Blog Post #2000: Barbara Ericson Proposes: Effectiveness and Efficiency of Adaptive Parsons Problems #CSEdWeek

My 1000th blog post looked backward and forward.  This 2000th blog post is completely forward looking, from a personal perspective.  Today, my wife and research partner, Barbara Ericson, proposes her dissertation.

Interesting side note: One of our most famous theory professors just blogged on the theory implications of the Parsons Problems that Barb is studying. See post here.

Barb’s proposal is the beginning of the end of this stage in our lives.  Our youngest child is a senior in high school. When Barbara finishes her Human-Centered Computing PhD (expected mid-2017), we will be empty-nesters and ready to head out on a new adventure.

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
————–
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
———–

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.