Announcing Barbara Ericson’s Defense on Effectiveness and Efficiency of Parsons Problems and Dynamically Adaptive Parsons Problems: Next stop, University of Michigan

Today, Barbara Ericson defends her dissertation. I usually do a blog post talking about the defending student’s work as I’ve blogged about it in the past, but that’s really hard with Barb.  I’ve written over 90 blog posts referencing Barb in the last 9 years.  That happens when we have been married for 32 years and collaborators on CS education work for some 15 years.

Barb did her dissertation on adaptive Parsons problems, but she could have done it on Project Rise Up or some deeper analysis of her years of AP CS analyses. She chose well. Her results are fantastic, and summarized below. (Yes, she does have six committee members, including two external members.)

Starting September 1, Barbara and I will be faculty at the University of Michigan. Barb will be an assistant professor in the University of Michigan School of Information (UMSI). I will be a professor in the Computer Science and Engineering (CSE) Division of the Electrical Engineering and Computer Science Department, jointly with their new Engineering Education Research program. Moving from Georgia Tech and Atlanta will be hard — all three of our children will still be here as we leave. We are excited about the opportunities and new colleagues that we will have in Ann Arbor.

Title: Evaluating the Effectiveness and Efficiency of Parsons Problems and Dynamically Adaptive Parsons Problems as a Type of Low Cognitive Load Practice Problem

Barbara J. Ericson

Human-Centered Computing

School of Interactive Computing

College of Computing

Georgia Institute of Technology

Date: Monday, March 12, 2018

Time: 12pm – 3pm

Location: TSRB 222

Committee:

Dr. Jim Foley (Advisor, School of Interactive Computing, Georgia Institute of Technology)

Dr. Amy Bruckman (School of Interactive Computing, Georgia Institute of Technology)

Dr. Ashok K. Goel (School of Interactive Computing, Georgia Institute of Technology)

Dr. Richard Catrambone (School of Psychology, Georgia Institute of Technology)

Dr. Alan Kay (Computer Science Department, University of California, Los Angeles)

Dr. Mitchel Resnick (Media Laboratory, Massachusetts Institute of Technology)

Abstract:

Learning to program can be difficult and time consuming.  Learners can spend hours trying to figure out why their program doesn’t compile or run correctly. Many countries, including the United States, want to train thousands of secondary teachers to teach programming.  However, busy in-service teachers do not have hours to waste on compiler errors or debugging.  They need a more efficient way to learn.

One way to reduce learning time is to use a completion task.  Parsons problems are a type of code completion problem in which the learner must place blocks of correct, but mixed up, code in the correct order. Parsons problems can also have distractor blocks, which are not needed in a correct solution.  Distractor blocks include common syntax errors like a missing colon on a for loop or semantic errors like the wrong condition on a loop.

In this dissertation, I conducted three studies to compare the efficiency and effectiveness of solving Parsons problems, fixing code, and writing code. (Editor’s note: I blogged on her first study here.) I also tested two forms of adaptation. For the second study, I added intra-problem adaptation, which dynamically makes the current problem easier.  For the last study, I added inter-problem adaptation which makes the next problem easier or harder depending on the learner’s performance.  The studies provided evidence that students can complete Parsons problems significantly faster than fixing or writing code while achieving the same learning gains from pretest to posttest.  The studies also provided evidence that adaptation helped more learners successfully solve Parsons problems.

These studies were the first to empirically test the efficiency and effectiveness of solving Parsons problems versus fixing and writing code.  They were also the first to explore the impact of both intra-problem and inter-problem adaptive Parsons problems.  Finding a more efficient and just as effective form of practice could reduce the frustration that many novices feel when learning programming and help prepare thousands of secondary teachers to teach introductory computing courses.