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.
Thursday, October 22, 2015, Atlanta, GA
A satellite workshop of the 2015 IEEE Symposium Visual Languages and Human-Centric Computing (VL/HCC) https://sites.google.com/site/vlhcc2015
Scope and Goals
Blocks programming environments represent program syntax trees as compositions of visual blocks. This family of tools includes Scratch, Code.org’s Blockly lessons, App Inventor, Snap!, Pencil Code, Looking Glass, etc. They have introduced programming and computational thinking to tens of millions, reaching people of all ages and backgrounds.
Despite their popularity, there has been remarkably little research on the usability, effectiveness, and generalizability of affordances of these environments. The goal of this workshop is to begin to distill testable hypotheses from the existing folk knowledge of blocks environments and identify research questions and partnerships that can legitimize, or discount, pieces of this knowledge. It will bring together educators and researchers who work with blocks languages and members of the broader VL/HCC community interested in this area. We seek participants with diverse expertise, including, but not limited to: design of programming environments, instruction with these environments, the learning sciences, data analytics, usability, and more.
The workshop will be a generative discussion that sets the stage for future work and collaboration. It will include participant presentations and demonstrations that frame the discussion, followed by reflection on the state of the field and smaller working-group discussion and brainstorming sessions.
Suggested Topics for Discussion
- Who uses blocks programming environments and why?
- Which features of blocks environments help or hinder users? How do we know? Which of these features are worth incorporating into more traditional IDEs? What helpful features are missing?
- How can blocks environments and associated curricular materials be made more accessible to everyone, especially those with disabilities?
- Can blocks programming appeal to a wider range of interests (e.g., by allowing connections to different types of devices, web services, data sources, etc.)?
- What are the best ways to introduce programming to novices and to support their progression towards mastery? Do these approaches differ for for learners of computing basics and for makers?
- What are the conceptual and practical hurdles encountered by novice users of blocks languages when they face the transition to text languages and traditional programming communities? What can be done to reduce these hurdles?
- How can we best harness online communities to support growth through teaching, motivating, and providing inspiration and feedback?
- What roles should collaboration play in blocks programming? How can environments support that collaboration?
- In these environments, what data can be collected, and how can that data be analyzed to determine answers to questions like those above? How can we use data to answer larger scale questions about early experiences with programming?
- What are the lessons learned (both positive and negative) from creating first programming environments that can be shared with future environment designers?
We invite two kinds of submissions:
- A 1 to 3 page position statement describing an idea or research question related to the design, teaching, or study of blocks programming environments.
- A paper (up to 6 pages) describing previously unpublished results involving the design, study, or pedagogy of blocks programming environments.
All submissions must be made as PDF files to the Easy Chair Blocks and Beyond workshop submission site (https://easychair.org/conferences/?conf=blocksbeyond2015). Because this workshop will be discussion-based, rather than a mini-conference, the number of presentation/demonstration slots are limited. Authors for whom presentation or demonstration is essential should indicate this in their submission.
- 24 Jul. 2014: Submissions due.
- 14 Aug. 2015: Author notification.
- 4 Sep. 2015: Camera ready copies due.
- 22 Oct. 2015: Workshop in Atlanta.
- Franklyn Turbak (chair), Wellesley College
- David Bau, Google
- Jeff Gray, University of Alabama
- Caitlin Kelleher, Washington University, St. Louis
- Josh Sheldon, MIT
- Neil Brown, University of Kent
- Dave Culyba, Carnegie Mellon University
- Sayamindu Dasgupta, MIT
- Deborah Fields, Utah State University
- Neil Fraser, Google
- Mark Friedman, Google
- Dan Garcia, University of California, Berkeley
- Benjamin Mako Hill, University of Washington
- Fred Martin, University of Massachusetts Lowell
- Paul Medlock-Walton, MIT
- Yoshiaki Matsuzawa, Aoyama Gakuin University
- Amon Millner, Olin College
- Ralph Morelli, Trinity College
- Brook Osborne, Code.org
- Jonathan Protzenko, Microsoft Research
- Ben Shapiro, Tufts University
- Wolfgang Slany, Graz University of Technology
- Daniel Wendel, MIT
I agree with the author of this recent NYTimes post. Women do seem to be more attracted to socially meaningful work than males. I don’t think that’s the complete solution, though. We have evidence that women are more likely to pursue studies in computer science if encouraged (see Joanne Cohoon’s work) and if they feel a sense of “belonging” with the department (see our work in Georgia). If we want more women in engineering, we have to think about recruitment (as this article does) and retention (as other work does).
Why are there so few female engineers? Many reasons have been offered: workplace sexism, a lack of female role models, stereotypes regarding women’s innate technical incompetency, the difficulties of combining tech careers with motherhood. Proposed fixes include mentor programs, student support groups and targeted recruitment efforts. Initiatives have begun at universities and corporations, including Intel’s recent $300 million diversity commitment.
But maybe one solution is much simpler, and already obvious. An experience here at the University of California, Berkeley, where I teach, suggests that if the content of the work itself is made more societally meaningful, women will enroll in droves. That applies not only to computer engineering but also to more traditional, equally male-dominated fields like mechanical and chemical engineering.
My daughter is enrolled in Georgia’s “Governor’s Honor Program” which started this week. The program is highly competitive — my daughter filled out multiple applications, wrote essays, and went through two rounds of interviews. Over 700 high school students from across Georgia attend for four weeks of residential classes on a university campus for free.
At the parent’s orientation, we heard from two former GHP students, the Dean of Student Life, the Dean of Residence Halls, the GHP Program Manager, and the Dean of Instruction. It’s that last one who really got me.
“You heard from these students, and many other students. GHP changes lives. There is magic in our program.“
The program sounds remarkable. No grades, no tests. The Dean of Instruction said she told the teachers to “give these students learning opportunities beyond what’s in any high school classroom.” Students are only there to learn for learning’s sake.
I was thrilled for my daughter, that she was going to have this experience. I was also thrilled as a teacher.
I want to teach in a program whose leadership says, “There is magic in our program. Our program changes lives.” Last week, I took my daughter to tour three universities. Our daughter is the youngest of three, so I’ve attended other prospective student tours at other universities. I’ve never heard anybody at any of these universities make that kind of claim.
I don’t mean to critique my leadership at Georgia Tech in particular. When I was the Undergraduate Program Director, I never said anything like that to my teachers or to prospective parents. I am critical of higher education more broadly. Higher education in America sets goals like preparing students for careers, giving them experiences abroad and in research, giving them options so that they can tailor their program to meet their particular desires, and surrounding them with great fellow students — I’ve heard all of those claims many times on many tours. I’ve never heard anyone say, “We change lives.”
Rich DeMillo argued in his book Apple to Abelard that higher education institutions need to differentiate from one another. Offering the same thing in the same way makes it hard to compete with the on-line and for-profit options. At Georgia Tech, the faculty are frequently told, “We get amazingly smart students.” We’re told to think about how to tune our education for these super-smart students. I’ve never been told, “Give these students experiences beyond what they will get in any other program. Create magic. Change their lives.”
What I gained at GHP is a new definition for what higher education should be about. We need to step up our game.
Bobby Schnabel has just been named the new CEO of ACM. This is a big win for computing education. Bobby has been an innovator and leader in efforts to improve computing education policy and broaden participation in computing. Now, he’s in charge of ACM overall, the world’s largest computing professional organization. That gives him a big pulpit for promoting the importance of computing education.
Schnabel has a long history of service to the computing community. He has served in several capacities, including chair, of ACM’s Special Interest Group on Numerical Mathematics (ACM SIGNUM). When Schnabel assumes his role as CEO, he will step down as founding chair of the ACM Education Policy Committee, which led to the creation of Computer Science Education Week in the US, and the formation of the industry/non-profit coalition, Computing in the Core. Schnabel also serves as board member of code.org, and as a member of the advisory committee of the Computing and Information Science and Engineering directorate of the National Science Foundation. He has served as a board member of the Computing Research Association.
Dedicated to improving diversity in computing, Schnabel is a co-founder and executive team member of the National Center for Women & Information Technology (NCWIT), a major non-profit organization in the US for the full participation of girls and women in computing and information technology. He also serves as chair of the Computing Alliance for Hispanic-Serving Institutions Advisory Board.
I found the article below fascinating, but as an instance of a general model. The article describes how scientists who study gun control have very different opinions about gun control than the general American public — who (presumably) don’t draw on scientific evidence to inform their opinions. People who draw on evidence have different opinions than those who don’t. Most people do not draw on evidence when informing their opinions.
I don’t see that the story here is “Scientists are smart and the public is dumb.”
I would bet that if you asked these same gun control scientists about something outside of their area of expertise, they similarly ignore evidence. I work with CS professors all the time who draw on evidence to inform their opinions within their area of expertise (e.g., robotics, HCI, networking), but when it comes to education, evidence goes out the window. Davide Fossati and I did a study (yeah, evidence — we know what that’s worth) describing how CS faculty make decisions (see post here). In my experience, if the evidence is counter to their opinion, evidence is frequently ignored. One of the things we learned in “Georgia Computes!” was just how hard it is to change faculty (see our journal article where we tell this story). CS teachers are pretty convinced that they teach just fine, despite evidence to the contrary. I regularly try to convince my colleagues to teach using active learning approaches like peer instruction given the overwhelming evidence of its effectiveness (see this article, for just one), and I regularly get told, “It really doesn’t work for me.”
People are people, even when scientists and CS faculty.
Of the 150 scientists who responded, most were confident that a gun in the home increases the chance that a woman living there will be murdered (72 percent agreed, 11 percent disagreed), that strict gun control laws reduce homicide (71 percent versus 12 percent), that more permissive gun laws have not reduced crime rates (62 percent versus 9 percent), that guns are used more often in crimes that in self-defense (73 percent versus 8 percent), and that a gun in the home makes it a more dangerous place to be (64 percent versus 5 percent).
Eighty-four percent of the respondents said that having a firearm at home increased the risk of suicide.
These figures stand sharply at odds with the opinions of the American public. A November 2014 Gallup poll found that 63 percent of Americans say that having a gun in the house makes it a safer place to be, a figure that has nearly doubled since 2000. According to the same survey, about 40 percent of Americans keep a gun in the home.