I don’t believe the main propositions of the article below. Not all STEM education will lead to more women discovering an interest in IT. Putting computing as a mandatory subject in all schools will not necessarily improve motivation and engagement in CS, and it’s a long stretch to say that that will lead to more people in IT jobs.
I addressed the quote below, by Ashley Gavin, in my Blog@CACM post for this month: The Danger of Requiring CS in US K-12 Schools.
“You make it an option, the girl is not going to take it. You have to make it mandatory and start it at a young age,” says Ashley Gavin, curriculum director at Girls Who Code, a nonprofit working to expose more girls to computer science at a young age that has drawn support from leading tech firms such as Google, Microsoft and Intel.
“It’s important to start early because, most of the fields that people go into, they have exposure before they get to college. We all study English before we get to college, we all study history and … social studies before we get to college,” Gavin says. “No one has any idea what computer science is. By the time you get to college, you develop fear of things you don’t know. Therefore early exposure is really important.”
Susan H. Rodger, recipient of the Karl V. Karlstrom Outstanding Educator Award for contributions to the teaching of computer science theory in higher education, and the development of computer science education in primary and secondary schools. She and her students developed JFLAP (Java Formal Languages and Automata Package), an interactive software tool that allows students to construct and test examples of automata and grammars. These concepts are foundational to the design of software components, such as compiler parts. Intended primarily for undergraduate students or as an advanced topic for high school, JFLAP is used worldwide in computer science theory, compiler, and discrete mathematics courses. Through workshops for faculty development, Rodger’s work contributed to the creation of a professional community around the use of visualizations to teach algorithms. She also leads efforts to introduce the programming language Alice in primary and secondary schools. Rodger is a professor of the practice of computer science at Duke University. Currently chair of the ACM Special Interest Group on Computer Science Education (SIGCSE), she is a board member of CRA-W and a member of the ACM Education Policy Committee. The Karlstrom Award recognizes educators who advanced new teaching methodologies; effected new curriculum development in Computer Science and Engineering; or contributed to ACM’s educational mission.
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.”