Archive for February, 2017
For Black History Month, the Google K-12 Education Outreach Team has released a 1 sheet brief that focuses exclusively on the K-12 CS experiences of Black students in the U.S. and provides specific recommendations as informed by our Diversity Gaps in Computer Science report.
Computer science (CS) education is critical in preparing students for the future. CS education not only gives students the skills they need across career fields, but it also fosters critical thinking, creativity, and innovation. This summary highlights the state of CS education during 2015–16 for Black students in 7th–12th grade, a group less likely to take the AP Computer Science Exam and with a lower pass rate on it compared to other racial groups.
My colleague, Ashok Goel, is getting a lot of (deserved) attention for exploring the role of a cognitive assistant as a teaching assistant, known as Jill Watson. The question he’s exploring is: How do we measure the effect of this assistant?
One exploration involves engagement. I thought that these numbers were interesting, because they’re comparable to the ones I explored in my information ecology paper in CSCL many years ago. 38 or 32 notes student in a 15 week class is a couple per week. That’s not a dialogue, but it might be more engagement. What should we expect? Could those couple notes per week be suggesting greater learning elsewhere? Is it an indicator?
“We’re seeing more engagement in the course. For instance, in fall of 2015 before Jill Watson, each student averaged 32 comments during the semester. This fall it was close to 38 comments per student, on average,” Goel said. “I attribute this increased involvement partly to our AI TAs. They’re able to respond to inquiries more quickly than us.”
Source: Jill Watson, Round Three
One of my favorite papers is the analysis of Stayers vs Leavers in undergraduate CS by Maureen Biggers and colleagues. This new research published by the CRA explores similar issues.
We also looked at words associated (correlated) with these two sets of words to give us context for frequently cited words. When talking about thoughts about leaving, students were particularly likely to associate “weed-out” with “classes”. They were also likely to use words such as “pretty” and “extremely” alongside “hard” and “difficult”, which sheds light on computing students’ experiences in the major. When talking about staying in their major, students cited words such as “prospect”, “security”, “stable”, and “necessary” along with the top two most commonly used words: “job” and “degree”. For instance, one student said: “[I thought about changing to a non-computing major because of] the difficulty of computing. [But I stayed for] the security of the job market.” Yet another student noted: “The competitive culture [in my computing major] is overwhelming. [But] the salary [that] hopefully awaits me [helped me stay].” Furthermore, students used the words “friends”, “family”, and “support” in association with each other, suggesting that friends and family support played a role in students’ decision/ability to stay in their computing major. As a case in point, one student noted: “The material is hard to learn! I had to drop one of my core classes and must take it again. But with some support from friends, academic advisors, more interesting classes, and a more focused field in the major I have decided to continue.”
There is a sense of vindication that the predictions that many of us made about MOOCs have been proven right, e.g., see this blog post where I explicitly argue (as the article below states) that MOOCs misunderstand the importance of active learning. It’s disappointing that so much effort went wasted. MOOCs do have value, but it’s much more modest than the sales pitch.
What accounts for MOOCs’ modest performance? While the technological solution they devised was novel, most MOOC innovators were unfamiliar with key trends in education. That is, they knew a lot about computers and networks, but they hadn’t really thought through how people learn.
It’s unsurprising then that the first MOOCs merely replicated the standard lecture, an uninspiring teaching style but one with which the computer scientists were most familiar. As the education technology consultant Phil Hill recently observed in the Chronicle of Higher Education, “The big MOOCs mostly employed smooth-functioning but basic video recording of lectures, multiple-choice quizzes, and unruly discussion forums. They were big, but they did not break new ground in pedagogy.”
Indeed, most MOOC founders were unaware that a pedagogical revolution was already under way at the nation’s universities: The traditional lecture was being rejected by many scholars, practitioners, and, most tellingly, tech-savvy students. MOOC advocates also failed to appreciate the existing body of knowledge about learning online, built over the last couple of decades by adventurous faculty who were attracted to online teaching for its innovative potential, such as peer-to-peer learning, virtual teamwork, and interactive exercises. These modes of instruction, known collectively as “active” learning, encourage student engagement, in stark contrast to passive listening in lectures. Indeed, even as the first MOOCs were being unveiled, traditional lectures were on their way out.
Aman Yadav and Steve Cooper have the CACM Viewpoints Education column this month. They raise the questions of how learning computing can lead to greater creativity, and how we can design computing education experiences to draw students in to greater depth.
Computing has the potential to provide users opportunities to extend their creative expression to solve problems, create computational artifacts, and develop new knowledge. The pervasive nature of computing and accessibility of digital tools is also transforming K-12 education as students move from being mere consumers of content to engaging in the subject matter by creating computational artifacts. Take Scratch, for example, which is one of the many tools designed to teach kids to code, and comes with varying levels of support for educators implementing them in both formal and informal learning settings. Scratch provides students with an opportunity to express their creativity through stories, games, and animations. While Scratch has the potential to be a powerful tool, it is often used as little more than a presentation tool in the classroom. Studies of Scratch users show that few projects use variables or control flow data structures. While the Scratch environment provides a ‘low floor, high ceiling’ that allows beginners to dive into the environment without frustration, many students do not advance to a higher level. Tools like Scratch can empower students to showcase their creativity like never before; however, the way these tools are taught by teachers and used by students significantly influences whether students move along the creativity continuum. While Scratch is widely used, we know little about how it influences students’ creative thinking.
Pleased to see that my colleagues are getting recognition for their cool work.
Julie Flapan and Jane Margolis had a piece last month in Education Week saying that the Trump administration should support CS education. Their piece starts with an argument that we should not scapegoat immigrants, and given the recent immigrant ban, seems amazingly prescient.
Julie and Jane point out that CS education is important to the values of the new administration. It’s good to see that the House is re-affirming the importance of STEM education in their new priority statement. We need to make the argument that computing education is not a previous adminstration issue, but is instead about bipartisan issues and values.
Computer science isn’t just about operating a computer or a cellphone. It’s about reimagining how computers are a part of what we do every day. Rather than being passive users of technology, students need to learn how to be responsible creators of it. Computer science teaches algorithmic thinking, problem-solving, and creativity as students learn how to build apps, design a web page, and understand how the internet actually works.
Beyond jobs, this past year revealed other reasons why learning computer science is important in a democracy. Whether it be through thinking critically to distinguish fake news from real news, understanding algorithms that are used to target its users, considering cybersecurity and the role it played in email scandals, or amplifying marginalized voices through social media, we can see the power of technology in our everyday lives. Becoming digitally literate, critical, and constructive thinkers about how to use technology responsibly should be required learning for everyone.
With the uncertainty of President Donald Trump’s education agenda and the future policy decisions under the Every Student Succeeds Act, one thing is clear: We need to continue to support public education and the inclusion of computer science as part of the new law’s call for a “well-rounded education.”
We encourage the new administration to continue to support the former administration’s national agenda to promote computer science for all, which prioritizes the needs of students underrepresented in computer science, including girls, low-income students, and students of color. Many education leaders support this national initiative at the local level.