Knowledge Management & E-Learning: An International Journal (KM&EL), Vol 9, No 1 (2017)

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Predicting computer science students’ online help-seeking tendencies

Qiang Hao, Brad Barnes, Robert Maribe Branch, Ewan Wright

Abstract


This study investigated how computer science students seek help online in their learning and what factors predict their online help-seeking behaviors. Online help-seeking behaviors include online searching, asking teachers online for help, and asking peers online for help. 207 students from a large university in the southeastern United States participated in the study. It was revealed that computer science students tended to search online more frequently than ask people online for help. Five factors, including epistemological belief, interest, learning proficiency level, prior knowledge of the learning subject, and problem difficulty, were explored as potential predictors in this study. It was found that learning proficiency level and problem difficulty were significant predictors of three types of online help-seeking behaviors, and other factors influenced online help seeking to different extents. The study provides evidence to support that online searching should be considered as an integrated part of online help seeking, and gives guidelines for practice of facilitating online help seeking and future studies.

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Knowledge Management & E-Learning: An International Journal (KM&EL)
ISSN 2073-7904

 

Maintained and Developed by:

Laboratory for Knowledge Management & E-Learning

Faculty of Education, The University of Hong Kong