Posts tagged ‘computing education research’

A high-level report on the state of computing education policy in US states: Access vs Participation

states-policyInteresting analysis from Code.org on the development of policies in US states that promote computing education — see report here, and linked below.  The map above is fascinating in that it shows how much computing education has become an issue in all but five states.

The graph below is the one I found confusing.

urm-access

I’ve been corrected: the first bar says that where the school’s population is 0-25% from under-represented minority groups, 41% of those schools teach CS.  Only 27% of mostly-minority schools (75%-100% URM, in the rightmost column) offer CS.  This is a measure of which schools offer computer science.

The graph above doesn’t mean that there are any under-represented minority students in any CS classes in any of those high schools.  My children’s public high school in Georgia was over 50% URM, but the AP CS class was 90% white and Asian kids.  From the data we’ve seen in Georgia (for example, see this blog post), few high schools offer more than one CS class. Even in a 75% URM high school, it’s pretty easy to find 30 white and Asian guys.  Of course, we know that there are increasing numbers of women and under-represented minority students in computer science classes, but that’s a completely different statistic from what schools offer CS.

I suspect that the actual participation of URM students in CS is markedly lower than the proportion in the school.  In other words, in a high school with 25% URM, I’ll bet that the students in the CS classes are less than 25% URM.  Even in a 75% URM high school, I’ll bet that CS participation is less than 75% URM.

Access ≠ participation.

Source: The United States for Computer Science – Code.org – Medium

October 12, 2018 at 7:00 am 5 comments

Closing the gaps is the real challenge in computing education (CIRCL Meet Mark Guzdial)

Meet_Mark_Guzdial_–_CIRCLThe Center for Innovative Research in CyberLearning (CIRCL) did a Perspectives interview with me (thanks, Quinn Burke!) that appears here.

I got to talk about the range of things I’ve done, what I’ve been surprised by and not surprised by, and what I think the big challenges to come in K-12 CS education.

In hindsight, it’s not a surprise that we’re having trouble closing the gaps.  There are increasingly more teachers who can teach CS, and there are governors and the Tech industry pushing for more CS Ed.  But in between, there are principals that don’t buy it, and the classes in the schools are few and tiny.  Most Schools of Education are still not players in promoting CS education. I predict over 85% of kids in Georgia (at least) are not getting a single experience with CS.  The percentage of schools having CS is getting higher, but real experience with CS is low.

As you might imagine, I focus on the need for more research and for reducing inequities. We have made a lot of progress on computing education, and we can make more progress still.


N.B. as Shriram points out in the comments, our claim for FCS1 about “language independent” is really about “multi-lingual.” I’ve asked CIRCL to update the piece, and I’ll try to be more careful about what I claim for FCS1 and SCS1.

 

October 1, 2018 at 8:00 am 11 comments

Applying diSessa’s Knowledge in Pieces Framework to Understanding the Notional Machine

In Lauren Margulieux’s blog where she summarizes papers from learning sciences and educational psychology, she takes on Andy diSessa’s 1993 paper “Toward an epistemology of physics” where diSessa applies his “knowledge in pieces” framework to how students develop an understanding of physics.  (See blog post here.)

The idea is that humans assemble their understanding of complex phenomenon out of knowledge of physical experiences, p-prims. Quoting Lauren:

Elements: P-prims are knowledge structures that are minimal abstractions of common phenomena and typically involve only a few simple parts, e.g., an observed phenomenon, like a person hitting a pen and that pen rolling across the table, and an explanation, like when people hit things, they move. P-prims are both phenomenological, meaning that they are interpretations of reality, and primitive, meaning that are (1) based on often rudimentary self-explanations and (2) an atomic-level mental structure that is only separated into parts by excessive force.

Cognitive Mechanism: P-prims are only activated when the learner recognizes similarities between a p-prim and the current phenomena. Recognition is impacted by many different features, such as cuing, frequency of activation, suppression, salience, and reinforcement. Because activation of p-prims depends on contextual features of phenomena, novices often fail to recognize relevant p-prims unless the contextual features align.

I find diSessa’s framework fascinating, and I’ve always wondered how we could apply it to students learning the notional machine (see blog post here on notional machine). My guess is that students use p-prims to develop their mental model of how the computer works, because — what else could they use? In the end, isn’t all our understanding grounded in physical experiences?  But using p-prims will likely lead to misconceptions since the notional machine is not based in the physical world.

Maybe this is a source of common misconceptions in learning computing.  The list of misconceptions that students have about variables, loops, scope, conditionals, and data structures is long and surprisingly consistent — across languages, over time.  What could possibly be the common source of all those misconceptions?  Maybe it’s physical reality.  Maybe students generally apply the same p-prims when trying to understand computing, and that’s why the same misconceptions arise. It’s sort of like using a metaphor to understand something in computing, but then realizing that the metaphor itself is leading to misconceptions.  And the metaphor that’s getting in our way is the use of physical world primitives for understanding the computational world.

Colleen Lewis, as a student of diSessa’s, uses the Knowledge in Pieces framework in her work.  In her terrific ICER 2012 paper, she does a detailed analysis of students’ debugging to identify misconceptions that they have about state. State is an interesting concept to study from a KiP perspective. It’s a common issue in CS, but less common in Physics. It’s not clear to me how students connect computational state to state in the real world.  Is it state like water being frozen or liquid, or state like being painted blue?  Do they get that state is malleable?

This is a rich space to explore in computing education. What are the p-prims for understanding the notional machine? How do students use the physical world to understand the computational one?

Read more of Lauren’s post here: Article Summary: diSessa (1993) Knowledge in Pieces Framework

September 14, 2018 at 7:00 am 4 comments

Growth mindset matters for individual human performance, with a more indirect connection to academic success

One of the most talked-about papers at ICER 2018 was this one, “Fixed versus Growth Mindset Does not Seem to Matter Much: A Prospective Observational Study in Two Late Bachelor level Computer Science Courses.” The claim was that fixed and growth mindset did not have much impact on student course performance.  One of the authors wrote a blog post summarizing the paper.
In my opinion, they got growth/fixed mindset theory wrong.  The mistake is in the first line of the abstract, “Psychology predicts that a student’s mindset—their implicit theory of intelligence—has an effect on their academic performance.”  Growth and fixed mindset have an effect on individual student development. There is an indirect effect on academic performance which is more complex. Grades are not the same as measuring learning. Grades are typically a measure of mastery of concepts.
The presentation of the paper had this amazing graph (picture I took below).  Most students fail in the courses they studied.  Look at the big peaks in the distribution on the left. Those are all the fails.
IMG_0863
In Freakonomics, there’s a chapter on why, if drug dealers make so much money, why do so many of them live with their mothers?  (The chapter is reprinted here.) The answer is that drug dealing (like professional sports or acting) is a “lottery” — many people try and make no money, and very few people get to the top and make lots of money.  All those high school and college football players who are waking up early to pump weights have a growth mindset — they believe that their effort will take them to the NFL.  However, the vast majority are *wrong*. They won’t make it.  There is no apparent connection between growth mindset and success.
That’s how I saw the ICER paper on fixed and growth mindset.  If the outcome variable is academic success, growth mindset isn’t going to always pay-off. Sometimes the deck is stacked against you, and even if you think you can win, you won’t.
However, if the outcome variable is individual development, growth mindset will likely beat fixed mindset.  If you believe you can get better, you might. If you don’t believe you can get better, you won’t. A good outcome variable would be learning gain, measured pre-test to post-test.  In this study, most students had a growth mindset, so they probably wouldn’t have seen much variation (between growth and fixed) even if they measured learning.
The students thought if they worked harder, they could do better. And they probably did all do better (from a learning perspective). They just weren’t going to win in this lottery.
It’s a different question whether a given intervention to improve mindset might lead to improved academic performance.  If you improve learning, and academic performance is reflective of learning, then there should be a connection IF it’s possible to change mindset with an intervention. Duckworth and Dweck have shown that they have successfully intervened to change students’ mindset and consequently improve academic performance, and that work was recently replicated (see post here).  The efforts to intervene on mindset in CS have had mixed success (see my blog post here on that). But it’s one thing to say that fixed vs growth mindset does not seem to matter much (the title of the paper presented at ICER), and another to say that a given mindset intervention did not result in academic performance increase. The first claim is about theory, and the second is about designing interventions with a multi-step causal chain. I don’t buy the former claim, but completely agree that the latter is a complex and interesting issue to explore.

September 7, 2018 at 7:00 am 5 comments

Join us at the University of Michigan to study Computing Education

As of September 1, Barbara Ericson and I are new faculty at the University of Michigan.  The School of Information has a nice write-up about their new faculty, including Barb here. The Computer Science and Engineering Division (of the Electrical Engineering and Computer Science Department) wrote up their new faculty, including me here.

We are both looking to bring on new students in Ann Arbor.  Readers of this blog can find out a lot about us.  Barb continues to be interested in further developing interactive ebooks as a medium for education and in issues of broadening participation in computing (especially looking to grow Project Rise Up and Sisters Rise Up).  I continue to be interested in how students come to understand program execution (building a mental model of the notional machine) and in the role of programs to be a notation for learning (like in Bootstrap).

Because of how we’re situated at the University of Michigan, there are several avenues for new PhD students:

The EER program is hosting a prospective student open house on Oct 22, and there are travel grants available. See https://eer.engin.umich.edu/ for more information, and I have part of the flyer pasted below.

Choosing between the degree options depends on what you want to do with your PhD after you graduate, and what kind of preparation you want during your PhD. You can do CS Ed research via the CS PhD at Michigan (I did), and your preparation will involve classes in CS and a CS qualifying examination. SI is more oriented towards psychological and sociological perspectives on computing. EER will be more about CS and education in an Engineering context. If you want a CS faculty job, the CS PhD is the surest bet, but SI PhDs do get hired in CS departments, and we hope EER PhDs do, too. EER PhD opens up possibilities in Engineering Education departments (like at Purdue, Virginia Tech, Ohio State, and Clemson), where a CS or SI Phd is less common.

Michigan is becoming a seriously interesting place for computing education research.  Elliot Soloway (my PhD advisor) is still an energetic force in CS. SI just hired Ron Eglash (starting the same time as us), who is one of the founders of ethnocomputing and ethnomathematics (see news article here). I’m eager to collaborate with my friends in the learning sciences here, like Betsy Davis, Barry Fishman, and Chris Quintana. Do come join us!

2018_EER_Open_House_Flyer_Final_pdf__1_page_

September 3, 2018 at 7:00 am 2 comments

In last five years, little progress in increasing the fraction of American CS BS degree recipients who are African Americans

Keith Bowman published a series of blog posts this summer on African American undergraduate degrees in engineering.  In July, he wrote one on computer science – linked here. It’s interesting, careful, and depressing. I’m quoting the conclusion below, but I highly recommend clicking on the link and seeing the whole report. What’s most interesting is the greater context — Bowman is comparing across different engineering programs, so he has a strong and data-driven sense of what’s average and what’s below average.

There has been little progress in increasing the fraction of American CS BS degree recipients who are African Americans. Progress will likely only take place through a concerted effort by industry, professional societies, academia and government to foster change, including stronger efforts in graduate degrees. CS undergraduate programs fare poorly compared to many other engineering disciplines in the context of gender diversity and slightly worse than engineering overall in the fraction of African Americans earning undergraduate degrees. Many of the largest CS programs in the US are strikingly behind the national averages for CS BS degrees earned by African Americans.

 

August 24, 2018 at 7:00 am 4 comments

How computing education researchers and learning scientists might better collaborate

Lauren Margulieux has started a blog which is pretty terrific.  I wrote about Lauren’s doctoral studies here, and I last blogged about her work (a paper comparing learning in programming, statistics, and chemistry) here.

In her blog, Lauren is explaining in lay terms papers from learning sciences, educational psychology, and educational technology.  She’s an interdisciplinary researcher, and she’s blogging to help others connect across disciplines.

Her most recent blog post is about an issue I’ve been thinking about a lot lately. I wrote a blog post in the summer about the challenge of bridging the modes of science and truth-seeking in (computing) education vs. computer science. Lauren summarizes a paper by Peffer and Renken about concrete strategies to be used between discipline-based education researchers (like math education researchers, science education researchers, or computing education researchers) and learning scientists. Quoting part of it below:

Challenges in Interdisciplinary Research: Collaboration within a field can be difficult as people attempt to reconcile different ideas towards one goal. Collaboration between fields, each with its own traditions in theory and methodology, can seem like a minefield. Below are some common challenges that DBERers and learning scientists face.

  1. Differences in hard and soft sciences – researchers in the hard sciences can often feel frustrated by the lack of predictability in human-subjects research, and researchers in social sciences can become frustrated when those in the hard sciences have unrealistic expectations or view research in the soft sciences as non-scientific.

  2. Differences in theories and frameworks – What constitutes a theory or framework can be different in different domains, confusing what is often a fundamental building block of research.

  3. Differences in research methodologies – those unfamiliar with human-subjects research can find its methodologies complex, varied, and full of uncertainty, and those who have endured countless hours of training in these methodologies can find it difficult to describe or justify methodological decisions in a concise way.

See more at https://laurenmarg.com/2018/07/29/peffer-renken-2016-dber-and-learning-sciences-collaboration-strategies/

August 12, 2018 at 11:00 pm 1 comment

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