Article
What Are the Most Important Predictors of Computer Science Students’ Online Help-Seeking Behaviors?
Computers in Human Behavior
(2016)
Abstract
This study investigates the most important predictors of computer science students’ online help-seeking behaviors. 203 computer science students from a large university in southeastern United States participated in the study. Online help-seeking behaviors explored in this study include online searching, asking teachers online for help, and asking peers online for help. Ten-fold cross validation was used to select the most significant predictors from eight potential factors, including prior knowledge of the learning subject, learning proficiency level, academic performance, epistemological belief, interests, problem difficulty, age and gender. Problem difficulty was selected as the most important predictor for all three types of online help seeking, while learning proficiency level, academic performance, and epistemological belief were selected as the most important predictors for both online searching and asking teachers online for help. Based on the selected factors and their relationships with online help seeking, the study provides guidance on targeted training for online help seeking in an era of mass higher education.
Keywords
- Online help seeking,
- Important predictors,
- Online searching,
- Asking peers online for help,
- Cross-validation
Disciplines
Publication Date
September, 2016
DOI
https://doi.org/10.1016/j.chb.2016.04.016
Publisher Statement
Copyright by Elsevier B.V.
Citation Information
Qiang Hao, Ewan Wright, Brad Barnes and Robert Maribe Branch. "What Are the Most Important Predictors of Computer Science Students’ Online Help-Seeking Behaviors?" Computers in Human Behavior Vol. 62 (2016) p. 467 - 474 Available at: http://works.bepress.com/qiang-hao/1/