Keywords

motiviation, self-regulated learning, online learning, tiered support

Abstract

Providing meaningful practice and feedback is a critical component of supporting students' learning, but it is also labor-intensive and time-consuming for instructors. This study evaluated the effectiveness of Knightly Codes, a supplemental system designed to provide automated, tiered feedback to students in an introductory, college-level Computer Science course designed to help them develop strategies to tackle real-world coding problems and maintain or improve their interest and motivation. Key design features include increasingly specific guidance as students submit multiple incorrect solutions, gamification elements including progress checks, badges, and personal and global statistics, and reflection surveys to promote self-regulated learning. A total of 170 students submitted at least one problem. To evaluate the system, data collection included measures of interest in computer science, problem difficulty ratings before and after solution, confidence in a correct submission, the number of submissions, and the results of those submissions. There was no change in students' interest in computer programming or their ability to evaluate problem difficulty. However, other data suggest improved motivation: students were nearly twice as likely to persist to a correct solution than abandon a problem, students required fewer attempts per problem as they progressed, and their confidence in their submissions increased over time. However, these results need to be interpreted with caution because only 25 students (7.5% of registered students) completed more than a third of the problems, and 7 students (2.1% of registered students) completed more than two-thirds of the problems. Notably, the system helped students understand introductory programming concepts and how these concepts and strategies fit different scenarios. The evaluation and conclusions in this dissertation offer suggestions for future development of the system or similar systems to enhance its effectiveness.

Completion Date

2025

Semester

Fall

Committee Chair

Boote, David

Degree

Doctor of Education (Ed.D.)

College

College of Community Innovation and Education

Department

Learning Sciences and Educational Research

Format

PDF

Identifier

DP0029753

Document Type

Thesis

Campus Location

Orlando (Main) Campus

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