Archive for August, 2018

US National Science Foundation increases emphasis on broadening participation in computing

The computing directorate at the US National Science Foundation (CISE) has increased its emphasis on broadening participation in computing (BPC).  (See quote below and FAQ here.) They had a pilot program where large research grants were required to include a plan to increase the participation of groups or populations underrepresented or under-served in computing. They are now expanding the program to include medium and large scale grants. The idea is to get more computing researchers nationwide focusing on BPC goals.

CISE recognizes that BPC requires an array of long-term, sustained efforts, and will require the participation of the entire community. Efforts to broaden participation must be action-oriented and must take advantage of multiple approaches to eliminate or overcome barriers. BPC depends on many factors, and involves changing culture throughout academia—within departments, classrooms, and research groups. This change begins with enhanced awareness of barriers to participation as well as remedies throughout the CISE community, including among principal investigators (PIs), students, and reviewers. BPC may therefore involve a wide range of activities, examples of which include participating in professional development opportunities aimed at providing more inclusive environments, joining various existing and future collective impact programs to helping develop and implement departmental BPC plans that build awareness, inclusion, and engagement, and conducting outreach to underrepresented groups at all levels (K-12, undergraduate, graduate, and postgraduate).

In 2017, CISE commenced a pilot effort to increase the community’s involvement in BPC, by requiring BPC plans to be included in proposals for certain large awards [notably proposals to the Expeditions in Computing program, plus Frontier proposals to the Cyber-Physical Systems and Secure and Trustworthy Cyberspace (SaTC) programs]. By expanding the pilot to require that Medium and Large projects in certain CISE programs [the core programs of the CISE Divisions of Computing and Communication Foundations (CCF), Computer and Network Systems (CNS), and Information and Intelligent Systems (IIS), plus the SaTC program] have approved plans in place at award time in 2019, CISE hopes to accomplish several things:

  • Continue to signal the importance of and commitment to BPC;
  • Stimulate the CISE community to take action; and
  • Educate the CISE community about the many ways in which members of the community can contribute to BPC.

The long-term goal of this pilot is for all segments of the population to have clear paths and opportunities to contribute to computing and closely related disciplines.

Read more at https://www.nsf.gov/pubs/2018/nsf18101/nsf18101.jsp

August 31, 2018 at 7:00 am 1 comment

A Guide to Teaching Computing to Adults in Informal Settings

Greg Wilson is a pioneer and a visionary. He saw an education problem and invented an organization to address it. The problem he’s addressing is that scientists, engineers, and other professionals (what he called “free-range learners”) are discovering that they need computing, but aren’t going to be taking formal classes on-campus. Greg co-founded “Software Carpentry” to offer workshops to adults that addresses their needs. I care about this problem, too, and am amazed at and impressed with how much Greg has grown Software Carpentry.

He has recently published an online text on how to teach technology in these settings. You can find it here. It’s more than just a how-to. Greg recognizes the value of drawing on the research on education, and computing education specifically. Greg explains why he makes these recommendations with lots of references to research literature, including some of my favorite work that I mention regularly here.

I want to make clear that it’s not a general guide for computing educators. There’s little here for K-12 teachers — this is about teaching adults. Few of the kinds of things that we teach in our New Faculty Workshops about active learning in the classroom are here. Still, there’s a lot here that CS faculty will find valuable and will learn from.

Teaching Tech Together

Hundreds of grassroots groups have sprung up around the world to teach programming, web design, robotics, and other skills to free-range learners outside traditional classrooms. These groups exist so that people don’t have to learn these things on their own, but ironically, their founders and instructors are often teaching themselves how to teach.

There’s a better way. Just as knowing a few basic facts about germs and nutrition can help you stay healthy, knowing a few things about psychology, instructional design, inclusivity, and community organization can help you be a more effective teacher. This book presents evidence-based practices you can use right now, explains why we believe they are true, and points you at other resources that will help you go further. Its four sections cover:

• how people learn;

• how to design lessons that work;

• how to deliver those lessons; and

• how to grow a community of practice around teaching.

Find more at: http://teachtogether.tech/en/partner/

August 27, 2018 at 7:00 am Leave a comment

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

High school students learning programming do better with block-based languages, and the impact is greatest for female and minority students

I learned about this study months ago, and I was so glad to see it published in ICLS 2018 this last summer.  The paper is “Blocks or Text? How Programming Language Modality Makes a Difference in Assessing Underrepresented Populations” by David Weintrop, Heather Killen, and Baker Franke.  Here’s the abstract:

Broadening participation in computing is a major goal in contemporary computer science education. The emergence of visual, block-based programming environments such as Scratch and Alice have created a new pathway into computing, bringing creativity and playfulness into introductory computing contexts. Building on these successes, national curricular efforts in the United States are starting to incorporate block-based programming into instructional materials alongside, or in place of, conventional text-based programming. To understand if this decision is helping learners from historically underrepresented populations succeed in computing classes, this paper presents an analysis of over 5,000 students answering questions presented in both block-based and text-based modalities. A comparative analysis shows that while all students perform better when questions are presented in the block-based form, female students and students from historically underrepresented minorities saw the largest improvements. This finding suggests the choice of representation can positively affect groups historically marginalized in computing.

Here’s the key idea as I see it. They studied over 5,000 high school students learning programming. They compared students use block-based and text-based programming questions.  Everyone does better with blocks, but the difference is largest for female students and those from under-represented groups.

Here’s the key graph from the paper:

Weintrop-blocks-text-icls18a-sub1402-i7_pdf__page_5_of_8_

So, why wouldn’t we start teaching programming with blocks?  There is an issue that students might think that it’s a “toy” and not authentic — Betsy DiSalvo saw that with her Glitch students. But a study with 5K students suggests that the advantages of blocks swamp the issues of inauthenticity.

The International Conference on the Learning Sciences (ICLS) 2018 Proceedings are available here.

August 20, 2018 at 7:00 am 10 comments

CRA Memo on Best Practices for Engaging Teaching Faculty in Research Computing Departments

I’m excited to see this memo from the Computing Research Association on the status of teaching faculty in computing departments. Computing departments are increasingly relying on teaching faculty, and it’s important to give them fair and equitable treatment.

I wrote in 2016 that “CS Teaching Faculty are like Tenant Farmers.” This memo addresses some of the issues I raised, though some are buried in the text of the memo.  I argued that teaching faculty should be involved in hiring for both traditional and teaching faculty, and that teaching faculty should serve in upper-level leadership positions.  The report does state halfway down the report, “Similarly, teaching faculty should be broadly included in faculty governance on matters related to their roles in the department, including participation in faculty meetings, voting rights on matters impacting the education mission, inclusion in evaluation of the teaching performance of other faculty, and input on hiring decisions.”  This memo is a step in the right direction.

To achieve their educational mission, computing departments at research universities increasingly depend on full-time teaching faculty who choose teaching as a long-term career. This memo discusses the need for teaching faculty, explores the impact of teaching faculty, and recommends best practices.

Essential best practices for departments include:

  • Departments should provide teaching faculty with equitable rights and resources, except in limited areas where differing job responsibilities make that inappropriate.

  • Departments should encourage teaching faculty to be equal and active partners on projects and committees with the goal of contributing to the department’s educational mission.

  • Departments should set course, preparation, student, and service loads of teaching faculty at a level that allows for innovation and quality instruction.

    ….

Source: Laying a Foundation: Best Practices for Engaging Teaching Faculty in Research Computing Departments

August 17, 2018 at 7:00 am 6 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

Adaptive Parsons problems, and the role of SES and Gesture in learning computing: ICER 2018 Preview

 

Next week is the 2018 International Computing Education Research Conference in Espoo, Finland. The proceedings are (as of this writing) available here: https://dl.acm.org/citation.cfm?id=3230977. Our group has three papers in the 28 accepted this year.

“Evaluating the efficiency and effectiveness of adaptive Parsons problems” by Barbara Ericson, Jim Foley, and Jochen (“Jeff”) Rick

These are the final studies from Barb Ericson’s dissertation (I blogged about her defense here). In her experiment, she compared four conditions: Students learning through writing code, through fixing code, through solving Parsons problems, and through solving her new adaptive Parsons problems. She had a control group this time (different from her Koli Calling paper) that did turtle graphics between the pre-test and post-test, so that she could be sure that there wasn’t just a testing effect of pre-test followed by a post-test. The bottom line was basically what she predicted: Learning did occur, with no significant difference between treatment groups, but the Parsons problems groups took less time. Our ebooks now include some of her adaptive Parsons problems, so she can compare performance across many students on adaptive and non-adaptive forms of the same problem. She finds that students solve the problems more and with fewer trials on the adaptive problems. So, adaptive Parsons problems lead to the same amount of learning, in less time, with fewer failures. (Failures matter, since self-efficacy is a big deal in computer science education.)

“Socioeconomic status and Computer science achievement: Spatial ability as a mediating variable in a novel model of understanding” by Miranda Parker, Amber Solomon, Brianna Pritchett, David Illingworth, Lauren Margulieux, and Mark Guzdial

(Link to last version I reviewed.)

This study is a response to the paper Steve Cooper presented at ICER 2015 (see blog post here), where they found that spatial reasoning training erased performance differences between higher and lower socioeconomic status (SES) students, while the comparison class had higher-SES students performing better than lower-SES students. Miranda and Amber wanted to test this relationship at a larger scale.

Why should wealthier students do better in CS? The most common reason I’ve heard is that wealthier students have more opportunities to study CS — they have greater access. Sometimes that’s called preparatory privilege.

Miranda and Amber and their team wanted to test whether access is really the right intermediate variable. They gave students at two different Universities four tests:

  • Part of Miranda’s SCS1 to measure performance in CS.
  • A standardized test of SES.
  • A test of spatial reasoning.
  • A survey about the amount of access they had to CS education, e.g., formal classes, code clubs, summer camps, etc.

David and Lauren did the factor analysis and structural equation modeling to compare two hypotheses: Does higher SES lead to greater access which leads to greater success in CS, or does higher SES lead to higher spatial reasoning which leads to greater success in CS? Neither hypothesis accounted for a significant amount of the differences in CS performance, but the spatial reasoning model did better than the access model.

There are some significant limitations of this study. The biggest is that they gathered data at universities. A lot of SES variance just disappears when you look at college students — they tend to be wealthier than average.

Still, the result is important for challenging the prevailing assumption about why wealthier kids do better in CS. More, spatial reasoning is an interesting variable because it’s rather inexpensively taught. It’s expensive to prepare CS teachers and get them into all schools. Steve showed that we can teach spatial reasoning within an existing CS class and reduce SES differences.

“Applying a Gesture Taxonomy to Introductory Computing Concepts” by Amber Solomon, Betsy DiSalvo, Mark Guzdial, and Ben Shapiro

(Link to last version I saw.)

We were a bit surprised (quite pleasantly!) that this paper got into ICER. I love the paper, but it’s different from most ICER papers.

Amber is interested in the role that gestures play in teaching CS. She started this paper from a taxonomy of gestures seen in other STEM classes. She observed a CS classroom and used her observations to provide concrete examples of the gestures seen in other kinds of classes. This isn’t a report of empirical findings. This is a report of using a lens borrowed from another field to look at CS learning and teaching in a new way.

My favorite part of of this paper is when Amber points out what parts of CS gestures don’t really fit in the taxonomy. It’s one thing to point to lines of code – that’s relatively concrete. It’s another thing to “point” to reference data, e.g., when explaining a sort and you gesture at the two elements you’re comparing or swapping. What exactly/concretely are we pointing at? Arrays are neither horizontal nor vertical — that distinction doesn’t really exist in memory. Arrays have no physical representation, but we act (usually) as if they’re laid out horizontally in front of us. What assumptions are we making in order to use gestures in our teaching? And what if students don’t share in those assumptions?

August 10, 2018 at 7:00 am Leave a comment

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