Session Number
I03
Advisor(s)
Alice Bennett, Argonne National Laboratory Meridith Bruozas, Argonne National Laboratory Emily Cantu, Argonne National Laboratory John Domyancich, Argonne National Laboratory
Location
A-117
Start Date
28-4-2016 1:35 PM
End Date
28-4-2016 2:00 PM
Abstract
An increase in computer science careers will require workers to solve problems with advanced technology; even jobs outside this domain will depend on basic understanding of computer power. However, teaching the skills needed for proficiency has proved difficult. To combat this, we designed and implemented a Scratch-based computational thinking program that culminated in a group problem solving activity. We then added a lesson into the program that focused on planning methods—goal setting, action planning, and division of labor. Students (N = 54) completed surveys before and after the learning experience to measure whether explicitly teaching planning strategies assists in computational problem solving. The mean perceived validities of solutions for control and experimental groups were not significantly different (p = 0.917). Mean student understanding of their group's solution was lower in the experimental group compared to the control group (p = 0.047). The mean number of students who used planning techniques was not significantly different between the two groups (p = 0.268; p = 0.594; p = 0.180). Positive correlations exist between goal setting or division of labor and perceived validity of solution (p = 0.027; p = 0.040). These data indicate that setting goals and dividing labor may assist in solving computational problems, but explicitly teaching such strategies does not change the frequency of their use.
Effects of Instruction in Advanced Planning on Computational Problem Solving in a Group Environment
A-117
An increase in computer science careers will require workers to solve problems with advanced technology; even jobs outside this domain will depend on basic understanding of computer power. However, teaching the skills needed for proficiency has proved difficult. To combat this, we designed and implemented a Scratch-based computational thinking program that culminated in a group problem solving activity. We then added a lesson into the program that focused on planning methods—goal setting, action planning, and division of labor. Students (N = 54) completed surveys before and after the learning experience to measure whether explicitly teaching planning strategies assists in computational problem solving. The mean perceived validities of solutions for control and experimental groups were not significantly different (p = 0.917). Mean student understanding of their group's solution was lower in the experimental group compared to the control group (p = 0.047). The mean number of students who used planning techniques was not significantly different between the two groups (p = 0.268; p = 0.594; p = 0.180). Positive correlations exist between goal setting or division of labor and perceived validity of solution (p = 0.027; p = 0.040). These data indicate that setting goals and dividing labor may assist in solving computational problems, but explicitly teaching such strategies does not change the frequency of their use.