Free ticketed event
Purpose and sources
Computer programming is most often taught as a kind of apprenticeship: Teachers lecture about the programming language constructs and library functions and then students spend many hours practicing those constructs and functions. This approach is demanding in terms of student time and frustration, and has a high cognitive load.
Starting in 2003, a team of computing education researchers began to teach non-technical majors programming using a contextualized approach called “Media Computation,” developing the curriculum with a design-based research approach. The team used assessments at multiple institutions over multiple years to measure student learning and attitudes. Media Computation is now in use in a range of universities for computer science majors, including Georgia Tech, UCSD, and West Point.
Worked examples and subgoal labeling for teaching programming were invented by drawing on findings from educational psychology and learning sciences (with Briana Morrison, Lauren Margulieux, and Richard Catrambone) . These methods are research-based, with peer-reviewed publications supporting the effectiveness claims. While the worked examples and subgoal labeling methods were developed in the Media Computation course, they have been evaluated in other course contexts and are applicable in any course teaching programming.
This workshop will be taught in an active learning format. Techniques will be demonstrated as if the attendees were students in a programming course. Then attendees will work with the techniques. Attendees will be provided with citations to the literature describing the techniques in greater detail and providing evidence of efficacy.
Agenda (Requesting 2 hours)
10 minutes: Welcome and introductions
15 minutes: Media Computation to manipulate pictures and sounds in Python and GP (blocks-based programming)
15 minutes: Attendees use either Python or GP to extend the examples
(Total: 40 minutes)
10 minutes: Worked examples: Using live coding to build a worked example, for predictions
10 minutes: Attendees work through prediction and discuss applications of the approach
10 minutes: Worked examples: Using worked examples for peer instruction.
10 minutes: Attendees solve a peer instruction question and discuss applicability in their courses
(Total: 1 hr 20 minutes)
15 minutes: Subgoal labeling: Examples of subgoal labeling, with results from research.
10 minutes: Attendees apply subgoal labels to a program.
15 minutes: Discussion—how might you use these in your course?
(Total: 2 hours)
Mark Guzdial is a Professor in the Computer Science & Engineering Division and in Engineering Education Research at the University of Michigan. He studies how people come to understand computing and how to make that more effective. He leads the CSLearning4U project to create ebooks to help high school teachers learn CS. He led the NSF alliance “Expanding Computing Education Pathways" which helps 16 US states and Puerto Rico improve and broaden their computing education. He invented “Media Computation” and has published several books on the use of media as a context for learning computing. He is on the editorial boards of the "Journal of the Learning Sciences," "ACM Transactions on Computing Education," and "Communications of the ACM." With his wife and colleague, Barbara Ericson, he received the 2010 ACM Karl V. Karlstrom Outstanding Educator award. He was also the recipient of the 2012 IEEE Computer Society Undergraduate Teaching Award. He is an ACM Distinguished Educator and a Fellow of the ACM.
Address: Computer Science & Engineering Division, 2260 Hayward St, Ann Arbor, MI 48109; telephone: (734) 647-1320; e-mail: firstname.lastname@example.org.