Posts tagged ‘teachers’
My Blog@CACM post this month is on the AAAS symposium I attended on undergraduate STEM education (see post here). The symposium set up for me a contrast between computing education and other STEM education. In math and science education, faculty are more likely to get continuing professional development and to value education more than CS faculty.
Why is it different in CS? In the blog post, I suggest that part of the issue is maturation of the field. But I have another hypothesis — I suggest that most CS teachers, especially at the undergraduate level, don’t think of themselves as teachers.
In my book Learner-Centered Design of Computing Education, I use Lave & Wenger’s situated learning theory as a lens for understanding motivations to pursue computing education. Lave & Wenger say every learner aims to join a community of practice. Learners start out on the periphery of the community, and work their way towards the center, adopting the skills, values, and knowledge that those in the center hold. They might need to take classes because that’s what the community values, or maybe they do an apprenticeship. The community of practice provides the learner and the practitioners a sense of identity: “I belong with this group. I do this practice. This is who I am.”
Lijun Ni taught me the value of teacher identity. Someone who says “I’m a math teacher” (for example) will join math teacher organizations, will seek out professional development, and will more likely be retained longer as a teacher. That’s their identity.
I believe that many science and math teachers (even at the undergraduate level) feel a sense of identity as teachers. Even at research universities, those teaching the intro courses in mathematics and science are likely teachers-first. They know that they are mostly no preparing future mathematicians, biologists, chemists, and physicists. They are preparing students for their chosen professions, perhaps in engineering, medicine, or computer science. The math and science teachers belong to a community of practice of teachers, e.g., they have a goal to be like the best teachers in their profession. They have an identity as teachers, e.g., they strive to be better math and science teachers.
I suspect that CS teachers feel a sense of identity as software developers. They see themselves as programmers primarily. They see themselves as producing future programmers. They take pride in what they can do with code. They have a sense of guardianship — they want the best and brightest in their field.
There’s a difference between CS teachers as programmers vs CS teachers. Programmers train other programmers. They learn new programming languages, new techniques of programming, the latest tools. Teachers teach everyone, and they learn how to be better at teaching. We need CS teachers to be teachers. It’s less important that they know the latest industry gadgets. It’s more important that they learn how to teach “all” about CS, and how to teach that CS better.
When Grady Booch came to SIGCSE 2007, I was surprised at how excited everyone was — people still talk about that visit (e.g., see the explanation for the BJC approach to computing). I realized that, for most of the people in the room, Grady was a role model. He was at the center of community that they most cared about. Note that Grady is not a teacher. He’s an exceptional software engineer.
There are serious ramifications of a teacher with an identity as a software engineer. I had a discussion a few months ago with one of our instructors, who told me, “I just don’t get why women would even want to be in computer science. Working in a cubicle is not a great place for women to be! They should get a better job.” I was shocked. I didn’t tackle the gender issues first. I started out trying to convince him that computer science doesn’t just lead to a cubicle. You could study computer science to become something other than a software developer, to work somewhere other than a cubicle. He wasn’t buying my argument. I realized that those cubicle jobs are the ones he wants to prepare students for. That’s where he imagines the best programmers working. He doesn’t want to teach computer science for whatever the students need it for. He prepares future programmers. That’s how he defines his job — a master software engineer with apprentice software engineers.
I am calling out undergraduate CS teachers in this post, but I suspect that many high school CS teachers see themselves as software developers (or as trainers of software developers), more than as teachers of computer science. I hear about high school CS teachers who proudly post on the wall the t-shirts of the tech companies who employ their former students. That’s a software developer focus, an apprenticeship focus. That’s not about teaching CS for all.
What would it take to shift the community of practice of CS teachers to value teaching over software development? It’s an important change in perspective, especially if we care about CS for all. Not all of our students are aiming for jobs in software development.
How did other STEM disciplines do it? How did they develop a culture and community of practice around teaching?
These are the right sort of questions to be asking, and then using when creating real programs. How would we get more undergraduate computing majors to consider teaching? We can’t do much about salary. Free tuition and student loan forgiveness are feasible and could result in many more teachers (and are being explored by ECEP states).
CERP asked undergraduate computing majors what would increase their interest in becoming a middle or high school computing teacher. As seen in the above graphic, financial incentive in the form of a higher teaching salary, free tuition for teacher training, and forgiven student loans were the top factors increasing students’ interest in becoming a middle or high school computing teacher. These findings provide insights into how to generate more computing educators for the K-12 school system, which is becoming increasingly important, given recent efforts to promote widespread K-12 computing education.
Top business leaders, 27 governors, urge Congress to boost computer science education – The Washington Post
I saw on Facebook that Hadi Partovi was at Congress. Now I see why — there’s an effort underway to get Congress to fund more in CS education. I’m wondering what they want to get funded. Incentives for teachers? Professional development? Pre-service education? Does someone know the details?
Despite this groundswell, three-quarters of U.S. schools do not offer meaningful computer science courses. At a time when every industry in every state is impacted by advances in computer technology, our schools should give all students the opportunity to understand how this technology works, to learn how to be creators, coders, and makers — not just consumers. Instead, what is increasingly a basic skill is only available to the lucky few, leaving most students behind, particularly students of color and girls.
How is this acceptable? America leads the world in technology. We invented the personal computer, the Internet, e-commerce, social networking, and the smartphone. This is our chance to position the next generation to participate in the new American Dream.
At LaTICE 2016, I attended a session on teacher professional development. I work at preparing high school CS teachers. I felt like I’d be able to relate to the professional development work. I was wrong.
One of the large projects presented at LaTICE 2016 was the T10kT project (see link here) whose goal is to use technology to train 10,000 teachers. What I didn’t realize at first was that the focus is on higher-education teachers, not high school teachers. The only high school outreach activity I learned about at LaTICE 2016 was from the second keynote, on an Informatics Olympiad from Madhavan Mukund (see slides here) which is only for a select group of students.
India has 500 universities, and over 42,000 higher education institutions. They have an enormous problem trying to maintain the quality of their higher-education system (see more on the Wikipedia page). They rely heavily on video, because videos can be placed on a CD or USB drive and mailed. The T10kT instructors can’t always rely on Internet access even to higher-education institutions. They can’t expect travel even to regional hubs because many of the faculty can’t travel (due to expense and family obligations).
As can be seen in the slide above, they have a huge number of participants. I asked at the session, “Why?” Why would all these higher-education faculty be interested in training to become better teachers? The answer was that participants get certificates for participating in T10kT, and those certificates do get considered in promotion decisions. That’s significant, and something I wish we had in the US.
I tried to get a sense for how many primary and secondary schools there are in India, and found estimates ranging from 740K to 1.3M. Compulsory education was only established in 2010 (goes to age 14), and is not well enforced. I heard estimates that about 50% of school-age children go to school because only enrollment is checked, not attendance.
Contrast this with the CS10K effort in the United States. There are about 25-30,000 high schools in the US. Having 10K CS teachers wouldn’t reach every school, but it would make a sizable dent. A goal to get 10K CS teachers in Indian high schools would be laughable. When you increase the number of high schools by two orders of magnitude, 10,000 teachers barely moves the needle. Given the difficulty of access and uncertain Internet, it’s certainly not cheaper to provide professional development in India. They have an enormous shortage of teachers — not just CS. They lack any teachers at all in many schools. The current national focus is on higher-education because the secondary and primary school problems are just so large.
Alan Kay has several times encouraged me to think about how to provide educational technology to support students who do not have access to a teacher. I resisted, because I felt that any educational technology was a poor substitute for a real teacher. Now I realize what a privilege it is to have any teacher at all, and how important it is to think about technology-based guided learning for the majority of students worldwide who do not have access to a teacher.
How do we do it? How do we design technology-based learning supports for Indian students who may not have access to a teacher? I attended a session on IITBx, the edX-hosted MOOCS developed by IIT-Bombay. I tweeted:
— Mark Guzdial (@guzdial) April 3, 2016
One of the IIT-Bombay graduate students responded:
— akothiyal (@akothiyal) April 3, 2016
Here’s the exchange as a screencap, just in case the Twitter feed doesn’t work right above:
I’m sure that Aditi (whose work was described in the previous blog post) is right. Developers in the US can’t expect to build technologies for India and expect them to work, not without involving Indian learners, teachers, and researchers. One of the themes in my book Learner-Centered Design of Computing Education is that motivation is everything in learning, and motivation is tied tightly to notions of identity, community of practice, and context. I learned that I don’t know much about any of those things for India, nor anywhere else in the developing world. The problems are enormous and worth solving, and US researchers and developers have a lot to offer — as collaborators. In the end, it requires understanding on the ground to get the context and motivation right, and nothing works if you don’t get that right.
I was at the Learning and Teaching in Computing Education (LaTICE 2016) conference in Mumbai in early April. It was one of my most memorable and thought-provoking trips. I have had few experiences in Asia, and none in India, so I was wide-eyed with amazement most of my time there. (Most of the pictures that I am including in this series of blog posts are mine or come from the LaTICE 2016 gallery.)
I was invited to join discussants at the LaTICE Doctoral Consortium on the day before the conference. LaTICE was hosted at IIT-Bombay, and IIT-Bombay is home to the Inter-disciplinary Program in Educational Technology (see link here). The IPD-ET program is an impressive program. Only five years old, it already has 20 PhD students. The lead faculty are Sahana Murthy and Sridhar Iyer who are guiding these students through interesting work. (Below picture shows Sahana with the DC co-chairs, Anders Berglund from Uppsala University and Tony Clear from Auckland University of Technology.) The Doctoral Consortium had students from across India and one from Germany. Not all were IDP-ET students, but most were.
Talking to graduate students was my main activity at LaTICE 2017. Aman Yadav (from Michigan State, in the back of the below picture) and I missed a lot of sessions as we met with groups of students. I don’t think I met all the IDP-ET students, but I met many of them, and wrestled with ideas with them. I was pleased that students didn’t just take me at my word — they asked for explanations and references. (I ripped out half of the pages of my notebook, handing out notes with names of papers and researchers.) I feel grateful for the experience of hearing about so many varied projects and to talk through issues with many students.
I’m going to take my blog writer’s prerogative to talk about some of the IDP-ET students’ work that I’ve been thinking about since I got back. I’m not claiming that this is the best work, and I do offer apologies to the (many!) students whose work I’m not mentioning. These are just the projects that keep popping up in my (still not sleeping correctly) brain.
Aditi Kothiyal is interested in how engineers estimate. Every expert engineer does back-of-the-envelope estimation before starting a project. It’s completely natural for them. How does that develop? Can we teach that process to students? Aditi has a paper at the International Conference of the Learning Sciences this year on her studies of how experts do estimation. I find this problem interesting because estimation might be one of those hard-to-transfer higher-order thinking skills OR it could be a rule-of-thumb procedure that could be taught.
Shitanshu Mishra is exploring question-posing as a way to encourage knowledge integration. He’s struggling with a fascinating set of issues. Question-posing is a great activity that leads to learning, but is practiced infrequently in classroom, especially by the students who need it the most. Shitanshu has developed a guided process (think the whiteboards in Problem-Based Learning, or classroom rituals in Janet Kolodner’s Learning-By-Design, or Scardamalia & Bereiter’s procedural facilitation) which measurably helps students to pose good questions that encourage students to integrate knowledge. When should he guide students through his question-posing process? Is it important that students use his process on their own?
Yogendra Pal is asking a question that is very important in India whose answer may also be useful here in the US: How do you help students who grew up in a non-English language in adapting to English-centric CS? India’s constitution recognizes 22 languages, and has 122 languages spoken by many Indian citizens on a daily basis. Language issues are core to the Indian experience. CS is very English-centric, from the words in our programming languages, to the technical terms that don’t always map to other languages. Yogendra is working with students who only spoke Hindi until they got to University, where they now want to adapt to English, the language of the Tech industry. I wonder if Yogendra’s scaffolding techniques would help children of immigrant families in the US succeed in CS.
Rwitajit Majumdar is developing visualizations to track student behavior on questions over time. Originally, he wanted to help teachers get a sense of how their students move towards a correct understanding over multiple questions during Peer Instruction. Now, he’s exploring using his visualizations with MOOC data. I’m interested in his visualizations for our ebooks. He’s trying to solve an important problem. It’s one thing to know that 35% of the students got Problem #1 right, and 75% got (similar) Problem #2 right. But is it the same 25% of students who got both wrong? What percentage of students are getting more right, and are there any that are swapping to more wrong answers? Tracking students across time, across problems is an important problem.
Overall, the LaTICE conference was comparable to SIGCSE or ITiCSE. It was single track, though it’s been dual-track at some instances. LaTICE is mostly a practitioner’s conference, with a number of papers saying, “Here’s what I’m doing in my class” without much evaluation. I found even those interesting, because many were set in contexts that were outside my experience. There are some good research papers. And there are some papers that said some things that I felt were outright wrong. But because LaTICE is a small (< 200 attendees, I’d guess) and collegial conference, I had one-on-one conversations with all the authors with whom I disagreed (and many others, as well!) to talk through issues.
My keynote was based on my book, Learner-Centered Design of Computing Education: Research on Computing for Everyone. I talked about why it’s important to provide computing education to more than computing majors, and how computing education would have to change for different audiences. Slides are here: http://www.slideshare.net/markguzdial/latice-2016-learnercentered-design-of-computing-education-for-all
The most remarkable part of my trip was simply being in India. I’ve never been any place so crowded, so chaotic, so dirty, and so vibrant. I felt like I took my life in my hands whenever I crossed the street after noon on any day (and given the pedestrian accidents that some conference participants reported seeing, including one possible fatality, I likely was taking a risk). I went out for three runs around Mumbai and across campus (only in the morning when the traffic was manageable) and enjoyed interactions with cows and monkeys. I was shocked at the miles and miles of slums I saw when driving around Mumbai. I got stuck on one side of a major street without any idea how I could possibly get through the crowds and traffic to the other side — on a normal Sunday night. The rich colors of the Indian clothing palette were beautiful, even in the poorest neighborhoods. There was an energy everywhere I went in Mumbai.
I’ve not experienced anything like Mumbai before. I certainly have a new sense of my own privilege — about the things I have that I never even noticed until I was somewhere where they are not given. Given that India has 1.2 billion people and the US only has some 320 million, I’m wondering about how I define “normal.”
When I saw Elizabeth’s debrief, I asked her if I could share it on here. She graciously prepared this guest blog post, with more detail to explain what she did. Thanks, Elizabeth!
About a year ago, I used a number of venues to recruit participants for an anonymous study about the distributions of grades in CS classes. The study involved a minor deception, and because we do not have the emails of all the participants, I’m posting the debrief openly.
Dear study participant,
You’re getting this email because about a year ago you participated in my research project, “An investigation of the grades distributions in university computer science”. In this project, I showed you a series of six histograms and I asked you how often you saw that distribution’s shape in your own teaching, as well as how you’d categorize the distribution (normal, bimodal, etc).
I’m writing to you know to let you know this study involved a minor deception. We were actually most interested whether you’d label some ambiguous distributions as “normal” versus “bimodal”. We’ve now completed our analysis, and we want to debrief you before we write up our results for publication.
As you may know, there is a common perception amongst CS educators that grades distributions are bimodal. However, upon statistical analysis of the grades distribution available to us, we discovered that most of our grades distributions pass statistical tests of normality and very few of them of them pass the statistical tests of bimodality (see link for more).
We were curious why the perception of bimodal grades is so prevalent, even when grades may actually be normally-distributed. Ahadi and Lister argued at ICER 2013 that the perception of bimodal grades comes from CS educators believing that some students possess an innate gift/talent to do computer science. Regular readers of Mark’s blog would know this as the “Geek Gene Hypothesis”: the notion that when it comes to CS, you either can get it, or you can’t.
Ahadi and Lister argued that when people see two “peaks” in their grades, it’s because they’re expecting to see two different populations to be represented: the students who get it, and the students who don’t. Usually, bimodal distributions represent data where you’ve sampled two different populations together at the same time. If CS grades were bimodal, that could imply we have two different populations of students (e.g. those who get it + those who don’t). Whereas if CS grades are normal, it would imply (but not prove) our students form a spectrum, where most students understand some — but not all — of the material.
In the study you participated in, all six histograms were randomly-generated normal distributions with a small sample size (and so looked noisy). We wanted to test two things:
- RQ1. Are CS educators more likely to label ambiguous distributions as “bimodal” if they believe that some students are inherently predisposed to do well in CS?
- RQ2. If we tell CS educators that it’s a commonly-held belief that CS grades are bimodal, will educators be more likely to label ambiguous distributions as “bimodal”?
For a random half of the participants, before you categorized the distributions, we asked you “It is a commonly-held belief that CS grades distributions are bimodal. Do you find this to be the case in your teaching?” The other half of the participants saw this question after categorizing the distributions. This priming was used to test RQ2.
If our consent form had said the true intent of the study, then all participants would have been primed, rather than a random half. Our minor deception about the purpose of the study was necessary to answer this research question.
We delayed the debriefing until after our analysis was complete, on the assumption you’d want to know the preliminary results of the study. We indeed found that participants who agreed more strongly with the statement “Some students are innately predisposed to do better at CS than others” were statistically significantly more likely to label ambiguous distributions as bimodal.
Participants who had been primed were more likely to label distributions as bimodal — and this effect was stronger if they also agreed that CS ability was innate.
To ensure participant anonymity, we did not collect names and emails on the SurveyMonkey survey. As a result we have no way to link your responses to your identity.
We will be submitting our results for publication in the coming months, and will happily disseminate the paper once it is published. If you’d like to receive a copy of the paper once it’s published, add your email to this form. If you have any further questions, please contact us.
Survey for CS Faculty on use of Evidence-based Instructional Practices: Guest blog post from Scott Grissom
Clearly an important topic — I’m sharing this here, with thanks to Scott.
The SIGCSE Committee on Evidence-based Instructional Practices is investigating the most commonly used teaching practices in CS education (such as classroom activities, student learning goals and assessment techniques). We are replicating a study from physics education that surveyed over 800 faculty. We have already used the validated instrument in a pilot study with twelve institutions and have received 160 responses so far.
Rather than simply send a survey link to this mailing list that might create a skewed sample, we are inviting entire CS departments to survey their members. Our goal is to survey instructors from many 2-year, 4-year, private, public, research and teaching institutions.
The project will allow us to accomplish three important objectives:
- Provide a baseline of instructional practices used in CS higher education.
- Compare CS instructional practices with other STEM disciplines.
- Inform efforts to reform CS education by increasing the adoption of evidence-based instructional practices.
WILL YOU INTRODUCE US TO YOUR COLLEAGUES?
Best survey practices have shown that an introduction from a trusted colleague increases response rates. This is where you can help! Are you willing to support the CS education community by introducing us to your colleagues and encouraging them to complete the survey?
Please contact Scott Grissom BY FRIDAY APRIL 15 if you are willing to help, and we will provide more information about what we will ask you to do.
SIGCSE Committee on Evidence-based Instructional Practices
– Scott Grissom, Grand Valley State University, firstname.lastname@example.org
– Sue Fitzgerald, Metropolitan State University, email@example.com
– Renée McCauley, College of Charleston, firstname.lastname@example.org
– Laurie Murphy, Pacific Lutheran University, email@example.com