My colleague, Amy Bruckman, considers in her blog how HCI design principles lead us to question whether MOOCs can achieve their goals.
Can a MOOC teach course content to anyone, anywhere? It’s an imagination-grabbing idea. Maybe everyone could learn about topics from the greatest teachers in the world! Create the class once, and millions could learn from it!
It seems like an exciting idea. Until you realize that the entire history of human-computer interaction is about showing us that one size doesn’t fit all.
The California state legislature is attempting to affect change to computer science education in California, and for all the right reasons. They’re getting the message that computer science is what drives innovation and economic growth in California, and that the demand for computer science graduates in California far exceeds supply. There are simply not enough students prepared or preparing to join this high tech workforce. They’re also starting to understand that computer science needs to count for something other than an elective course for more schools to offer it and for more students to take it – especially girls and underrepresented students of color. What they may not quite understand yet is that there aren’t enough teachers prepared to teach computer science in K-12, although one assemblyman spoke of the need for a single subject teaching credential in computer science, so maybe someday we’ll get there … baby steps!
So, it was exciting in Sacramento last week as the Assembly and Senate Education Committees passed a handful of CS-related bills with flying colors and broad bi-partisan support! ACCESS (the Alliance for California Computing Education in Students and Schools) was on hand to help provide analysis and information. Many thanks to Josh Paley, a computer science teacher at Gunn High School in Palo Alto and a CSTA advocacy and leadership team member, who provided substantive testimony on two priority bills*. Josh provided compelling stories of students who had graduated and gone on to solve important problems using their CS skills. Amy Hirotaka, State Policy and Advocacy Manager, of Code.org, Andrea Deveau, Executive Director of TechNet, and Barry Brokaw, lobbyist for Microsoft also testified on these bills. It was also exciting to see a wide range of organizations supporting this important discipline.
All of the following CS-related bills passed out of committee, all but one with unanimous approval:
1) AB 1764* (Olsen and Buchanan) would allow school districts to award students credit for one mathematics course if they successfully complete one course in computer science approved by the University of California as a “category c” (math) requirement for admissions. Such credit would only be offered in districts where the school district requires more than two courses in mathematics for graduation, therefore, it does not replace core math requirements.
2) AB 1539* (Hagman) would create computer science standards that provide guidance for teaching computer science in grades 7-12.
3) AB 1540 (Hagman) establishes greater access to concurrent enrollment in community college computer science courses by high school students.
4) AB 1940 (Holden) establishes a pilot grant program to support establishing or expanding AP curriculum in STEM (including computer science) in high schools with such need (passed with two noes).
5) AB 2110 (Ting) requires computer science curriculum content to be incorporated into curriculum frameworks when next revised.
6) SB1200 (Padilla) would require CSU and request UC to establish a uniform set of academic standards for high school computer science courses, to satisfy the “a-g” subject requirements, as defined, for the area of mathematics (“c”) for purposes of recognition for undergraduate admission at their respective institutions.
7) ACR 108 (Wagner) would designate the week of December 8, 2014, as Computer Science Education Week (passed on consent).
AB 1530 (Chau), to be heard by the Assembly Education Committee on April 23, would encourage the Superintendent of Public Instruction to develop or, as needed, revise a model curriculum on computer science, and to submit the model curriculum to the State Board of Education for adoption (specifically focuses on grades 1-6).
Anyone really interested in hearing the bill presentation, testimony and supporters can see it here:
Senate Education Committee: http://calchannel.granicus.com/MediaPlayer.php?view_id=7&clip_id=2012
Assembly Education Committee: http://calchannel.granicus.com/MediaPlayer.php?view_id=7&clip_id=2019
I’ll plan another update once these bills move further.
Really interesting idea — Code.org’s Pat Yongpradit sent a note to all of CSTA, asking CS teachers to help provide hints for Code.org tutorials. By reaching out to CSTA, they’re doing better than crowd-sourcing. They’re CS-teacher-sourcing.
We’ve had millions of students try the Code.org tutorials. They’ve submitted over 11 million unique computer programs as solutions to roughly 100 puzzles.
We’ve mapped out which submissions are errors (ie they don’t solve the puzzle), and which are sub-optimal solutions (they solve the puzzle, but not efficiently).
Today, erroneous user submissions receive really unhelpful error feedback, such as “You’re using the right blocks, but not in the right way”. We want your help improving this, by providing highly personal feedback to very specific student errors. Watch the video below to see what we mean.
Important article that gets at some of my concerns about using MOOCs to inform education research. The sampling bias mentioned in the article below is one of my responses to the claim that we can inform education research by analyzing the results of MOOCs. We can only learn from the data of participants. If 90% of the students go away, we can’t learn about them. Making claims about computing education based on the 10% who complete a CS MOOC (and mostly white/Asian, male, wealthy, and well-educated at that) is bad science.
Cheerleaders for big data have made four exciting claims, each one reflected in the success of Google Flu Trends: that data analysis produces uncannily accurate results; that every single data point can be captured, making old statistical sampling techniques obsolete; that it is passé to fret about what causes what, because statistical correlation tells us what we need to know; and that scientific or statistical models aren’t needed because, to quote “The End of Theory”, a provocative essay published in Wired in 2008, “with enough data, the numbers speak for themselves”.
Unfortunately, these four articles of faith are at best optimistic oversimplifications. At worst, according to David Spiegelhalter, Winton Professor of the Public Understanding of Risk at Cambridge university, they can be “complete bollocks. Absolute nonsense.”
Last month, Steve Cooper organized a remarkable workshop at Stanford on the Future of Computing Education Research. The question was, “How do we grow computing education research in the United States?” We pretty quickly agreed that we have a labor shortage — there are too few people doing computing education research in the US. We need more. In particular, we need more CS Ed PhD students. The PhD students do the new and exciting research. They bring energy and enthusiasm into a field.
We also need these students to fit into Computing departments, where that could be Computer Science, or Informatics, or Information Systems/Technology/Just-Information Departments/Schools/Colleges. Yes, we need a presence in Education Schools at some point, to influence how we develop new teachers, but that’s not how we’ll best push the research.
How do we get there?
Roy Pea came to the event. He could only spare a few hours for us, and he only gave a brief 10 minute talk, but it was one of the highlights of the two days for me. He encouraged us to think about Learning Sciences as a model. Learning Science grew out of cognitive science and computer science. It’s a field that CS folks recognize and value. It’s not the same as Education, and that’s a positive thing for our identity. He told us that the field must grow within Computing departments because Domain Matters. The representations, the practices, the abstractions, the mental models — they all differ between domains. If we want to understand the learning of computing, we have to study it from within computing.
I asked Roy, “But how do we influence teacher education? I don’t see learning science classes in most pre-service teacher development programs.” He pointed out that I was thinking about it all wrong. (Not his words — he was more polite than that.) He described how learning sciences has influenced teacher development, integrated into it. It’s not about a separate course: “Learning science for teachers.” It’s about changing the perspective in the existing classes.
Ken Hay, a learning scientist (and long-time friend and colleague) who is at Indiana University, echoed Roy’s recommendation to draw on the learning sciences as a model. He pointed out that Language Matters. He said that when Indiana tried to hire a “CS Education Researcher,” faculty in the CS department said, “I teach CS. I’m a CS Educator. How is s/he different than me?”
We started talking about how “Computer Science Education Research” is a dead-end name for the research that we want to situate in computing departments. It’s the right name for the umbrella set of issues and challenges with growing computing education in the United States. It includes issues like teacher professional development and K-12 curricula. But that’s not what’s going to succeed in computing departments. It’s the part that looks like the learning sciences that can find a home in computing departments. Susanne Hambrusch of Purdue offered a thought experiment that brought it home for me. Imagine that there is a CS department that has CS Ed Research as a research area. They want to list it on their Research web page. Well, drop the word “Research” — this is the Research web page, so that’s a given. And drop the “CS” because this is the CS department, after all. So all you list is “Education.” That conveys a set of meanings that don’t necessarily belong in a CS department and don’t obviously connect to our research questions.
In particular, we want to separate (a) the research about how people learn and practice computing from (b) making teaching and learning occur better in a computing department. (a) can lead to (b), but you don’t want to demand that all (a) inform (b). We need to make the research on learning and practice in computing be a value for computing departments, a differentiator. “We’re not just a CS department. We embrace the human side and engage in social and learning science research.” Lots of schools offer outreach, and some are getting involved in professional development. But to do those things informed by learning sciences and informing learning sciences (e.g., can get published in ICER and ICLS and JLS and AERA) — that’s what we want to encourage and promote.
I was in a breakout that tried to generate names. Michael Horn of Northwestern came up with several of my favorites. Unfortunately, none of them were particularly catchy:
- Learning Sciences of Computing
- Learning Sciences for Computing
- Computational Learning and Practice (sounds too much like machine learning)
- Learning Sciences in Computing Contexts
- Learning and Practice in Computing
- Computational Learning and Literacy
We do have a name for a journal picked out that I really like: Journal of Computational Thinking and Learning.
I’d appreciate your thoughts on these. What would be a good name for the field which studies how people learn computing, how to improve that learning, how professionals practice computing (e.g., end-user programming, computational science & engineering), and how to help novices join those professional communities of practice?
I can’t remember the last time I learned so much and had my preconceived notions so challenged in just two days. I have a lot more notes on the workshop, and they may make it into some future blog posts. Kudos to Steve for organizing an excellent workshop, and my thanks to all the participants!
Yup, Herminia has the problem right — if CS MOOCs are even more white and male than our face-to-face CS classes, and if hiring starts to rely on big data from MOOCs, we become even less diverse.
But that’s just the tip of the iceberg. One of the developments that will undoubtedly cement the relationship between big data and talent processes is the rise of massive open online courses, or MOOCs. Business schools are jumping into them whole hog. Soon, your MOOC performance will be sold to online recruiters taking advantage of the kinds of information that big data allows—fine distinctions not only on content assimilation but also participation, contribution to, and status within associated online communities. But what if these new possibilities—used by recruiters and managers to efficiently and objectively get the best talent—only bake in current inequities? Or create new ones?