateful that she agreed.


  • Email top performers on a recent homework or exam to congratulate them; be sure to include a diverse group.
  • Personally invite a woman or minority student who is doing well to major in CS, apply to an internship, or go to grad school. If your TAs work with small groups of students in a discussion section, have them do this as well.1
  • Review today’s lecture slides to make sure that your gender pronouns are varied, and not in ways that conform to stereotype.
  • Avoid heteronormative examples (e.g., bijective function between sets “boys” and “girls”).
  • When using arbitrary names in examples, choose a broader selection (Juan, Neha, Maria, Mohammed, instead of just Jane Doe and John Smith). To represent your school’s population, use a previous quarter roster for ideas.
  • At the beginning of the quarter, ask each student to email you to introduce themselves by naming one of their core values, and one way that CS relates to or could be used in service of that core value (or write it down in class, and/or share with a neighbor in class).2
  • Never say, “This UI is so easy your mom could use it” or “How would you explain this to your mom?” or other phrases that equate women with lack of tech savvy. 3
  • Review today’s lecture slides to make sure that stock photos and illustrations with people in them include diverse races and genders in non-stereotyped roles.
  • Believe that hard work and effective practice matters more than DNA. Your beliefs influence students’ beliefs and impact their performance. 4
  • Take a moment in class today to encourage students to focus on their “slope,” not their “y-intercept.” That is, in the long run it matters how fast you’re growing and learning, not advantages or deficiencies in where you started. 5
  • Start class today by telling the students you’re proud of them and how hard they are working. Tell them you are enjoying working with them this quarter.
  • Start class today by renewing your encouragement to students to come to office hours. Explicitly instruct them how to do it: “you don’t need to have a particular question-you’re welcome to just stop by for 5 minutes to introduce yourself” and “I’m not just here for homework questions-if you are considering changing your major to CS and want to talk about it, if you want to know what it’s like to work as a software engineer, if you are thinking about applying to grad school but don’t know where to begin, I’m happy to discuss that kind of thing as well.”
  • Have very clear written expectations for student work (coding style, project components, etc.). Where possible, show sample solutions exactly as you would want a student to write them (don’t just give a “sketch” of the solution).
  • Allow and encourage pair programming on assignments. 6
  • Provide students with clear and timely feedback, including class-wide distribution data. Women and minority students often fear the worst about their position relative to the class and can be reassured by data. 7
  • After a midterm exam, step through the math showing that they can still pass the course even if they did poorly. It’s just some multiplication, but take the time to talk about it. Be factual-no need to “sugar coat”-but provide facts that will help students who think things are worse than they really are.
  • When a student is speaking, wait for the student to finish then count “one one-thousand, two one-thousand” in your mind before responding. Both men and women are prone to prematurely cutting off women when they speak. You may do this unconsciously unless you consciously add that pause. 8
  • Occasionally choose a lecture to actually write a tally of how many times you’ve called on men vs women in the class. Both men and women are prone to calling on men more often. You may do this unconsciously unless you consciously do otherwise. 9
  • Actively mitigate when students may be intimidating each other. When a student uses jargon in a question (often one of those questions that is more of a boast than a real question), explicitly identify when you expect that most students will not be familiar with that jargon, and/or it is not something other students are expected to know for the class (“Thanks for your comment. For the rest of the class, I’m sure most of you aren’t familiar with some of those terms-don’t worry, you’re not alone. Those terms are outside the scope of this class and not necessary to know.”)
  • Ensure that you and your TAs call each student by their preferred name and gender pronoun-including allowing students to write their preferred name on homework and exams-even if these do not match their current legal and/or registrar records of name and sex. This issue deeply affects transgender students, and also many students who prefer to have an alternate anglicized name. Some institutions are good about allowing students to easily make these changes with the registrar so the preference will automatically show up on your roster. Find out about your school’s policies. You could also put a statement in your syllabus that you welcome students to email you about their preference.
  • Watch out for examples or anecdotes about your childhood or daily life that may cause students to feel excluded for economic reasons (e.g., talking about pricey gadgets or vacations in Hawaii as normal). Even if you know that you did not experience these things and are simply using them as an example, students don’t know that and can mistakenly assume you are referring to them in a normative way.
  • Mid-quarter, reach out to a student who has filed a disability accommodation form with you and ask them if their needs are being met in your class. Reaffirm your commitment to complying with their approved accommodations and your willingness to receive complaints if there is a problem.
  • Encourage your colleagues to do the items on this list. Advertise your good example by bringing up your performance of these items in conversations with other faculty.


  1. Holly Lord and Joanne McGrath Cohoon. “Recruiting and Retaining Women Graduate Students in Computer Science and Engineering,” 2006. ↩︎
  2. Research shows this intervention mitigates stereotype threat. Reduced racial gap by 30%. ↩︎
  3. This sexist trope is something women have been working to expunge from our vocabulary. Unfortunately, still often seen in discussion of UI design.,_your_mother_could_do_it ↩︎
  4. Carol Dweck. “The New Psychology of Success.” This research shows that minority students perform worse in classes where the professor believes in a “fixed mindset” (talent is innate) when compared to performance in classes where professor has a “growth mindset” (talent can be developed through effort). See also CS-specific work on mindsets: Laurie Murphy and Lynda Thomas. “Dangers of a fixed mindset: implications of self-theories research for computer science education.” ITiCSE 2008. ↩︎
  5. Articulating this idea as slope/y-intercept is from Professor John Ousterhout of Stanford. ↩︎
  6. Among other research showing benefits of pair programming: Leo Porter and Beth Simon. “Retaining nearly one-third more majors with a trio of instructional best practices in CS1,” SIGCSE ’13. ↩︎
  7. These fears are related to “Imposter Syndrome”-even highly talented students from under-represented groups fear that they are unskilled, and more unskilled than everyone else. Overview of Imposter Syndrome research: ↩︎
  8. Occasioned by a news item about a panel discussion in Silicon Valley, NYTimes reviews research on women being interrupted when speaking: ↩︎
  9. Jere Brophy and Thomas Good. “Teachers’ communication of differential expectations for children’s classroom performance,” 1970. ↩︎

September 28, 2015 at 8:50 am 4 comments

Different is not Lite: A 2002 Argument Against Media Computation


I recently moved offices. In the process of packing and pitching, I found the above editorial from the Georgia Tech student newspaper.  Dated September 2002, it urged the faculty in the Liberal Arts, Architecture, and Management Colleges to reject the newfangled Media Computation class that was being proposed.

I had heard the argument being made in the editorial before, and continue to hear it today.  The argument is that we do our students a disservice if we don’t give them “real” computer science.  The editor cited above is arguing that all students at Georgia Tech deserve the same high-quality computer science education.  If we don’t give them the “real” thing, if liberal arts and management majors aren’t getting the same thing as CS majors, they are only getting “CS lite.”

That phrase “CS lite” gets applied to our BS in Computational Media regularly. (See the blog post where I talk about that.)  Which is funny, because all but one of the CS classes that CM majors take are the same ones that CS majors take.  Georgia Tech CS majors take many more credit hours than other majors (including CS majors at other institutions), and the CM major has enough CS courses to be ABET accredited as a computing program.  So, what’s “lite” about that?  Are other schools’ BS in CS programs “Georgia Tech CS lite” because they have fewer credit hours in CS?

Media Computation wasn’t lite. It was different.  MediaComp didn’t cover everything that the intro course for CS majors did.  But the course for CS majors didn’t cover everything that MediaComp did.  In fact, after a few years, the CS instructors complained that our CS majors didn’t know about RGB and how to implement photo effects (like how to negate an image, or how to generate grayscale from a color picture) — which non-CS majors did know!  Content on media got added to the CS majors classes.

Computational Media isn’t CS lite.  It’s CS different.  The one course that’s different between CS and CM is the required course on computer organization.  CS majors take a course based on Patt and Patel’s book.  CM majors take a course where they program a Nintendo Gameboy.  The courses are not exactly the same, but have a significant overlap.  We did a study of the two courses a few years ago and published a journal paper on it (see link here, and article is on my papers page). There was no significant difference in student learning between the two courses.  But the CM majors liked their course much more.  Now, there are projects on programming the Gameboy in the CS majors classes, too.

Different is good.  Different is where you invent new things.  Some of those new curricular ideas helped CS courses.  Some of those different ideas stayed in the CM and MediaComp courses. Those courses serve different populations and different needs. Not all of it was appropriate or useful for CS majors.

Just because there is difference doesn’t mean that it’s lite.  Do we call mechanical engineering “physics lite”?  Or chemical engineering “chemistry lite”?  I’m sure that there are people who do, but that’s disparaging to the difference and diminishes the value of exploring different combinations of subject areas.  Valuing different combinations with computing is a particularly important idea for computer science, because interdisciplinary computing degrees are the only ones where the percentage of women majors are growing (see RESPECT report here).  We should value interdisciplinary courses and programs because it’s good for our students and for diversity.  We should not disparage the CS + X perspectives as “CS lite.”

September 23, 2015 at 8:22 am 1 comment

Older Posts

Recent Posts

November 2015
« Oct    


Blog Stats

  • 1,154,027 hits

Enter your email address to follow this blog and receive notifications of new posts by email.

Join 3,667 other followers

CS Teaching Tips


Get every new post delivered to your Inbox.

Join 3,667 other followers