根據先前的物聯網介面評估計畫，研究團隊認為現行的使用者經驗研究實務場域尚缺乏一個更有效率的心智模型量測方法，而本研究認為概念圖是其中最適合的選擇。概念圖作為一種將個人認知架構以概念節點和其間關係作為表徵的圖形，同時符合計畫中的觀察結果和實際應用需求。
為了確認此量測方法和經由客觀計算產出之概念圖相似值指數可被應用於滿足實務需求，本研究先以另一經由主觀感知產生的相似度分數作為效標，並計算兩者之間的相關程度作為相似值指數的效標關聯效度，最終兩個變數之間呈現中等程度的相關 (r = 0.513, p < .01)。再者，將兩個變數分別作為應變數 (y)，並協同可能影響概念圖相似度的預想影響因子集合產生兩個迴歸模型，用於檢視、比較兩種相似度的可能判斷來源組成。根據迴歸模型，主觀認知相似度主要反映較高層次的圖形特性與作答者背景知識，而相似值指數則涵蓋全部層次的圖形特徵以及作答者認知。
綜上所述，概念圖是一個囊括靈活性、適應性、經濟性、自動化可能和信效度的心智模型測量方法。若評分和執行方法設計得宜，此量測方法可被廣泛應用於各種目的的心智模型測量，亦可橫跨所有產品開發階段。

Based on previous IoT UI evaluation project, the need for a more efficient mental model measurement tool in UX research practices was identified, and concept mapping was considered the ideal candidate. Concept mapping is a kind of cognitive structure presentation that reflects the person’s mind by concept nodes and relationships between, which matches prior observations and meets practical needs.
To validate the method and corresponding objectively-calculated closeness index as the similarity parameter, the subject-perceived similarity was utilized to measure the criterion-related validity, indicating a moderate correlation between the two (r = 0.513, p < .01). Next, the two variables than served as the response (y) in fitted regression models, for further contributory factor inspection and comparison. As a result, subjective similarity reflects mainly the higher-level graphical features and subject’s background knowledge, while the other grasp all aspects and local graphical features.
In conclusion, concept mapping possesses many advantages for it being flexible, adaptable, economical, automatable, and reliable as a mental model measurement and assessment tool. If designed properly, the method is capable of being applied for various purposes as well as across design development stages.

Agile alliance. (2013, June 8). What is Agile Software Development?. [Blog post]. Retrieved from https://www.agilealliance.org/agile101/
Akoglu, H. (2018). User's guide to correlation coefficients. Turkish journal of emergency medicine, 18(3), 91–93. doi:10.1016/j.tjem.2018.08.001
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things: a survey on enabling technologies, protocols, and applications. IEEECommun. Surv. Tutor. 17 (4), 2347–2376.
Andreas, B. G. (1972). Experimental psychology (2nd edition). New York, USA: John Wiley & Sons.
Andrews, K., & Smrdel, A. (2017). Responsive Data Visualisation. 113-115. Poster session presented at Eurographics / VGTC Conference on Visualization, Barcelona, Spain. https://doi.org/10.2312/eurp.20171182
Anohina-Naumeca, A., & Graudina, V. (2012). Diversity of concept mapping tasks: degree of difficulty, directedness, and task constraints. Proceedings of the 5th International Conference on Concept Mapping, 164-171.
Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence, & J. T. Spence (Eds.), The psychology of learning and motivation: Advances in research and theory (Vol. 2, pp. 89-195). New York: Academic Press.
Bias, R. G., Moon, B. M., & Hoffman, R. R. (2015). Concept mapping usability evaluation: An exploratory study of a new usability inspection method. International Journal of Human-Computer Interaction, 31(9), 571-583. doi:10.1080/10447318.2015.1065692
Brambilla, M., Umuhoza, E., & Acerbis, R. (2017). Model-driven development of user interfaces for IoT systems via domain-specific components and patterns. Journal of Internet Services and Applications, 8(1). doi:10.1186/s13174-017-0064-1
Chokshi, S., & Mann, D. (2018). Innovating from within: A process model for user-centered digital development in academic medical centers. JMIR Human Factors.
Goldsmith, T. E., Johnson. P. J. & Acton, W. H. (1991). Assessing structural knowledge. Journal of Educational Psychology, 83, 88-96.
Gothelf, J. (2013). Lean UX: Applying lean principles to improve user experience. Cambridge, MA: O’Reilly Media.
Grundspenkis, J., & Strautmane, M. (2009). Usage of Graph Patterns for Knowledge Assessment Based on Concept Maps. Scientific Journal of Riga Technical University, Computer Science, 38, 60-71.
Gubbi, J., Buyya, R., S. Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): a vision, architectural elements, and future directions. Future Generation Computer Systems, 29 (7), 1645-1660.
Harnisch, D. L., Sato, T., Zheng, P., Yamaji, S., & Connell, M. (1996). Concept mapping approach and its implications in instruction and assessment. Computers in the Schools, 5(3), 132-168.
Hassenzahl, M., & Tractinsky, N. (2006). User Experience – A Research Agenda. Behaviour & Information Technology, 25(2), 91-97.
Hoz, R., Tomer, Y., & Tamir, P. (1990). The relations between disciplinary and pedagogi- cal knowledge and the length of teaching experience of biology and geography teachers. Journal of Research in Science Teaching, 27, 973-985.
International Telecommunication Union, ITU. (2012). Overview of the Internet of things. ITU.
Isomursu, M., Sirotkin, A., Voltti, P., & Halonen, M. (2012). User experience design goes agile in lean transformation – A Case Study. Proc of Agile Conference (AGILE), 1–10.
Lomask, M., Baron, J. B., Greig, J., & Harrison, C. (1992, March). ConnMap: Connecti- cut’s use of concept mapping to assess the structure of students’ knowledge of science. Paper presented at the annual meeting of the National Association of Research in Science Teaching, Cambridge, MA.
Ng, I. C. L., & Wakenshaw, S. Y. L. (2017). The internet-of-things: Review and research di-rections. International Journal of Research in Marketing, 34(1), 3–21.
Nielsen, J. (1989). Usability engineering at a discount. In Salvendy, G., and Smith, M.J. (Eds.), Designing and Using Human-Computer Interfaces and Knowledge Based Systems, Elsevier Science Publishers, Amsterdam. 394-401.
Nielsen, J. (2010, October 18). Mental Models. Retrieved June 25, 2019, from https://www.nngroup.com/articles/mental-models/
Nielsen, J., and Landauer, T. K. (1993). A mathematical model of the finding of usability problems. Proc. ACM INTERCHI'93 Conf. (Amsterdam, the Netherlands, 24-29 April), 206-213.
Norman, D. A. (2013). The Design of Everyday Things, revised & expended ed. Basic Books, New York.
Novak, J. D., & Can˜as, A. J. (2006). The theory underlying concept maps and how to construct them. Technical Report IHMC Cmap Tools 2006-01. Retrieved June 25, 2019, Florida Institute for Human and Machine Cognition, from http://cmap.ihmcus/Publications/ResearchPapers/TheoryUnderlyingConceptMaps.pdf.
Novak, J. D., & Cañas, A. J. (2008). The theory underlying concept maps and how to construct and use them (Technical Report IHMC CmapTools 2006-01 Rev 01-2008). Florida Institute for Human and Machine Cognition. Retrieved from http://cmap.ihmc.us/Publications/ResearchPapers/TheoryUnderlyingConceptMaps.pdf
Novak, J. D., & Gowin, D. B. (1984). Learning how to learn. New York: Cambridge Uni-versity Press.
Ruiz-Primo, M. A., & Shavelson, R. J. (1996). Problems and issues in the use of concept maps in science assessment. Journal of Research in Science Teaching, 33, 569–600.
Ruiz-Primo, M. A., Schultz, S. E., Li, M., & Shavelson, R. J. (2001). Comparison of the reliability and validity of scores from two concept-mapping techniques. Journal of Research in Science Teaching, 38(2), 260–278.
Watson, M. K., Pelkey, J. G., Noyes, C., & Rodgers, M. O. (2015). Assessing conceptual knowledge using three concept map scoring methods. J. Eng. Educ., 105 (1), 118-146.
Wentzel, J., Müller, F., de Jong, N., & Van Gemert-Pijnen, J. (2016). Card sorting to evaluate the robustness of the information architecture of a protocol website. International journal of medical informatics, 86, 71 -81.
Yin, Y., Vanides, J., Ruiz-Primo, M. A., Ayala, C. C., & Shavelson, R. J. (2005). Comparison of two concept-mapping techniques: Implications for scoring, interpretation, and use. Journal of Research in Science Teaching, 42, 166–184.