Posts tagged ‘teachers’
I highly recommend Shuchi Grover’s piece in EdSurge news (linked below). She makes a great point — that the goal of learning computing goes beyond learning to code. It’s not enough to learn to code. She talks about the challenge of learning to code:
There are similar themes in Roy Pea’s 1983 paper with Midian Kurland, “On the cognitive prerequisites of learning computing programming.”
Even among the 25% of the children who were extremely interested in learning programming, the programs they wrote reached but a moderate level of sophistication after a year’s work and approximately 30 hours of on-line programming experience. We found that children’s grasp of fundamental programming concepts such as variables, tests, and recursion, and of specific Logo primitive commands such as REPEAT, was highly context-specific and rote in character. To take one example: A child who had written a procedure using REPEAT which repeatedly printed her name on the screen was unable to recognize the efficiency of using the REPEAT command to draw a square. Instead, the child redundantly wrote the same line-drawing procedure four times in succession.
Coding is hard. Coding has always been hard. We want students to know more than just code about computing.
I’m not sure that Shuchi is right. Maybe learning to code is enough — if it happens. When I studied foreign languages in secondary and post-secondary school (Latin and French for me), there was a great emphasis on learning the culture of a language. There was an explicit belief that learning about the culture of a language facilitated learning the language. Does it go further? Can one learn the language without knowing anything about the culture? If one does learn the language well, did you necessarily learn the culture too? Maybe it works the same for programming languages.
Our human-centered computing PhD students who focus on learning sciences and technologies (LS&T) are required to read two chapters of Noss and Hoyles 1996 book Windows on Mathematical Meanings: Learning Cultures and Computers. They make the argument that you can’t learn Logo well apart from an effective classroom culture. As Pea and Kurland noted in 1983, and Grover has noted thirty years later in 2013, students aren’t really learning programming well.
What if they did? What if students did learn programming? Would they necessarily also learn computing? And isn’t it possible that a culture that taught programming well would also teach things beyond coding? Maybe even problem-solving skills? David Palumbo’s excellent review of the literature on programming and problem-solving pointed out that there was very little link from programming to problem-solving skills — but for the most part, students weren’t learning programming. I don’t really think that that would work, that learning to code would immediately lead to learning problem-solving skills. I do wonder if learning to code might also lead to learning the other things that we think are important about computing.
There is a positive evidence for the value of classroom culture. Consider the work by Leo Porter and Beth Simon, where they found that combining pair programming, peer instruction, and Media Computation led to positive retention and learning (as measured by success in later classes). Porter and Simon have also noted how students learning programming also develop new insight into the applications that they use. Maybe it’s the case that if you change the culture in the classroom and what students do, and maybe students learn programming and computing.
The AP CS readers I know (and I’m married to one) say that we had about 32,000 test takers, a huge increase over the 24,782 from last year. The website linked below (thanks to Gas station without pumps for the link) shows a significant increase in passing grades, too. I’m sure that Barb will do a detailed analysis when the state-by-state and demographic data come out.
Scoring is complete for AP Computer Science. Bravo to these teachers & students: a large increase in 4s/5s over last year.
AP Comp Sci students’ multiple-choice results: on average, students performed best on the logic/software eng/recursion questions, on average, students performed least well on questions about data structures.
AP Comp Sci free-response: similar scores across all 4 questions, slightly higher scores on Q1, slightly lower on Q3: ow.ly/lVKJp
16% more students took AP Computer Science this year, which makes the expanded ratio of 4s and 5s all the more impressive. What teachers!
My former student, Jeff Rick, has posted a reflection on MOOCs (on Facebook, so I can’t easily link to it from here), with an important point:
There’s an additional element that strikes me as critically missing from MOOCs: feedback to the instructor. Teaching is not about throwing good information out into the world; if so, Wikipedia (or public libraries a la Goodwill Hunting) would make formal education unnecessary. It is about making sure that the students get something out of it. For me, that requires a feedback cycle: realizing what problems students have, changing your teaching to meet their needs / interests, realizing and correcting your mistakes, etc.
Peter Norvig has said that he did the first AI MOOC with Sebastian Thrun explicitly to get more feedback. He was working on a revision for his AI textbook, and he didn’t want to just build it again and throw it into the world. By offering the book/course as a MOOC, he was able to get fine-grained data from many students on how they were using his book.
Teachers offering courses via Coursera or Udacity today get quite little data. The data is all captured behind corporate walls. I talked to Tucker Balch about the data he was gathering from his Coursera course “Computational Investing.” He said that he had the right to survey his students, but Coursera didn’t share any data that they had on the students. He got data on numbers of unique registrants, percent that took the first homework, percent that completed, etc. But nothing about how students did on particular problems, or how long they spent reviewing any particular video. No data that would help you figure out, “Hmm, I don’t think that’s working for the students.”
Isn’t that surprising, that in era of “Big Data,” MOOCs would be about “little data” getting back to the teacher who can most easily improve the course?
Barbara Ericson just found out that several teachers have dropped out from a professional development workshop that we’re offering next week. This means that we have some (limited) funding for travel available, and hotel rooms already booked, so we’re trying to get the word out broadly to fill those (very last minute) slots. Below is the message that she sent to teachers in Georgia. We’ll take teachers from other states as well.
The workshop is on CS Principles Big Ideas from June 17-21st at Georgia Tech. Rebecca Dovi is leading this workshop. She is one of the CS:Principles pilot teachers. She has created many interesting activities for teaching CS Principles and will be sharing those activities. See http://supercomputerscience.blogspot.com for her blog.
We still have hotel rooms available for attendees. We pay for parking and lunch for all attendees. We have limited funds to reimburse for travel as well. You can register at http://www.surveymonkey.com/s/CSP2013-BigIdeas
For more information on the workshop, see http://coweb.cc.gatech.edu/ice-gt/2175
I don’t know how I missed this! I just watched the opening preview video, and it looks really cool. I don’t have the time to join in right now, but encourage others to check it out.
Creative computing is about creativity. Computer science and computing-related fields have long been perceived as being disconnected from young people’s interests and values. Creative computing supports the development of personal connections to computing, by drawing upon creativity, imagination, and interests.
Creative computing is about computing. Many young people with access to computers participate as consumers, rather than designers or creators. Creative computing emphasizes the knowledge and practices that young people need to create the types of dynamic and interactive computational media that they enjoy in their daily lives.
Engaging in the creation of computational artifacts prepares young people for more than careers as computer scientists or as programmers. It supports young people’s development as computational thinkers – individuals who can draw on computational concepts, practices, and perspectives in all aspects of their lives, across disciplines and contexts.
This caught my eye as something that we really need to push computing education. For CS10K to be successful, we need a mesh of education research with public policy work. That’s what ECEP is about. In particular, this kind of multiple stakeholders work is what I think that the U. Chicago Landscape Study is pointing toward.
“Design-Based Implementation Research applies design-based perspectives and methods to address and study problems of implementation…DBIR challenges education researchers to break down barriers between sub-disciplines of educational research that isolate those who design and study innovations within classrooms from those who study the diffusion of innovations.”
From the Introduction to the forthcoming NSSE Yearbook, Design-Based Implementation Research: Theories, methods, and exemplars.
This web site presents resources related to an emerging model of research and development called Design-Based Implementation Research (DBIR). DBIR has four key principles:
- a focus on persistent problems of practice from multiple stakeholders’ perspectives
- a commitment to iterative, collaborative design
- a concern with developing theory related to both classroom learning and implementation through systematic inquiry
- a concern with developing capacity for sustaining change in systems
How would one measure extraordinary, innovative teaching? We have a difficult time measuring regular teaching!
The Minerva Project, a San Francisco venture with lofty but untested plans to redefine higher education, said on Monday that starting next year it would award an annual $500,000 prize to a faculty member at any institution in the world who has demonstrated extraordinary, innovative teaching.
I gave the last GVU Brown Bag seminar of the academic year. Video is available at the link below.
Speaker: Mark Guzdial
Title: What We Know About Teaching Computer Science (“What does Guzdial do, Anyway?”)
We have known for over 30 years that learning to program is surprisingly hard. A series of international studies have shown remarkably little success in teaching programming. In my group, we have been developing approaches to improve learning about computing, by improving retention through relevance and by teaching in problem domain context. Our classes and studies have utilized computer-supported collaborative learning, so we explore learning on-line as well as in-classroom. We have learned how anchored collaboration can lead to longer on-topic discussions, but how perceptions of course culture can dramatically inhibit discussion. We have shown that well-designed on-line activities can lead to better learning at reduced cost (including time costs for the student and instructor). We are currently developing an ebook for learning computer science by high school teachers where we are trying to integrate these lessons for a new audience.
I’ve written before about computer science pedagogical content knowledge (PCK). Phil Sadler and his colleagues just published a wonderful study about the value of PCK. He found that science teachers need to know science, but the most effective science teachers also know what students get wrong — their misconceptions, what the learning difficulties are, and what are the symptoms of misunderstandings. I got a chance to ask him about this paper, and he said one of the implications of the work that he sees is that he offers a way to measure PCK, and measuring something important about teaching is hard and useful.
For the study described in their paper, Sadler and his colleagues asked teachers to answer each question twice, once to give the scientifically correct answer, and the second time to predict which wrong answer their students were likeliest to choose. Students were then given the tests three times throughout the year to determine whether their knowledge improved.
The results showed that students’ scores showed the most improvement when teachers were able to predict their students’ wrong answers.
“Nobody has quite used test questions before in this way,” Sadler said. “What I had noticed, even before we did this study, was that the most amazing science teachers actually know what their students’ wrong ideas are. It occurred to us that there might be a way to measure this kind of teacher knowledge easily without needing to spend long periods of time observing teachers in their classrooms.”
Hot topic these days, like the debate in the UK. Workshop to be held in conjunction with ASEE in Atlanta June 26-28.
A primary objective of undergraduate computing and engineering programs is to prepare graduates for professional practice. New graduates often find themselves working on large, complex systems that require dozens (or hundreds) of people and months (or years) to complete. Unfortunately, graduates often feel ill-prepared to work on systems of such size and complexity. Educators find it extremely difficult to provide a realistic experience with such systems in an academic environment.
Engineering and computing curricula primarily rely on a senior design course (one or two semesters in length) to teach professional practice. Students are typically organized in project teams to develop a realistic product or service, in which the students engage in various professional practices: such as project management, requirements analysis and modeling, highlevel and detailed design, implementation or simulation, quality assurance, project reporting, and use of appropriate engineering tools and methods.
Posted to the SIGCSE-Members list from Moti Ben Ari:
Michal Armoni and I have written a book: “Computer Science Concepts in Scratch”. (See the short description below.) It can be freely downloaded from http://stwww.weizmann.ac.il/g-cs/scratch/scratch_en.html under the Creative Common BY-NC-ND license.
The book is based on Scratch 1.4 … although Scratch 2.0 is due to be released in a few days. We are planning to prepare a supplement and / or revision for 2.0 in the future.
We’ve set up a separate email account for correspondence related to the book: email@example.com.
Moti and Michal
Prof. Mordechai (Moti) Ben-Ari
Department of Science Teaching
Weizmann Institute of Science
Interesting — HP is offering a MOOC for “STEMx” teachers (below), and Google is offering CS teacher fellowships. Nice to see the companies stepping up. I’m not convinced that MOOCs are the best way to reach teachers, but a bigger question is how many teachers will identify with the term “STEMx.” We have seen that teacher identity drives teacher’s pursuit of professional development. Will they see themselves in this term?
Coined by the HP Catalyst Initiative, STEMx covers not only science, technology, engineering and math, but also other high-technology disciplines such as computer science, nanoscience and biotech. The modified acronym also refers to the skills of collaboration, creativity, communication, problem solving, inquiry, computational thinking and “global fluency.”
The MOOC was announced by HP’s education partners, the International Society for Technology in Education (ISTE) and the New Media Consortium (NMC), during the 2013 HP Catalyst Summit in Sao Paulo, Brazil. The meeting attracted more than 120 educators and policy leaders.
This is a compelling vision. Set aside MOOCs or not — how could we use a team-based approach in building postsecondary education, so that we have the best of texts, tools, in-class experiences, videos, and individualized tutoring and advising? If we want higher-quality, we can’t expect one teacher to perform all roles for increasing numbers of students.
The real threat to traditional higher education embraces a more radical vision that removes faculty from the organizational center and uses cognitive science to organize the learning around the learner. Such models exist now.
Consider, for example the implications of Carnegie Mellon’s Open Learning Initiative. More than 10 years ago, Herb Simon, the Carnegie Mellon University professor and Nobel laureate, declared, “Improvement in postsecondary education will require converting teaching from a solo sport to a community-based research activity.” The Open Learning Initiative (OLI) is an outgrowth of that vision and has been striving to realize it for more than a decade.
At first, Google contacted us to find existing CS teachers to be part of their new teaching fellows program, but they’ve just opened it up to new grads as well.
Google is searching for talented (STEM) Science, Technology, Engineering or Math teachers to join a 2-year post-graduate program designed to grow leaders in computer science education. The program targets new graduates passionate about the future of computer science education. Applications are being accepted on a rolling basis for a two-year program that begins in June 2013. Applicants must be able to commit to the entire two years. As a part of the practicum, you will be working with thought leaders in education to learn the newest techniques and programs for computer science pedagogy, implementing programs with area schools and students, and creating your own innovative approaches to student learning. You can apply for the position and find more details about the program on this website. Please direct any questions you might have to TeachCS@google.com.
The role: Computer Science Teaching Fellows, New Grad 2013
• Bachelor’s degree in computer science or related field
• Some form of teaching or instruction experience (e.g., teaching assistant, tutor)
• Able to commit to a 2-year program and start June 2013
• Willing to relocate to/within South Carolina
I really liked this post, in part because of how differently it is being interpreted within my department. I posted it on a school-wide discussion list, to emphasize the value of what we do that cannot be automated. However, my MOOC-favoring colleagues read this post in exactly the opposite way to how I interpreted it. “Anyone can do this kind of grading, so we shouldn’t waste our time at it! Instead, we should abandon all courses that require this kind of grading.” What can’t be automated isn’t worth doing?
I know that a lot of MOOC-proponents are pushing automatic grading of papers as a cost-effective way to handle classes with over 1000 students. Quite frankly, the idea appalls me—I can’t see any way that computer programs could provide anything like useful feedback to students on any sort of writing above the 1st-grade level. Even spelling checkers (which I insist on students using) do a terrible job, and what passes for grammar checking is ludicrous nonsense. And spelling and grammar are just the minor surface problems, where the computer has some hope of providing non-negative advice. But the feedback I’m providing covers lots of other things like the structure of the document, audience assessment, ordering of ideas, flow of sentences within a paragraph, proper topic sentences, design of graphical representation of data, feedback on citations, even suggestions on experiments to try—none of which would be remotely feasible with the very best of artificial intelligence available in the next 10 years.