Abstract
In this work we focus on immediate feedback during technology enhanced assessment. We distinguish two types of feedback: just after answering a test item or just after the completion of a test (cumulative feedback). We identified three challenges related to generation of formative feedback: (1) the lack of information about the user at beginning of the test; (2) the identification of features for the feedback generation on the item level, (3) generation of formative cumulative feedback from limited contextual information. We approach these challenges by creating a user model incrementally from observed user behavior. The conceptual model is validated in an e-learning platform EAGLE targeting information literacy, ICT literacy, and change management in an adult professional learning environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amaral, L., Meurers, D.: From recording linguistic competence to supporting inferences about language acquisition in context. CALL 21(4), 323–338 (2008)
Foulonneau, M., Ras, E.: Automatic item generation: new perspectives using open educational resources and semantic web. Int. J. E-Assess. 1 (2014)
Greiff, S., Wüstenberg, S., Avvisati, F.: Computer-generated log-file analyses as a window into students’ minds? a showcase study based on the PISA 2012 assessment of problem solving. Comput. Educ. 91, 92–105 (2015)
Katerina, T., Nicolaos, P., Charalampos, Y.: Mouse tracking for web marketing: enhancing user experience in web application software by measuring self-efficacy and hesitation levels. Int. J. Strateg. Innovative Mark. 1, 233–247 (2014)
Klapproth, F., Krolak-Schwerdt, S., Hörstermann, T., Schaltz, P.: Leistungstestwerte als Validierungskriterium für die prognostische Validität von Schullaufbahnempfehlungen: Ein neuer formaler Ansatz. Emp. Päd. 27, 206–225 (2013)
Lipowsky, F.: Unterricht. In: Wild, E., Möller, J. (eds.) Pädagogische Psychologie. Springer-Lehrbuch, pp. 69–105. Springer, Heidelberg (2015)
Narciss, S.: Designing and evaluating tutoring feedback strategies for digital learning. Digital Educ. Rev. 23, 7–26 (2013)
Reichert, M.: The validity of the C-test revisited: findings from a multilingual environment. Ph.D. thesis, University of Luxembourg (2011)
Rich, E.: Users are individuals: individualizing user models. Int. J. Man Mach. Stud. 18(3), 199–214 (1983)
Shute, V.J.: Focus on formative feedback. Rev. Educ. Res. 78(1), 153–189 (2008)
Wiliam, D.: Embedded Formative Assessment. Solution Tree Press, Bloomington, United States (2011)
Wilson, M.: Structured constructs models (SCM): a family of statistical models related to learning progressions. In: Learning Progressions in Science (LeaPS) Conference, Iowa City, IA (2009)
Acknowledgments
The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement N619347.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Höhn, S., Ras, E. (2016). Designing Formative and Adaptive Feedback Using Incremental User Models. In: Chiu, D., Marenzi, I., Nanni, U., Spaniol, M., Temperini, M. (eds) Advances in Web-Based Learning – ICWL 2016. ICWL 2016. Lecture Notes in Computer Science(), vol 10013. Springer, Cham. https://doi.org/10.1007/978-3-319-47440-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-47440-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47439-7
Online ISBN: 978-3-319-47440-3
eBook Packages: Computer ScienceComputer Science (R0)