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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?
If states offer career and technical education in pathways (typically 3-4 courses) with a pathway completion exam, they are eligible for Perkins legislation funding to pay for staff and equipment. If AP CS is one of those courses, it’s easier to build the pathway (2-3 courses to define, rather than 3-4) and the pathway is more likely to lead to college-level CS, if a student so chooses. But as the below report mentions, many states believe that Perkins legislation disallows the AP to count. It can, and here’s the report describing how.
If you’re hearing this story in your state, be sure to send your department of education this report!
Career and Technical Education and Advanced Placement (July 2013, PDF)
Traditionally Advanced Placement® (AP) courses and exams have not been recommended for students in Career Technical Education (CTE) programs. This paper, jointly developed and released by NASDCTEc and the College Board aims to bust this myth by showing how AP courses and exams can be relevant to a student’s program of study across the 16 Career Clusters®.
I hadn’t heard about this theory before the below blog post — recommended reading. As usual, I appreciate Kevin’s analysis.
As parents and teachers we encourage children to pursue fields that they enjoy, that they are good at, and that can support them later in life. It may be that girls are getting the “that they are good at” message more strongly than boys are, or that enjoyment is more related to grades for girls. These habits of thought can become firmly set by the time students become men and women in college, so minor setbacks (like getting a B in an intro CS course) may have a larger effect on women than on men. I’m a little wary of putting too much faith in this theory, though, as the author exhibits some naiveté.
The story is interesting and disappointing. Why would GitHub go through all these contortions just because they had this one female engineer — and would have there been less drama and stress if there had been more than just one female engineer? The story has been updated in Sunday’s NYTimes.
The exit of engineer Julie Ann Horvath from programming network GitHub has sparked yet another conversation concerning women in technology and startups. Her claims that she faced a sexist internal culture at GitHub came as a surprise to some, given her former defense of the startup and her internal work at the company to promote women in technology.
In her initial tweets on her departure, Horvath did not provide extensive clarity on why she left the highly valued startup, or who created the conditions that led to her leaving and publicly repudiating the company.
Horvath has given TechCrunch her version of the events, a story that contains serious allegations towards GitHub, its internal policies, and its culture. The situation has greater import than a single person’s struggle: Horvath’s story is a tale of what many underrepresented groups feel and experience in the tech sector.
Hackathons seem the antithesis of what we want to promote about computer science. On the one hand, they emphasize the Geek stereotype (it’s all about caffeine and who needs showers?), so they don’t help to attract the students who aren’t interested in being labeled “geeky.” On the other hand, it’s completely against the idea of designing and engineering software. “Sure, you can do something important by working for 36 hours straight with no sleep or design! That’s how good software ought to be written!” It’s not good when facing the public (thinking about the Geek image) or when facing industry and academia.
So why try to make them “female-friendly”?
OK, so there are a number of valid reasons women tend to stay away from hackathons. But what can hackathon planners due to get more females to attend their events? I found some women offering advice on this subject. Here are some suggestions for making your hackathon more female-friendly.
Amy Quispe, who works at Google and ran hackathons while a student at Carnegie Mellon University, writes that having a pre-registration period just for women makes them feel more explicitly welcome at your event. Also, shy away from announcing that its a competition (to reduce the intimidation factor), make sure the atmosphere is clean and not “grungy” and make it easy for people to ask questions. “A better hackathon for women was a better hackathon for everyone,” she writes.
I recently watched the documentary Why we fight, and was struck by the prescience of President Eisenhower’s warning. So many of our educational decisions are made because of the harsh economic realities of today. How many of these are guns-for-butter choices might we have made differently if education was considered? Here in Georgia, computer science curricular decisions are being made with a recognition that there will be little or no funding available for teacher professional development — certainly not enough for every high school CS teacher in the state. What percentage of the DoD budget would it cost to provide professional learning opportunities to every CS teacher in the country? It’s certainly in the single digits.
Every gun that is made, every warship launched, every rocket fired signifies, in the final sense, a theft from those who hunger and are not fed, those who are cold and are not clothed.
This world in arms in not spending money alone.
It is spending the sweat of its laborers, the genius of its scientists, the hopes of its children.
The cost of one modern heavy bomber is this: a modern brick school in more than 30 cities.
It is two electric power plants, each serving a town of 60,000 population.
It is two fine, fully equipped hospitals.
It is some 50 miles of concrete highway.
We pay for a single fighter with a half million bushels of wheat.
We pay for a single destroyer with new homes that could have housed more than 8,000 people.
This, I repeat, is the best way of life to be found on the road the world has been taking.
This is not a way of life at all, in any true sense. Under the cloud of threatening war, it is humanity hanging from a cross of iron.
via Cross of Iron Speech.
The report on the CCC’s workshop on MOOCs and other online education technologies is now out.
In February 2013 the Computing Community Consortium (CCC) sponsored the Workshop on Multidisciplinary Research for Online Education (MROE). This visioning activity explored the research opportunities at the intersection of the learning sciences, and the many areas of computing, to include human-computer interactions, social computing, artificial intelligence, machine learning, and modeling and simulation.
The workshop was motivated and informed by high profile activities in massive, open, online education (MOOE). Point values of “massive” and “open” are extreme values that make explicit, in ways not fully appreciated previously, variability along multiple dimensions of scale and openness.
The report for MROE has been recently completed and is online. It summarizes the workshop activities and format, and synthesizes across these activities, elaborating on 4 recurring themes:
- Next Generation MOOCs and Beyond MOOCs
- Evolving Roles and Support for Instructors
- Characteristics of Online and Physical Modalities
- Physical and Virtual Community
Andy Ko made a fascinating claim recently, “Programming languages are the least usable, but most powerful human-computer interfaces ever invented” which he explained in a blog post. It’s a great argument, and I followed it up with a Blog@CACM post, “Programming languages are the most powerful, and least usable and learnable user interfaces.”
How would we make them better? I suggest at the end of the Blog@CACM post that the answer is to follow the HCI dictum, “Know thy users, for they are not you.“
We make programming languages today driven by theory — we aim to provide access to Turing/Von Neumann machines with a notation that has various features, e.g., type safety, security, provability, and so on. Usability is one of the goals, but typically, in a theoretical sense. Quorum is the only programming language that I know of that tested usability as part of the design process.
But what if we took Andy Ko’s argument seriously? What if we designed programming languages like we defined good user interfaces — working with specific users on their tasks? Value would become more obvious. It would be more easily adopted by a community. The languages might not be anything that the existing software development community even likes — I’ve noted before that the LiveCoders seem to really like Lisp-like languages, and as we all know, Lisp is dead.
What would our design process be? How much more usable and learnable could our programming languages become? How much easier would computing education be if the languages were more usable and learnable? I’d love it if programming language designers could put me out of a job.
Thought-provoking piece on NPR. Take parents who believe that the MMR vaccine causes autism. Show them the evidence that that’s not true. They might tell you that they believe you — but they become even less likely to vaccinate future children. What?!?
The explanation (quoted below) is that these parents found a sense of identity in their role as vaccine-deniers. They rejected the evidence at a deeply personal level, even if they cognitively seemed to buy it.
I wonder if this explains a phenomenon I’ve seen several times in CS education: teaching with a non-traditional but pedagogically-useful tool leads to rejection because it’s not the authentic/accepted tool. I saw it as an issue of students being legitimate peripheral participants in a community of practice. Identity conflict offers a different explanation for why students (especially the most experienced) reject Scheme in CS1, or the use of IDE’s other than Eclipse, or even CS teacher reaction when asked not to use the UNIX command line. It’s a rejection of their identity.
An example: I used to teach object-oriented programming and user interface software using Squeak. I had empirical evidence that it really worked well for student learning. But students hated it – especially the students who knew something about OOP and UI software. “Why aren’t we using a real language? Real OOP practitioners use Java or C++!” I could point to Alan Kay’s quote, “I invented the term Object-Oriented, and I can tell you I did not have C++ in mind.” That didn’t squelch their anger and outrage. I’ve always interpreted their reaction to the perceived inauthenticity of Squeak — it’s not what the majority of programmers used. But I now wonder if it’s about a rejection of an identity. Students might be thinking, “I already know more about OOP than this bozo of a teacher! This is who I am! And I know that you use Java or C++!” Even showing them evidence that Squeak was more OOP, or that it could do anything they could do in Java or C++ (and some things that they couldn’t do in Java or C++) didn’t matter. I was telling them facts, and they were arguing about identity.
What Nyhan seems to be finding is that when you’re confronted by information that you don’t like, at a certain level you accept that the information might be true, but it damages your sense of self-esteem. It damages something about your identity. And so what you do is you fight back against the new information. You try and martial other kinds of information that would counter the new information coming in. In the political realm, Nyhan is exploring the possibility that if you boost people’s self-esteem before you give them this disconfirming information, it might help them take in the new information because they don’t feel as threatened as they might have been otherwise.