Posts tagged ‘computing education research’
Barbara Ericson and I were invited to be discussants at a showing of “Code: Debugging the Gender Gap.” I highly recommend the movie. It was fascinating to watch, made all the more fun by seeing heroes that I know appear, like Nathan Ensmenger, Avis Yates Rivers, Jane Margolis, Ari Schlesinger, Colleen Lewis, and Maria Klawe.
Afterward, I got to make a few comments — expanding on some of the movie’s points, and disagreeing with others.
The movie makes the argument that men and women aren’t wired differently. We are all capable of learning computer science. They didn’t have to make a biological argument. In the Middle East and many other parts of the world, computer science is female-dominated. Clearly, it’s not biology. (Perhaps surprisingly, I recently got asked that question at one of the top institutes of technology in the United States: “Don’t women avoid CS because their brains work differently?” REALLY?!?)
The movie talks about how companies like IBM and RCA started advertising in the 1970’s and 1980’s for “men” with “the right stuff,” and that’s when the field started masculinizing. They don’t say anything about the role that educators played, the story that Nathan Ensmenger has talked about in his book “The Computer Boys Take Over.” When we realized that we couldn’t teach programming well, we instead started to filter out everyone who would not become a great programmer. For example, that’s when calculus was added into computer science degree requirements. Women were less interested in the increasingly competitive computer science programs, especially when there were obvious efforts to weed people out. That was another factor in the masculinization of the field.
Many of those interviewed in the movie talk about the importance of providing “role models” to women in computing. The work of researchers like the late (and great) Joanne Cohoon show that role models aren’t as big a deal as we might think. Here’s a thought experiment to prove the point: There are biology departments where the faculty are even more male than most CS departments, yet those departments are still female-dominant. What we do know is that women and URM students need encouragement to succeed in CS, and that that encouragement can come from male or female teachers.
Finally, several interviewed in the movie say that we have to get girls interested in CS early because high school or university is “way too late.” That’s simply not true. The chair of my School of Interactive Computing, Annie Antón, didn’t meet computing until she was an undergraduate, and now she’s full Professor in a top CS department. Yes, starting earlier would likely attract more women to computing, but it’s never “too late.”
After the movie, an audience member asked me if I really believed that diversity was important to build better products, and how would we prove that. I told him that I didn’t think about it that way. I’m influenced by Joanna Goode and Jane Margolis. Computing jobs are high-paying and numerous. Women and under-represented minority students are not getting to those jobs because they’re not getting access to the opportunites, either because of a lack of access to computing education or because of bias and discrimination that keep them out. It’s not about making better products. This is a social justice issue.
Koli, Finland, 16-19 November 2017
Original submissions are invited in all areas related to the conference theme and should have an explicit connection to computing education. Topics of interest include, but are not limited to:
- Computing education research: theoretical aspects, methodologies and results;
- Development and use of technology to support education in computing and related sciences, e.g., tools for visualisation or concretisation;
- Teaching and assessment approaches, innovations and best practices;
- Distance, online, blended, and informal learning;
- Learning analytics and educational data mining;
- Computing education in all educational levels, e.g., K12, context and teacher training.
Program Chairs, Koli Calling 2017
The ACM Turing China conference will have a SIGCSE track this May. Come see SIGCSE Chair Amber Settle, world-famous CS educator Dan Garcia (recently in NYTimes) from Berkeley, and me in Shanghai in May.
The ACM TURC 2017 (SIGCSE China) conference is a new leading international forum at the intersection of computer science and the learning sciences, seeking to improve practice and theories of CS education. ACM TURC 2017 will be held in Shanghai, China, 12-14 May, 2017. We invite the submission of original rigorous research on methodologies, studies, analyses, tools, or technologies for computing education.
Source: ACM TURC 2017 (SIGCSE China)
Please follow the survey link below to give feedback to Google on what you think is important in CS education research.
We are collecting input to inform the direction of Google’s computer science (CS) education research in order to better support the field. As researchers, educators, and advocates working in the field everyday, your input is extremely valued. Please complete this survey by Sunday, April 23. Feel free to share this survey with others who may be interested in sharing their insights.Thank you,Jennifer, on behalf of Google‘s CS Education Research & Evaluation team
Kate Cunningham is a first year PhD student working with me in computing education research. She just won an NSF graduate research fellowship, and the College of Computing interviewed her. She explains the direction that she’s exploring now, which I think is super exciting.
“I’m interested in examining the kinds of things students draw and sketch when they trace through code,” she said. “Can certain types of sketching help students do better when they learn introductory programming?” She grew interested in this topic while working as a teacher for a program in California. As she watched students there work with code, she found that they worked solely with the numbers and text on their computer screen.“They weren’t really drawing,” she said. “I found that the drawing techniques we encouraged were really useful for those students, so I was inspired to study it at Georgia Tech.”
Essentially, the idea is that by drawing or sketching a visual representation of their work as they code, students may be able to better understand the operations of how the computer works. “It’s a term we call the ‘notional machine,’” Cunningham explained. “It’s this idea of how the computer processes the instructions. I think if students are drawing out the process for how their code is working, that can help them to fully understand how the instructions are working.” That’s one benefit. Another, she said, is better collaboration. If a student is sketching the process, she posits, the teacher can better see and understand what they’re thinking.
I’ve worked with Susanne in a variety of contexts and recommend checking out her page linked below. She’s taught me a lot about how computing education research connects to the rest of CS, as described in the quote below. She’s done some interesting work on CS teacher professional development. Most recently, she is one of the authors of the “Generation CS” CRA report.
As computer science evolves into a recognized subject in K-12 curricula, we not only need to know how students learn, but we also need to know how to educate and prepare their teachers. The National Science Foundation’s CS10K effort has been an ambitious project with a significant impact on schools and computer science education research. Online learning opportunities, including MOOCs, Khan Academy, Stack Overflow, and Code.org, help many students learn to code and advance their computing knowledge. Online forums can provide data on clicks, completions, progress, and more. How can this data be used to advance how users learn? How can the background and the goals of the learner be integrated into providing personalized and more meaningful help that advances and enhances learning? To answer questions like this, we need to apply knowledge from a range of areas. Computer science education research is an interdisciplinary field that combines learning sciences and areas of computer science, including software engineering, programming languages, machine learning, human-computer interaction, and natural language processing. Techniques, approaches, and tools developed by researchers in these areas have the potential to create new knowledge about learning and teaching computer science. In turn, this new knowledge has the potential to drive new research in computer science.
Elementary School Computer Science – Misconceptions and Developmental Progressions: Papers from SIGCSE 2017
March 8-11, Seattle hosted the ACM SIGCSE Technical Symposium for 2017. This was the largest SIGCSE ever, with over 1500 attendees. I was there and stayed busy (as I described here). This post isn’t a trip report. I want to talk about two of my favorite papers (and one disappointing one) that I’ve read so far.
We are starting to gather evidence on what makes elementary school computer science different than undergraduate computer science. Most of our research on learning programming and computer science is from undergraduates, published in SIGCSE venues. We know relatively little about elementary school students, and it’s obvious that it’s going to be different. But how?
Shuchi Grover and Satabdi Basu of SRI are starting to answer that question in their paper “Measuring Student Learning in Introductory Block-Based Programming: Examining Misconceptions of Loops, Variables, and Boolean Logic.” They looked at the problems that 6th, 7th, and 8th graders had when programming in Scratch. They’re reporting on things that I’ve never heard of before as misconceptions at the undergraduate level. Like this quote:
Students harbored the misconception that a variable is a letter that is used as a short form for an unknown number – an idea that comes from middle school mathematics classes. Together, this led students to believe that repeat(NumberOfTimes) was a new command. One student conjectured it was a command for multiplication by 5 (the value of NumberOfTimes), while another thought it would print each number five times… After being told that NumberOfTimes was indeed a variable, the students could correctly predict the program output, though they continued to take issue with the length of the variable name.
I find their description believable and fascinating. Their paper made me realize that middle school students are expending cognitive load on issues like multi-character variable names that probably no computer scientist even considered. That’s a real problem, but probably fixable — though the fix might be in the mathematics classes, as well as in the CS classes.
The paper that most impressed me was from Diana Franklin’s group, “Using Upper-Elementary Student Performance to Understand Conceptual Sequencing in a Blocks-based Curriculum.” They’re studying over 100 students, and starting to develop general findings about what works at each of these grade levels. Three of their findings are quoted here:
Finding 1: Placing simple instructions in sequence and using simple events in a block-based language is accessible to 4th-6th grade students.
Finding 2: Initialization is challenging for 4th and 5th grade students.
Finding 3: 6th grade students are more precise at 2-dimension navigation than 4th and 5th grade students.
I’ve always suspected that there was likely to be an interaction between a student’s level of cognitive development and what they would likely be able to do in programming, given how much students are learning about abstraction and representation at these ages. Certainly, programming might influence cognitive development. It’s important to figure out what we might expect.
That’s what Diana’s group is doing. She isn’t saying that fourth grader’s can’t initialize variables and properties. She’s saying it’s challenging for them. Her results are likely influenced by Scratch and by how the students were taught — it’s still an important result. Diana’s group is offering a starting point for exploring these interactions and understanding what we can expect to be easy and what might be hard for the average elementary school student at different ages. There may be studies that also tell us about developmental progressions in countries that are ahead of the US in elementary school CS (e.g., maybe Israel or Germany). This is the first study of its kind that I’ve read.
SIGCSE 2017 introduced having Best Paper awards in multiple categories and Exemplary Paper awards. I applaud these initiatives. Other conferences have these kinds of awards. The awards helps our authors stand out in job searches and promotion time.
To be really meaningful awards, though, SIGCSE has to fix the reviewing processes. There were hiccups in this year’s reviewing where there wasn’t much of a match between reviewer expertise and the paper’s topic. The hiccups led to papers with significant flaws getting high rankings.
The Best Paper award in the Experience Report category was “Making Noise: Using Sound-Art to Explore Technological Fluency.” The authors describe a really nifty idea. They implement a “maker” kind of curriculum. One of the options is that students get toys that make noise then modify and reprogram them. The toys already work, so it’s about understanding a system, then modifying and augmenting it. The class sounds great, but as Leah Buchele has pointed out, “maker” curricula can be overwhelmingly male. I was surprised that this award-winning paper doesn’t mention females or gender — at all. (There is one picture of a female student in the paper.) I understand that it’s an Experience Report, but gender diversity is a critical issue in CS education, particularly with maker curricula. I consider the omission of even a mention of gender to be a significant flaw in the paper.