Xiangmin Zhang (ae9101)

Xiangmin Zhang  is a Professor in the School of Information Sciences, where he teaches human-computer interaction and information technology courses. His research interests include UX research, human-AI interactions, information searching behaviors, personalization techniques, data analytics, and learning-analytics. His research has been funded by both government agencies and industry. 

Guest editor: Special Issue on "Unintended Consequences and Impacts of Emerging Technologies," the International Journal of Information Management Data Insights (submission deadline expired)

https://www.sciencedirect.com/journal/international-journal-of-information-management-data-insights/about/call-for-papers

Guest editor: Special issue on Explainable AI for Recommender Systems, the International Journal of Computational Intelligence.

Teaching: Human-Computer Interaction; UX Design,Information Architecture; Building Web-based Information Services;  and Website Development

• Ph.D., iSchool, University of Toronto
• B.A., M.S., Department of Information Management, Peking University

 Wednesdays 7:00 - 8:00 p.m.

User experience research; human-computer interaction; human-AI interaction, usability/UX evaluations; user domain knowledge modeling; personalization of information systems; information retrieval; experimental design; quantitative research; data analytics

 Google Scholar Profile: scholar.google.com/citations

 rm300.1, Kresge Library

Debajyoti Pal, Xiangmin Zhang, Subhodeep Mukherjee, & Suree Funilku (2023). Switching to metaverse? Perspectives from push–pull–mooring model, November 2023, Journal of Computers in Education, https://doi.org/10.1007/s40692-023-00301-y

Debajyoti Pal , Vajirasak Vanijja, Himanshu Thapliyal, & Xiangmin Zhang (2023). What affects the usage of artificial conversational agents? An agent personality and love theory perspective. April 2023, Computers in Human Behavior 145(3). DOI: 10.1016/j.chb.2023.107788

Debajyoti Pal , Vajirasak Vanijja, Xiangmin Zhang, & Himanshu Thapliyal (2021). Exploring the Antecedents of Consumer Electronics IoT Devices Purchase Decision: A Mixed Methods Study. IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, VOL. 67, NO. 4, 305-318.

Debajyoti Pal, Mohammad Dawood Babakerkhell, & X. Zhang (2021). Exploring the Determinants of Users' Continuance Usage Intention of Smart Voice Assistants. IEEE Access, Vol. 9, DOI 10.1109/ACCESS.2021.3132399

Ketcheson LR, Moore EWG, Wentz CF, Zhou K, Zhang X, et al. (2021) Variables Contributing to the Age of Diagnosis of Autism Spectrum Disorder (ASD): Implications for Addressing Diagnostic Disparities. Int J Autism & Relat Disabil: IJARD-149. DOI: 10.29011/2642-3227.000049

Pal, D. , Zhang, X., Siyal, S., “ Prohibitive factors to the acceptance of Internet of Things (IoT) technology in society: A smart-home context using a resistive modelling approach”, 2021, Technology in Society, Volume 66, August 2021, (Available online 20 July 2021), https://doi.org/10.1016/j.techsoc.2021.101683

DEBAJYOTI PAL 1, SUREE FUNILKUL1, & X. ZHANG (2020), Should I Disclose My Personal Data? Perspectives From Internet of Things Services. IEEE Access, DOI: 10.1109/ACCESS.2020.3048163

Wentz,C., Zhou, K., Bai, C., Ketcheson, L., Charbonneau, D., Zhang, X., Lu, S. (2019). Predicting Age of Diagnosis in Children with Autism Spectrum Disorder Using Deep Learning Approaches, INSAR 2019.

DEBAJYOTI PAL, VAJIRASAK VANIJJA, CHONLAMETH ARPNIKANONDT, X. ZHANG, and BORWORN PAPASRATORN (2019), A Quantitative Approach for Evaluating the Quality of Experience of Smart-Wearables From the Quality of Data and Quality of Information: An End User Perspective. IEEE Access, vol. 7, 2019, pp. 64266-64278.

Debajyoti Pal, Tuul Triyason, Vijayakumar Varadarajan, & X. Zhang (2019), Quality of Experience Evaluation of Smartwearables: A Mathematical Modelling Approach. Proceedings of the 35th IEEE International Conference on Data Engineering Workshops (ICDEW 2019), pp. 74-80.

Jingjing Liu & Xiangmin Zhang (2019). The Role of Domain Knowledge in Document Selection from Search Results. Journal of the Association for Information Science and Technology.

Zhang, X. (2018). User Perceived Learning from Interactions during Searching on Big Medical Literature Data, Big Data and Information Analytics, doi: 10.3934/bdia.2017019

Liu, C., Zhang, X., & Huang, W. (2016) The Exploration of Objective Task Difficulty and Domain Knowledge Effects on Users’ Query Formulation. Proceedings of the Association for Information Science & Technology 2016 Annual Conference

Zhang, X. (2016). An Analysis of Online Students’ Behaviors on Course Sites and the Effect on Learning Performance: A Case Study of Four LIS Online Classes. Journal of Education for Library and Information Science, 57(4), 2016

Zhang, X. (2016). Search interactions and perceived learning. Proceedings of the HCI International 2016, Springer, July 17 – 22, Toronto, 2016

Zhang, X., Liu, J., Cole, M., Belkin, N. (2015) Predicting Users’ Domain Knowledge in Information Retrieval Using Multiple Regression Analysis of Search Behaviors, Journal of the Association for Information Science and Technology, 66(5):980–1000

Zhang, X., Liu, J., Liu, C., Cole, M. (2014): Factors Affecting Users' Perceived Learning during Searching. Proceedings of the International Conference on eLearning (ICEL) 2014. ACPL. pp. 200-210

Yuan, X., Chen, C., Zhang, X., Avery, J., Xu, T. (2013): Effects of Domain Knowledge on User Task Performance in a Knowledge Domain Visualization System. Proceedings of the HCI International 2013. Springer

Ho, S. M., Bieber, M., Song, M., & Zhang, X. (2013) Seeking Beyond with IntegraL: A User Study of Sense-Making Enabled by Anchor-based Virtual Integration of Library Systems, Journal of the American Society for Information Science and Technology, 64(9), 1927-1945

Cole, M., Gwizdka, J., Liu, C., Belkin, N., Zhang, X. (2013): Inferring User Knowledge Level from Eye Movement Patterns, Information Processing & Management. 49(5), 1075-1091

Liu, J., Belkin, N., Zhang, X., Yuan, X. (2013): Examining Users’ Knowledge Change in the Task Completion Process. Information Processing & Management, 49(5), 1058-1074

Zhang, X., Liu, J. & Cole, M. (2013): Task Topic Knowledge vs. Background Domain Knowledge: Impact of Two Types of Knowledge on User Search Performance. In: Álvaro Rocha, Ana Maria Correia, Tom Wilson, & Karl A. Stroetmann (ed.): Advances in Information Systems and Technologies: Proceedings of the 2013 World Conference on Information Systems and Technologies. Springer, 2013; pp. 179-191

Liu, C., Liu, J., Cole, M., Belkin, N., Zhang, X. (2012): Task Difficulty and Domain Knowledge Effects on Information Search Behaviors, ASIST 2012 Annual Meeting, Baltimore, MD, October 26-30, 2012

Liu, J., Liu, C., Cole, M., Belkin, N., Zhang, X. (2012): Exploring and Predicting Search Task Difficulty, Proceedings of ACM CIKM2012, ACM.

Zhang, X. & Y. Li (2012): Effects of “Advanced Search” on User Performance and Search Efforts: A Case Study with Three Digital Libraries, Proceedings of the 45th Annual Hawaii International Conference on Systems Science, January 4-7, 2012, IEEE Computer Society Press, 2012, 10 p

Yuan, X.-J., Zhang, X.-J, Chen, C., Avery, J. (2011): "Seeking information with an information visualization system: a study of cognitive styles". Information Research, 16(4) paper 499 [Available at http://InformationR.net/ir/16-4/paper499.html]

Li, Y. & Zhang, X. (2011): Individual Differences and the Usability
of Digital Libraries, Journal of the China Society for Scientific & Technical Information, 2011, 30(9): 980-989

M. Cole, J. Gwizdka, C. Liu, R. Bierig, N. Belkin & X. Zhang (2011): Task and User Effects on Reading Patterns in Information Search. Interacting with Computers, 23 (4): 346-362

X. Zhang, M. Cole, N. Belkin (2011): Predicting Users’ Domain Knowledge from Search Behaviors, Proceedings of ACM SIGIR 2011, Beijing, China, July 24 -28, 2011.

M. Cole, X. Zhang, C. Liu, N. Belkin, J. Gwizdka, (2011): Knowledge Effects on Document Selection in Search Results Pages. Proceedings of the ACM SIGIR 2011, Beijing, China, July 24 -28, 2011.

Shuyuan Mary Ho & Xiangmin Zhang (2011): i-Sensor Inference Model for Assessing Trustworthiness in Computer-Mediated Communications, Proceedings of ACM CSCW 2011, March 19–23, 2011, Hangzhou, China
 

• May 2019 – summer 2020: Co-PI, “Leveraging Big Data to Investigate Health Disparities among Children with Autism Spectrum Disorders (ASD)”, sponsored by the WSU Provost’s office; $10k
• Oct 1, 2007 – Dec. 30, 2010: “Personalization of Digital Library Experience,” Institute of Museum, Library Services, co-PI (PI: Nick Belkin); $964,890.00
• • July 2007 – May 2008: “Eliciting and Modeling Users’ Subject Domain Knowledge.” Rutgers University, Research Council Grant Award, $1100
• • November 2006 – June 2007: “Subject Domain Knowledge Modeling: A Software Tool.” SCILS Grants to Get Grant: $1472
• • June 2004 -- 2006: PI: “Experiment and Evaluation of Digital Library Performance.” Funded by the IEEE, Inc., $107,000
• ...

User experience research; human-computer/AI interaction; user domain knowledge modeling; personalization of information systems; information retrieval; experimental design; quantitative research; data analytics