Abstract
This article describes efforts to offer automated assessment of students within an exploratory learning environment. We present a regression model that estimates student assessments in an ill-defined medical diagnosis tutor called Rashi. We were pleased to find that basic features of a student’s solution predicted expert assessment well, particularly when detecting low-achieving students. We also discuss how expert knowledge bases might be leveraged to improve this process. We suggest that developers of exploratory learning environments can leverage this technique with relatively few extensions to a mature system. Finally, we describe the potential to utilize this information to direct teachers’ attention towards students in need of help.
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
Preview
Unable to display preview. Download preview PDF.
References
Ting, C.-Y., Phon-Amnuaisuk, S.: Optimal dynamic decision network model for scientific inquiry learning environment. Applied Intelligence 33(3), 387–406 (2010)
Fournier-Viger, P., Nkambou, R., Nguifo, E.M.: Exploiting Partial Problem Spaces Learned to Provide Key Tutoring Services in Procedural and Ill-Defined Domains. Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling 200, 383 (2009)
Dragon, T., Park Woolf, B., Marshall, D., Murray, T.: Coaching within a domain independent inquiry environment. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 144–153. Springer, Heidelberg (2006)
Walker, Erin, et al.: Two approaches for providing adaptive support for discussion in an ill-defined domain. Intelligent Tutoring Systems for Ill-Defined Domains: Assessment and Feedback in Ill-Defined Domains, 1 (2008)
Mitrovic, A., Weerasinghe, A.: Revisiting ill-denedness and the consequences for ITSs. Artificial intelligence in Education: Building Learning Systems that Care from Knowledge Representation to Affective Modelling, 375–382 (2009)
Graesser, A.C., et al.: Using latent semantic analysis to evaluate the contributions of students in AutoTutor. Interactive Learning Environments 8(2), 129–147 (2000)
Mavrikis, M., et al.: Design requirements, student perception indicators and validation metrics for intelligent exploratory learning environments. Personal and Ubiquitous Computing 17(8), 1605–1620 (2013)
Lynch, C., et al.: Defining ill-defined domains; a literature survey. In: Proceedings of the Workshop on Intelligent Tutoring Systems for Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems (2006)
Davoodi, A., Kardan, S., Conati, C.: Understanding Users Interaction Behavior with an Intelligent Educational Game: Prime Climb. In: AIED 2013 Workshops Proceedings Volume 2 Scaffolding in Open-Ended Learning Environments (OELEs), p. 9 (2013)
Kinnebrew, J.S., Segedy, J.R., Biswas, G.: Analyzing the temporal evolution of students behaviors in open-ended learning environments. Metacognition and learning 9(2), 187–215 (2014)
Segedy, J., Biswas, G., Sulcer, B.: A Model-Based Behavior Analysis Approach for Open-Ended Environments. Educational Technology and Society 17(1), 272–282 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Floryan, M., Dragon, T., Basit, N., Dragon, S., Woolf, B. (2015). Who Needs Help? Automating Student Assessment Within Exploratory Learning Environments. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-19773-9_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19772-2
Online ISBN: 978-3-319-19773-9
eBook Packages: Computer ScienceComputer Science (R0)