Posts tagged ‘computing education research’
Google has just released a new report on K-12 CS Education. It’s linked at the bottom. I’m going to quote from a new Wired article that describes one of the big bottomlines.
In a big survey conducted with Gallup and released today, Google found a range of dysfunctional reasons more K-12 students aren’t learning computer science skills. Perhaps the most surprising: schools don’t think the demand from parents and students is there.
Google and Gallup spent a year and a half surveying thousands of students, parents, teachers, principals, and superintendents across the US. And it’s not that parents don’t want computer science for their kids. A full nine in ten parents surveyed viewed computer science education as a good use of school resources. It’s the gap between actual and perceived demand that appears to be the problem.
Searching for Computer Science: Access and Barriers in U.S. K-12 Education
To understand perceptions of computer science and associated opportunities, participation, and barriers, we worked with Gallup, Inc. to survey over 1,600 students, 1,600 parents, 1,000 teachers, 9,600 principals, and 1,800 superintendents. We found:
Exposure to computer technology is vital to building student confidence for computer science learning.
Opportunities to learn computer science at schools is limited for most students. When available, courses are not comprehensive.
Demand for CS in schools is high amongst students and parents, but school and district administrators underestimate this interest.
Barriers to offering computer science in schools include testing requirements for other subjects and limited availability and budget for qualified teachers.
I posted a few weeks about our two Georgia Tech papers at the first ever RESPECT conference (post on Miranda Parker’s paper and post on Barbara Ericson’s paper). The conference itself was great — I expect to see a lot more good things coming out of that conference. (The papers should show up in the IEEE Xplore library soon.)
What I liked about RESPECT was that the focus just on broadening participation in computing issues allowed for greater depth and nuance than at ICER or SIGCSE. The first paper of the day was Representation of Women in Postsecondary Computing 1990-2013: Disciplines, Institutional, and Individual Characteristics Matter by Stuart Zweben and Betsy Bizot. They dove into the differences between women in Computer Science vs. Computer Engineering vs. Software Engineering vs… They all have a depressing downward trend — except for one. Interdisciplinary degrees (like our Computational Media major) are the ones in which representation of women is increasing. (The slide they presented with this graphic was easier to read than the one in the paper, but my picture of the slide is less clear.)
I also found fascinating the paper by Hodari, Ong, Ko, and Smith, Enabling Courage: Agentic Strategies of Women of Color in Computing. They pointed out differences in the experiences of women of color. I was quite surprised at how different they are. (The below graph isn’t in the paper, so you’ll have to make do with my picture of the slide.) That relatively flat red line at the bottom is the percentage of Hispanic or Latina Females in computer science. I found the flatness of that line encouraging. In the last few years, we’ve had a massive rise in enrollment. The fact that the Hispanic/Latina women line is pretty steady means that we must have had a commensurate rise in the numbers of Hispanic/Latina women in CS.
There were a bunch of short papers and lightning talks that left me wanting more detail — which is exactly what they’re supposed to do. The paper Encouraging Online Contributions in Underrepresented Populations by Nacu, Martin, Sandherr, and Pinkard got me thinking about the importance of co-design (involving the target student populations involved in the creation of the classes, like the participatory design methods that Betsy DiSalvo uses) to get buy-in and to insure that the interventions are culturally appropriate.
The RESPECT panels didn’t work as well for me — and I admit to being on one of the two panels. They were more like a bunch of short presentations, and went on too long with little discussion. It’s hard to get panels to work in a research conference. Everybody wants to talk about their thing. Panels work best when there is some disagreement on the panel, and the discussion can help everyone to gain a new perspective.
RESPECT was popular which led to a minor problem. The exemplary paper sessions were packed with all the RESPECT attendees and all the co-located STARS attendees who wanted to hear the great research results! They’re going to need a bigger space next year. That’s a good problem to have for a first time conference.
NYTimes recently had a series of op-ed articles about the role of technology in our world, specifically, “Is Silicon Valley Saving the World, or Just Making Money?” The piece by Melinda Gates (quoted below) caught my attention because she’s invoking the desire to meet students’ “different learning styles” (see blog post on this theme, and why it leads to worse learning).
There’s an important issue here (beyond me critiquing Melinda Gates, who does important work that I admire). It’s not all technology. We need other disciplines as well. Educational psychologists should be informing these developers at Facebook to tell them, “Stop. That’s a bad idea.”
I was at a workshop last year at Stanford about how to grow more CS Education Research in the United States. Andrew Ng spoke to us about the research going on at Coursera. He was clearly not previously informed about the focus of the workshop. When asked, “Would you want to hire more PhD’s in CS Education?” he answered (my paraphrase), “Sure, but we just hire CS PhD’s, and they’re smart enough to pick up anything on-the-fly.” No, that’s wrong. CS is not a superset of all other disciplines. That belief is exactly the problem I see in the below quoted piece. Scholars in other areas do know things that CS PhD’s don’t, and they bring something unique to the table. Believing that it’s all technology is exactly why Silicon Valley gets accused of being more interested in money than having actual positive impact.
One of the biggest problems in American education is that teachers have to teach 30 students with different learning styles at the same time. Developers at Facebook, however, have built an online system that gives teachers the information and tools they need to design individualized lessons. The result is that teachers can spend their time doing what they’re best at: inspiring kids.
I’ve written a couple times now about the workshop I attended at the University of Oldenburg the first week of June. (See the post where I talked about my two weeks in Germany.) For Blog@CACM, I wrote a post about teaching as collective practice and the workshop I took with Barbara Hofer (see post here). I wrote about learning about teacher beliefs and self-efficacy from Helenrose Fives here (see post).
Before we left for the workshop, I got to spend time with Ira Diethelm at the University of Oldenburg and one of her students. Ira is one of at least 16 (that Ira could count) CS Education professors in Germany. Ira works with pre-service teachers, in-service teachers, and graduate students. Her graduate students build outreach efforts and curricula as part of their research, then roll them out and provide resources to teachers. It’s remarkable what Ira is doing, and I understand that the other German CS Ed professors do similar things. I came away with a new insight: If we want to bootstrap and sustain CS Education in the United States, we should fund several endowed chairs of CS Education at top Schools of Education. Eventually, we have to have pre-service computing education programs if we want to make CS education sustainable (see that post here). Creating these endowed chairs gives us the opportunity to create positions like Ira’s in the United States.
Overall, the workshop was a terrific experience. The PhD student work was fascinating, and I enjoyed discussing their research with them. It was great to hear about German research perspectives that I hadn’t previously, like the Model of Educational Reconstruction that informs science education (see paper here). Barbara and Helenrose were only two of a several outstanding international education researchers who attended. As I mentioned to Pat Alexander (who has a lengthy Wikipedia page of her accomplishments), I enjoyed being able to wallow in educational psychology for a week, because I so rarely get to do that. I gave a talk on three of our projects related to the theme of developing teachers: on Lijun Ni’s work on teacher identity, on the Disciplinary Commons for Computing Education, and on our ebook for preparing CS teachers. (See Slideshare here.)
The response to my talk was fascinating. Some of the German mathematics education researchers are deeply opposed to computing education in schools. (I suspect that more than one of them completely skipped my talk because they are so opposed.) “Computing education keeps stealing from mathematics teachers, and learning mathematics is more important.” At my talk, Pat Alexander asked me the same question that Peter Elias asked Alan Perlis in 1961, “Won’t the computer eventually just understand us? Doesn’t the computer just become invisible and not need to be programmed?” I told the story about Alan Perlis’s talk and about Michael Mateas’s argument, “There will always be friction.” From the computing educators, I heard a lot of anger. The German computing education researchers feel that other fields squeeze CS out because the they are not willing to allow computing education to take up any time or budget in the curriculum.
Probably the most interesting pushback was against computational thinking. The educational psychologists thought it was unbelievable that learning computing would in any way impact the way that people think or problem-solve in everyday life. “Didn’t we believe that once about Latin? and Geometry?” asked Gavin Brown. The psychologists at the workshop I attended saw a clear argument that we need to introduce computing in high school so that students can see if it’s for them, but not to teach general problem-solving skills. If we really want algorithmic thinking, they can design easier ways to achieve that goal than teaching programming.
We can probably help students to learn about computing in such a way that it might influence problem-solving on the computer. That’s part of Jeanette Wing’s model of Computational Thinking (see her 2010 paper). It’s the “Computational Thinking in Daily Life” part that the psychologists weren’t buying. That learning about computation helps with computational X is quite reasonable. If you understand what IP addresses are, we can help you to understand DNS problems and to realize that it’s not really that big of a deal if Wikipedia stores your IP address (see story about Erika Poole’s research). There is evidence that learning one programming language will likely transfer to another one (see Michal Armoni’s paper on transfer from Scratch to a text-based language). Learning to program is unlikely to influence any problem-solving in everyday life.
Source of the “Geek Gene”? Teacher beliefs: Reading on Lijun Ni, Learning from Helenrose Fives on teacher self-efficacy
I discovered the below quoted post when I was looking up a paper by my former student, Lijun Ni. It’s nice to see her work getting recognized and reviewed! I talked a lot about her work when I was talking to PhD students at the University of Oldenburg program — Lijun has studied the beliefs of CS teachers, and that’s super important.
One of the other international guests at the Oldenburg program I attended last month (see post here) was Helenrose Fives who has literally written the book on teacher beliefs (see Amazon reference). Several of the PhD students who presented their research talked about student teachers having lower self-efficacy after actually being in the classroom, less commitment to ideals like inquiry learning, and less belief that students can learn. Helenrose said that that’s really quite common. Teachers have a high level of self-efficacy (“I can teach using novel approaches that will really help students learn!”) before they enter the classroom, and that sense of self-efficacy falls off a cliff once they face the reality of the classroom. The self-efficacy rises over time (up and down, but mostly up) but never reaches the optimism of before teachers enter the classroom.
I talked to Helenrose about what her work means for University CS teachers. In general, the work she describes is about school teachers, not faculty. She agreed that it’s possible for University CS teachers to have high self-efficacy even if they are not successful teachers, because University teachers define self-efficacy differently than school teachers. School teachers are responsible for student learning. They know individual students. They actually know if they are successful in their teaching or not (in terms of student learning and engagement). University teachers tend to have larger classes, and they tend to teach via lecture. They usually have little knowledge of individual student learning and engagement. Their sense of self-efficacy may arise from their ability to succeed at their task, “I can give great lectures. (Almost nobody falls asleep.) I can manage huge classes.” Where they do have knowledge of learning and evidence of ineffective teaching, they may simply decide that it’s the student’s fault. Perhaps this is where the Geek Gene is born.
Here’s a hypothesis: If a University teacher has high self-efficacy (great confidence in his or her teaching ability) and sees evidence of students not learning, it’s rational for that teacher to believe that the problem lies with the students and that the problem is innate — beyond the ability of the teacher to improve it.
In the first study, Ni interviewed teachers about their identity in order to establish what strengths and weaknesses are common in high school computer science teachers. She found that the teaching identity of computer science teachers is largely underdeveloped compared to teachers in other fields, and that often computer science teachers prefer to identify as a math teacher or a business teacher, rather than a computer science teacher.
Further, she found that high school computer science teachers generally do not have any sort of teaching support community to turn to, because they are often the only computer science teacher at their school.
All of these problems combine to keep computer science teachers from developing a strong teaching identity centered in the computer science field. Instead, we have teachers with low commitment levels to the field training our next generation of programmers in basic computing skills that are generally unrelated to the field of computer science itself.
I had the honor to serve on Tom Park’s dissertation committee and got to see this work unfold. It’s important to do. Computer scientists are happy to tell you that “HTML is not really programming,” and that’s true. But what computing education researchers need to realize is that HTML is a formal, computing-interpreted notation — probably the first one that most computing students ever face. Understanding what works and doesn’t there is important to understanding what’s hard about formal computing representations at all, versus what’s complicated because it’s programming. For example, over 50% of the knowledge-based errors that were observed in the study were never resolved. That’s the definition of a hard problem that’s worth understanding to improve education. It’s also important to consider — are those also learning difficulties that we see in programming?
In the end, the number of errors under the three aforementioned categories broke down as follows:
70.9% of all errors were skill-based errors.
16.9% were rule-based errors.
12.1% were knowledge-based errors.
As mentioned, most of the errors were resolved during the task completion process, but some were not, and they broke down like this:
4.3% of all skill-based errors were unresolved.
39.6% of rule-based errors were unresolved.
52.1% of knowledge-based errors were unresolved.
Want to give a lightning talk or present a poster in Omaha, August 10-12 to spark discussion or discover possible collaborations? Keep reading!
Lightning Talk Application Deadline: June 15, 2014 (submission details below)
ICER 2015 — International Computing Education Research Conference
University of Nebraska, Omaha
August 10-12, 2015
What is a Lightning Talk?
Lightning Talks are strictly timed 3 minute presentations intended to further expand the ICER community and spark discussion among conference participants. The intent is for these talks to provide a venue in support of new ideas and newcomers to our community. Lightning Talks are a great way to get early feedback on a work in progress, to demo a new tool or technique, and to find potential collaborators at other institutions.
What is a Poster?
Posters are a new way for ICER attendees to present early results, gain feedback from conference attendees, find collaborators on a topic, and/or spark discussion among conference participants. The intent is for these posters is similar to lightning talks in that they provide a venue in support of new ideas and newcomers to our community.
Can I do both – give a Lightning Talk and have a Poster?
Absolutely! New this year, Lightning Talk presenters may elect to present a poster in conjunction with their lightning talk to provide additional information to curious parties and help foster post-lightning talk discussion.
Note: Work already being presented at ICER (i.e., accepted papers, doctoral consortium submissions) is ineligible for the lightning talks or poster sessions.
Submissions for consideration as lightning talks should use the provided MS Word template and are limited to a maximum of 300 words. Abstracts should be submitted no later than June 15 to Leo Porter at email@example.com for consideration. The template can be found at:
Are there additional opportunities to receive feedback on a work in progress?
If you are interested in an interactive feedback session after ICER, you may also want to check out the Works in Progress Workshop:
Please let me know if you have any questions!
Assistant Teaching Professor, Computer Science
University of California, San Diego