Kim, K. & Clariana, R. (R&R). The influence of resource interdependence during collaborative problem solving: Tracking changes in knowledge structure. Educational Technology Research and Development.
Kim, K. (R&R). Visualizing reading comprehension: Understanding the influence of text structures on readers' knowledge structures. Reading Research Quarterly.
2020
Tawfik, A., Kim, K, & Kim, D. (2020). Effects of case library recommendation system on problem solving and knowledge structure development. Educational Technology Research and Development.
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2019
Kim, K., Clariana, R., & Kim, Y. (2019). Automatic representation of knowledge structure: Enhancing science learning through knowledge structure reflection in an online course. Educational Technology Research & Development.
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2018
Tawfik, A., Kim, K., Hogan, M., & Msilu, F. (2018). How success vs. failure cases support knowledge construction in collaborative problem solving. Journal of Educational Computing Research.
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Kim, K. (2018). An automatic measure of cross-language text structures. Technology, Knowledge and Learning. 28(2), 301-314.
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Smithwick, E., Baxter, E., Kim, K., Edel-Malizia, S., Rocco, S., & Blackstock, D. (2018). Interactive Videos enhance learning about socio-ecological systems. Journal of Geography. 117(1), 40-49.
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Zimmerman, W.A., Kang, H.B., Kim, K., Johnson, G., Clariana, R., & Zhang, F. (2018). Computer-automated approach for scoring short essays in an introductory statistics course. Journal of Statistics Education. 26(1), 40-47.
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Tawfik, A., Law, V., Ge, X., Wanli, X., & Kim, K. (2018). The Effect of sustained vs. faded scaffolding on students' argumentation in ill-structured problem solving. Computers in Human Behavior. 87, 436-449.
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2017
Kim, K. (2017). Graphical Interface of Knowledge Structure: A web-based research tool for representing knowledge structure in text. Technology, Knowledge and Learning.
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2015
Kim, K., & Clariana, R. (2015). Knowledge structure measures of reader’s situation models across languages: Translation engenders richer structure. Technology, Knowledge and Learning. 20(2), 249-268.
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2014
Clariana, R., Wolfe, M., & Kim, K. (2014). The influence of narrative and expository lesson text structures on knowledge structure: Alternate measure of knowledge structure. Educational Technology Research and Development, 62(5), 601-616.
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GIKS-Map Publications
2025
Kim, K. (R&R). Visualizing reading comprehension: Understanding the influence of text structures on readers' knowledge structures. Reading Research Quarterly.
2019
Kim, K., & Clariana, R. (2019). Applications of Pathfinder Network scaling for identifying an optimal use of first language for second language science reading comprehension. Educational Technology Research and Development.
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2017
Kim, K. (2017). Visualizing first and second language interactions in science reading: A knowledge structure network approach. Language Assessment Quarterly. 14(4), 328-345.
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Kim, K., & Clariana, R. (2017). Text signals influence second language science text comprehension: Knowledge structure analysis. Educational Technology Research and Development. 65(4), 909-930.
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Awards
2019
Young Researcher Award, Association for Educational Communications & Technology
Timothy & Cynthia Shanahan Outstanding Dissertation Award, International Literacy Association
Outstanding Empirical Journal Article Award, Association for Educational Communications & Technology
Journal of Geography Award, National Council for Geographic Education
Distance Education Best Practice Award, Association for Educational Communications & Technology
2018
Best Journal Article Award, Association for Educational Communications & Technology
Design and Development Division Outstanding Practice Award, Association for Educational Communications & Technology
Outstanding Journal Article Award, Association for Educational Communications & Technology
2017
Distance Education Best Practice Award, Association for Educational Communications & Technology
McJulien Scholar Best Paper Award, Association for Educational Communications & Technology
GIKS is designed to capture, visually represent, and compare the “knowledge structure” from learners’ multimodal data in their learning process, which can be used to enrich our understanding of the learning process in a new way.