Sustained Idea Improvement: Validating a Conceptual Model for Knowledge Building in a Graduate Online Course

ABSTRACT Background Working innovatively with ideas plays a vital role in knowledge creation, yet the processes underlying sustained idea development remain incompletely understood. Objectives This study investigated how a conceptual model of sustained idea improvement facilitated knowledge creation among graduate students in an online course aimed at developing their thesis proposals. Methods Through mixed‐methods analysis of students' idea generation, online interactions and discourse patterns, the study examined knowledge‐building dynamics in an online environment. Results and Conclusions Findings suggest that the model of sustained idea improvement is effective in terms of guiding students in productive discourse that fosters the development of promising ideas for their thesis proposals. During the course, students progressively shifted their discourse from shallow sharing‐oriented to in‐depth elaboration‐ and synthesis‐oriented moves, demonstrating an increasing capacity for deeper intellectual engagement. Guided by this model, the study underscores the importance of a balanced approach that incorporates dynamic discourse moves for effective knowledge building (KB) in graduate school education contexts.

Authors

Institutions

Publication Details

Journal
Journal of Computer Assisted Learning
Published
2026-02-17
DOI
https://doi.org/10.1002/jcal.70211
Primary Topic
Educational Strategies and Epistemologies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sustained Idea Improvement: Validating a Conceptual Model for Knowledge Building in a Graduate Online Course

Chih‐Hsuan Chang, Mei‐Ju Chen, Huang‐Yao Hong
Journal of Computer Assisted Learning
Educational Strategies and Epistemologies
article

Sustained Idea Improvement: Validating a Conceptual Model for Knowledge Building in a Graduate Online Course

Chih‐Hsuan Chang, Mei‐Ju Chen, Huang‐Yao Hong
article en

Abstract

ABSTRACT Background Working innovatively with ideas plays a vital role in knowledge creation, yet the processes underlying sustained idea development remain incompletely understood. Objectives This study investigated how a conceptual model of sustained idea improvement facilitated knowledge creation among graduate students in an online course aimed at developing their thesis proposals. Methods Through mixed‐methods analysis of students' idea generation, online interactions and discourse patterns, the study examined knowledge‐building dynamics in an online environment. Results and Conclusions Findings suggest that the model of sustained idea improvement is effective in terms of guiding students in productive discourse that fosters the development of promising ideas for their thesis proposals. During the course, students progressively shifted their discourse from shallow sharing‐oriented to in‐depth elaboration‐ and synthesis‐oriented moves, demonstrating an increasing capacity for deeper intellectual engagement. Guided by this model, the study underscores the importance of a balanced approach that incorporates dynamic discourse moves for effective knowledge building (KB) in graduate school education contexts.

Journal of Computer Assisted LearningVol. 42(2)
University of Science and Technology (YE), National Tsing Hua University (TW), National Kaohsiung University of Science and Technology (TW), National Chengchi University (TW)
National Science and Technology Council
Openalex Percentile: Top 54%
Educational Strategies and Epistemologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.