Title: EVALUATING THE EFFECTIVINESS AND EFFICIENCY OF PARSONS PROBLEMS AND DYNAMICALLY ADAPTIVE PARSONS PROBLEMS AS A TYPE OF LOW COGNITIVE LOAD PRACTICE PROBLEM

Barbara J. Ericson
Ph.D. student
Human Centered Computing
College of Computing
Georgia Institute of Technology

Date: Wednesday, December 9, 2015
Time: 12pm to 2pm EDT
Location: TSRB 223

Committee
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Dr. James Foley, School of Interactive Computing (advisor)
Dr. Amy Bruckman, School of Interactive Computing
Dr. Ashok Goel, School of Interactive Computing
Dr. Richard Catrambone, School of Psychology
Dr. Mitchel Resnick, Media Laboratory, Massachusetts Institute of Technology

Abstract
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Learning to program can be difficult and can result in hours of frustration looking

for syntactic or semantic errors. This can make it especially difficult to prepare inservice

(working) high school teachers who don’t have any prior programming

experience to teach programming, since it requires an unpredictable amount of time for

practice in order to learn programming. The United States is trying to prepare 10,000

high school teachers to teach introductory programming courses by fall 2016. Most

introductory programming courses and textbooks rely on having learners gain experience

by writing lots of programs. However, writing programs is a complex cognitive task,

which can easily overload working memory, which impedes learning.

One way to potentially decrease the cognitive load of learning to program is to

use Parsons problems to give teachers practice with syntactic and semantic errors as well

as exposure to common algorithms. Parsons problems are a type of low cognitive load

code completion problem in which the correct code is provided, but is mixed up and has

to be placed in the correct order. Some variants of Parsons problems also require the

code to be indented to show the block structure. Distractor code can also be provided

that contains syntactic and semantic errors.

In my research I will compare solving Parsons problems that contain syntactic and

semantic errors, to fixing code with the same syntactic and semantic errors, and to writing

the equivalent code. I will examine learning from pre- to post-test as well as student

reported cognitive load. In addition, I will create dynamically adaptive Parsons problems

where the difficulty level of the problem is based on the learners’ prior and current

progress. If the learner solves one Parsons problem in one attempt the next problem will

be made more difficult. If the learner is having trouble solving a Parsons problem the

current problem will be made easier. This should enhance learning by keeping the

problem in the learner’s zone of proximal development as described by Vygotsky. I will

compare non-adaptive Parsons problems to dynamically adaptive Parsons problems in

terms of enjoyment, completion, learning, and cognitive load.

The major contributions of this work are a better understanding of how variants of

Parsons problems can be used to improve the efficiency and effectiveness of learning to

program and how they relate to code fixing and code writing. Parsons problems can help

teachers practice programming in order to prepare them to teach introductory computer

science at the high school level and potentially help reduce the frustration and difficulty 

all beginning programmers face in learning to program.