Abstract
Online surveys are becoming more popular as a means of information gathering in both academia and industry because of their relatively low cost and delivery. However, there are increasing debates on data quality in online surveys. We present a novel survey prototyping tool that integrates embedded learning resources to facilitate the survey prototyping process and encourage creating scientifically sound surveys. Results from a controlled pilot study confirmed that survey structure follows three guided principles: simple-first, structure-coherent and gradual-difficulty-increase, revealing positive effects on survey structures under learning resources influences.
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Evans, J.R., Mathur, A.: The value of online surveys. Internet Research 15(2), 195–219 (2005)
Aleven, V., Koedinger, K., Cross, K.: Tutoring answer explanation fosters learning with understanding. In: Lajoie, S., Vivet, M. (eds.) Artificial Intelligence in Education, pp. 199–206 (1999)
He, Y., Hui, S.H., Quan, T.T.: Automatic summary assessment for intelligent tutoring systems. Computers & Education 53(3), 890–899 (2009)
Pinkwart, N., Ashley, K.D., Lynch, C., Aleven, V.: Evaluating an Intelligent Tutoring System for Making Legal Arguments with Hypotheticals. To appear in International Journal of AI in Education; Special Issue on Ill-Defined Domains 19(4) (2009), Aleven, V., Lynch, C. Pinkwart, N., Ashley, K. (eds)
Hsiao, I., Malhotra, M., Joo, J., Chae, H.S., Natriello, G.: Survey Sidekick: Learning & designing scientifically sound surveys. In: Paper for “Human-Computer Interaction and the Learning Sciences” Workshop, CSCL 2013, Madison, WI (2013)
Sudol, L.A., Rivers, K., Harris, T.K.: Calculating Probabilistic Distance to Solution in a Complex Problem Solving Domain. In: EDM, pp. 144–147 (2012)
Iarossi, G.: The Power of Survey Design: A User’s Guide for Managing Surveys, Interpreting Results, and Influencing Respondents. The World Bank, Washington, D.C (2006)
Barnes, T., Stamper, J.: Automatic hint generation for logic proof tutoring using historical data. Journal of Educational Technology & Society, Special issue on Intelligent Tutoring Systems 13(1), 3–12 (2010)
Le, N.-T., Menzel, W.: Using Constraint-Based Modelling to Describe the Solution Space of Ill-defined Problems in Logic Programming. In: Advances in Web Based Learning (ICSL 2007), pp. 367–379 (2007)
Mitrovic, A.: An Intelligent SQL Tutor on the Web. International Journal of Articial Intelligence in Education 13(2-4), 173–197 (2003)
Kittur, A., Chi, E.H., Suh, B.: Crowdsourcing user studies with Mechanical Turk. In: Proc. of CHI 2008 (2008)
Snow, R., O’Connor, B., Jurafsky, D., Ng, A.Y.: Cheap and fast—but is it good?: evaluating non-expert annotations for natural language tasks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Honolulu, Hawaii, October 25-27 (2008)
Hsiao, I., Brusilvsky, P.: The Role of Community Feedback in the Student Example Authoring Process: An Evaluation of AnnotEx. British Journal of Educational Technology 42(3), 482–499 (2011)
Liu, Z., Jansen, B.J.: Factors influencing the response rate in social question and answering behavior. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work (CSCW 2013), pp. 1263–1274. ACM, New York (2008)
Harper, F.M., Raban, D., Rafaeli, S., Konstan, J.A.: Predictors of answer quality in online Q&A sites. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2008), pp. 865–874. ACM, New York (2008)
Graesser, A.C., Cai, Z., Louwerse, M.M., Daniel, F.: Question Understanding Aid (QUAID) A Web Facility that Tests Question Comprehensibility. Public Opinion Quarterly 70(1), 3–22 (2006)
Dillman, D.A.: Mail and internet surveys: The tailored design method, vol. 2. Wiley, New York (2000)
Yue, Z., Han, S., He, D.: Modeling search processes using hidden states in collaborative exploratory web search. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 820–830 (2014)
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Hsiao, IH., Han, S., Malhotra, M., Chae, H.S., Natriello, G. (2014). Survey Sidekick: Structuring Scientifically Sound Surveys. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 8474. Springer, Cham. https://doi.org/10.1007/978-3-319-07221-0_65
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DOI: https://doi.org/10.1007/978-3-319-07221-0_65
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07220-3
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