Defining the Ill-Defined: From Abstract Principles to Applied Pedagogy (bibtex)
by Benjamin D. Nye, Michael W. Boyce, Robert Sottilare
Abstract:
Attempts to define ill-defined domains in intelligent tutoring system (ITS) research has been approached a number of times (Fournier-Viger, Nkambou, & Nguifo, 2010; Lynch, Ashley, Pinkwart, & Aleven, 2009; Mitrovic & Weerasinghe, 2009; Jacovina, Snow, Dai, & McNamara, 2015; Woods, Stensrud, Wray, Haley, & Jones, 2015). Related research has tried to determine levels of ill-definedness for a domain (Le, Loll, & Pinkwart, 2013). Despite such attempts, the field has not yet converged on common guidelines to distinguish between well-defined versus ill-defined domains. We argue that such guidelines struggle to converge because a domain is too large to meaningfully categorize: every domain contains a mixture of well-defined and ill-defined tasks. While the co-existence of well-defined and ill-defined tasks in a single domain is nearly universally-agreed upon by researchers; this key point is often quickly buried by an extensive discussion about what makes certain domain tasks ill-defined (e.g., disagreement about ideal solutions, multiple solution paths). In this chapter, we first take a step back to consider what is meant by a domain in the context of learning. Next, based on this definition for a domain, we map out the components that are in a learning domain, since each component may have ill-defined parts. This leads into a discussion about the strategies that have been used to make ill-defined domains tractable for certain types of pedagogy. Examples of ITS research that applies these strategies are noted. Finally, we conclude with practical how-to considerations and open research questions for approaching ill-defined domains. This chapter should be considered a companion piece to our chapter in the prior volume of this series (Nye, Goldberg, & Hu, 2015). This chapter focuses on how to understand and transform ill-defined parts of domains, while the prior chapter discusses commonly-used learning tasks and authoring approaches for both well-defined and ill-defined tasks. As such, this chapter is intended to help the learner understand if and how different parts of the domain are ill-defined (and what to do about them). The companion piece in the authoring tools volume discusses different categories of well and ill-defined tasks, from the standpoint of attempting to author and maintain an ITS.
Reference:
Defining the Ill-Defined: From Abstract Principles to Applied Pedagogy (Benjamin D. Nye, Michael W. Boyce, Robert Sottilare), Chapter in Design Recommendations for Intelligent Tutoring Systems: Volume 4-Domain Modeling, US Army Research Laboratory, volume 4, 2016.
Bibtex Entry:
@incollection{nye_defining_2016,
	address = {Orlando, FL},
	title = {Defining the {Ill}-{Defined}: {From} {Abstract} {Principles} to {Applied} {Pedagogy}},
	volume = {4},
	isbn = {978-0-9893923-9-6},
	url = {https://gifttutoring.org/attachments/download/1736/Design%20Recommendations%20for%20ITS_Volume%204%20-%20Domain%20Modeling%20Book_web%20version_final.pdf},
	abstract = {Attempts to define ill-defined domains in intelligent tutoring system (ITS) research has been approached a number of times (Fournier-Viger, Nkambou, \& Nguifo, 2010; Lynch, Ashley, Pinkwart, \& Aleven, 2009; Mitrovic \& Weerasinghe, 2009; Jacovina, Snow, Dai, \& McNamara, 2015; Woods, Stensrud, Wray, Haley, \& Jones, 2015). Related research has tried to determine levels of ill-definedness for a domain (Le, Loll, \& Pinkwart, 2013). Despite such attempts, the field has not yet converged on common guidelines to distinguish between well-defined versus ill-defined domains. We argue that such guidelines struggle to converge because a domain is too large to meaningfully categorize: every domain contains a mixture of well-defined and ill-defined tasks. While the co-existence of well-defined and ill-defined tasks in a single domain is nearly universally-agreed upon by researchers; this key point is often quickly buried by an extensive discussion about what makes certain domain tasks ill-defined (e.g., disagreement about ideal solutions, multiple solution paths).
In this chapter, we first take a step back to consider what is meant by a domain in the context of learning. Next, based on this definition for a domain, we map out the components that are in a learning domain, since each component may have ill-defined parts. This leads into a discussion about the strategies that have been used to make ill-defined domains tractable for certain types of pedagogy. Examples of ITS research that applies these strategies are noted. Finally, we conclude with practical how-to considerations and open research questions for approaching ill-defined domains.
This chapter should be considered a companion piece to our chapter in the prior volume of this series (Nye, Goldberg, \& Hu, 2015). This chapter focuses on how to understand and transform ill-defined parts of domains, while the prior chapter discusses commonly-used learning tasks and authoring approaches for both well-defined and ill-defined tasks. As such, this chapter is intended to help the learner understand if and how different parts of the domain are ill-defined (and what to do about them). The companion piece in the authoring tools volume discusses different categories of well and ill-defined tasks, from the standpoint of attempting to author and maintain an ITS.},
	booktitle = {Design {Recommendations} for {Intelligent} {Tutoring} {Systems}: {Volume} 4-{Domain} {Modeling}},
	publisher = {US Army Research Laboratory},
	author = {Nye, Benjamin D. and Boyce, Michael W. and Sottilare, Robert},
	month = jul,
	year = {2016},
	keywords = {Learning Sciences, UARC, ARL, DoD},
	pages = {19--37}
}
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