Posts tagged ‘active learning’
I decided to use clickers in CS1315 this semester (n=217), rather than use the colored index cards that I’ve used in the past for Peer Instruction (see blog post here). With cards, I can only take a vote — no histogram of results, and I can’t provide any grade value for the participation. With clickers, I can use the evidence-based practice as developed by Eric Mazur, Cynthia Lee, Beth Simon, Leo Porter, et al. (plugging the Peer Instruction for CS website):
- Ask everyone to answer to prime their thinking about the question,
- ask students to discuss the question in groups of 2-3,
- then vote again (consensus within groups), and
- show the results and discuss the misconceptions.
To make it worthwhile, I’m giving 10 points of final course grade for scoring over 50% on the second question (only — first one is just to get predictions and activate knowledge), 5 points for scoring over 30%.
I’m trying to do this all with campus-approved standards: TurningPoint clickers, TurningPoint software. I’d love to use an app-based solution, but our campus Office of Information Technologies warns against it. They can’t guarantee that, in large classes, the network will support all the traffic for everyone to vote at once.
The process is so complicated: Turn on clickers in our learning management software (a form of Sakai called T-Square), download the participant list, open up ResponseWare and define a session (for those using the app version), plug in receiver. After class, save the session, integrate the session with the participant list, then integrate the results with T-Square for grades. The default question-creation process in TurningPoint software automatically shows results and demands a specific format (e.g., which makes it hard to show screenshots as part of a question), so I’m using “Poll Anywhere” option, which requires me to process the session file after class to delete the first question (where everyone votes to prime their thinking) and to define the correct response(s) for each question.
I’m willing to do all that. But it’s more complicated than that.
Turns out that Georgia Tech hasn’t upgraded to the latest version of the TurningPoint software (TurningPoint Cloud). GT only supports TurningPoint 5. TurningPoint stopped distributing that version of the software in May 2016, so you have to get it directly from the on-campus Center for Teaching and Learning. I got the software and installed it — and discovered that it doesn’t run on the current version of MacOS, Sierra.
I did find a solution. Here’s what I do. Before each lecture, I move my lecture slides to a network drive. When I get to class, I load my lecture on the lecture/podium computer (which runs Windows and TurningPoint 5 and has a receiver built-in). I gather all the session data while I teach with the podium computer and do live coding on my computer (two screens in the massive lecture hall). I save the session data back to the network drive. Back in my office, I use an older Mac that still runs an older version of MacOS to download the session data, import it using TurningPoint 5, do all the deletions of priming questions and correct-marking of other questions, then integrate and upload to T-Square.
Counting my laptop where I make up slides and do live coding, my Peer Instruction classes require three computers.
Every CS teacher should use active learning methodologies in our classes. Our classes are huge. We need better and easier mechanisms to make this work.
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.
Higher education should be about more than lectures: What students do is more important than what they hear
I was reminded of this work by Ken Koedinger in a recent faculty meeting focusing on Georgia Tech’s future strategic directions in education. We got to considering the quality of different courses, and the analysis centered around “materials” (e.g., slides, textbook), “lectures” (the dynamic presentation of the materials), and “assessment” (e.g., exams and homework). That feels like the wrong set of categories to me. The most important category is “What students do to learn.” “Lectures” simply aren’t an important part of student learning.
So it was difficult for me to open my mind to fresh data analysis, from Professor Ken Koedinger of Carnegie Mellon University, which adds more weight to the argument that lectures aren’t an effective way to learn, despite our nostalgic memories of enjoying them. Koedinger didn’t study live lectures, but recorded ones that were part of a free online psychology class produced by the Georgia Institute of Technology. He and a team of four Carnegie Mellon researchers mined the data from almost 28,000 students who took the course over the Coursera platform for Massive Open Online Courses (MOOCs).
They found that video lecturers were the least effective way to learn. Students who primarily learned through watching video lectures did the worst both on the 11 quizzes during the 12-week course and on the final exam. Students who primarily learned through reading, or a combination of reading and video lectures, did a bit better, but not much.
The students who did the best were those who clicked on interactive exercises. For example, one exercise asked students to click and drag personality factors to their corresponding psychological traits. A student would need to drag “neuroticism” to the same line with “calm” and “worrying,” in this case. Hints popped up when a student guessed wrong.
White House Call to Action: Incorporating Active STEM Learning Strategies into K-12 and Higher Education
I’m so happy to see this! I’ve received significant pushback on adopting active learning among CS faculty. Maybe a White House call can convince CS higher education faculty to adopt active learning strategies?
Active learning strategies include experiences such as:
- Authentic scientific research or engineering or software design in the classroom to help students understand the practice of science, technology, and engineering and promote deep learning of the subject matter;
- Interactive computer activities to support students’ exposure to trial-and-error and promote deep learning;Discussions to encourage collaboration and idea exchange among students; and
- Writing to generate original ideas and solidify knowledge.
Today, the White House Office of Science and Technology Policy is issuing a call to action to educators in K-12 and higher education, professional development providers, non-profit organizations, Federal agencies, private industry, and members of the public to participate in a nationwide effort to meet the goals of STEM for All through the use of active learning at all grade levels and in higher education.
Annie Murphy Paul is talking about inclusive teaching here, but she could just as well be talking about active learning. The stages are similar (recall the responses to my proposal to build active learning methods into hiring, promotion, and tenure packages). These are particularly critical for computing where we have so little diversity and CS teachers are typically poor at teaching for diverse audiences.
Stages of Inclusive Teaching Acceptance
Denial: “I treat all my students the same. I don’t see race/ethnicity/gender/sexual orientation/nationality/disability. They are just people.”
Anger: “This is all just social science nonsense! Why won’t everyone just get over this PC stuff? When I went to grad school, we never worried about diversity.”
Bargaining: “If I make one change in my syllabus, will you leave me alone?”
Depression: “Maybe I’m not cut out to teach undergraduates. They’re so different now. Maybe I just don’t understand.”
Overwhelmed: “There is so much I didn’t know about teaching, learning, and diversity. How can I possibly accommodate for every kind of student?”
Acceptance: “I realize that who my students are and who I am influences how we interact with STEM. I can make changes that will help students learn better and make them want to be part of our community.”
Because of the kind of work that we do in my group at Georgia Tech, we visit a lot of computer science classrooms, recitations, and labs. Sometimes what we see is counter to what we now know is effective. Here are two examples from this semester:
- We see small group recitations, where students sit for 90 minutes and passively listen to a recap of the lecture. No peer instruction. We know active learning is better, and we know that it’s even easier to do active learning in small groups.
- We see intro courses teaching recursion before iteration. One of the few replicated results in CS Ed is that iteration should precede recursion. John Anderson and company found that teaching iteration first was better even when teaching Lisp, and Susan Wiedenbeck replicated the result (see blog post).
I can’t really blame these teachers. How could they know that there is a better way? How could we make it better? By what mechanism do we help CS teachers improve? This is a technology transfer problem. The research knows a better way. How do we change practice?
I’d argued previously that we should change promotion and tenure requirements to encourage active learning, and received massive pushback. I don’t think we’ll see that happen anywhere anytime soon. Teachers don’t want to feel “forced” to teach better.
Instead, what kind of feedback mechanism could we create so that undergraduate teachers learn that they’re not using effective methods? At my school (and I’m betting at most undergraduate institutions), the only feedback that a teacher gets is from student surveys, course-instructor opinion surveys. That’s not going to help with this problem. How could students know that the class would be better with peer instruction? How could students know that they should have seen iteration before recursion to learn more effectively?
Questions like these have been asked on the SIGCSE-Members list recently. What do you think? What kinds of effective feedback mechanisms have you seen to improve CS teaching? How do CS teachers get informed about research on better practices?
I’m looking forward to these results! That interaction is better than video lectures is really not surprising. That it leads to better learning than even reading is quite a surprise. My guess is that this is mediated by student ability as a reader, but as a description of where students are today (like the prior posts on active learning), it’s a useful result.
Koedinger and his team further tested whether their theory that “learning by doing” is better than lectures and reading in other subjects. Unfortunately, the data on video watching were incomplete. But they were able to determine across four different courses in computer science, biology, statistics and psychology that active exercises were six times more effective than reading. In one class, the active exercises were 16 times more effective than reading. (Koedinger is currently drafting a paper on these results to present at a conference in 2016.)