THE INVESTIGATION OF NOVICE PROGRAMMERS’ DEBUGGING BEHAVIORS TO INFORM INTELLIGENT E-LEARNING ENVIRONMENTS: A CASE STUDY

Year 2020, Volume: 21 Issue: 3, 142 - 155, 01.07.2020

Gamze Türkmen Sonay Caner

https://doi.org/10.17718/tojde.762039

Abstract

This study aims to provide a comprehensive and in-depth investigation of the debugging process in
programming teaching in terms of cognitive and metacognitive aspects, based on programming students
who demonstrate low, medium, and high programming performance and to propose instructional strategies
for scaffolding novice learners in an effective way. Data were collected from 41 novice programming students
from C++ and Python programming language courses of the same instructor in the scope of case study
methodology, and data instruments included paper-based programming questions. The questions were
framed under three categories as questions’ difficulty levels (low, moderate and high), error types (syntax
and logic), and question types (if-else and while). As having three categories, a total of 12 different data
(3x2x2) were taken from each student, which means 492 data rows were evaluated in the study. Chi-square
test results revealed that while error detection and correction are significantly high in low difficulty level
questions, error detection and error correction attempts for logic errors were substantially higher compared
to syntax errors. Further analysis conducted for paper-based markings that were used by students throughout
their error detection, correction, and completion attempts. Chi-square test results revealed significant
relationships between marking availability and error types, as well as difficulty levels. Results were discussed
for both traditional learning and e-learning environments in terms of what kind of educational implications
and strategies can be outlined by data for increasing the effectiveness of programming education for novice
learners.

Keywords

Programming instruction, debugging,, novice programmers, correlational study, intelligent e-learning environments

References

• Liang, K., Zhang, Y., He, Y., Zhou, Y., Tan, W., & Li, X. (2017). Online behavior analysis-based student profile for intelligent E-learning. Journal of Electrical and Computer Engineering, 2017.