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Plan Recognition and Visualization in Exploratory Learning Environments

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Educational Data Mining

Part of the book series: Studies in Computational Intelligence ((SCI,volume 524))

Abstract

Exploratory Learning Environments (ELEs) are open-ended software in which students build scientific models and examine properties of the models by running them and analyzing the results (Amershi and Conati, Intelligent tutoring systems. LNCS. Springer, Heidelberg, 463–472, 2006); Chen (Instr Sci, 23(1–3):183–220, 1995); (Cocea et al., 2008). ELEs are generally used in classes too large for teachers to monitor all students and provide assistance when needed (Gal et al., 2008). They are also becoming increasingly prevalent in developing countries where access to teachers and other educational resources is limited (Pawar et al., 2007). Thus, there is a need to develop tools of support for teachers’ understanding of students’ activities. This chapter presents methods for addressing these needs. It presents an efficient algorithm for intelligently recognizing students’ activities, and novel visualization methods for presenting these activities to teachers. Our empirical analysis is based on an ELE for teaching chemistry that is used by thousands of students in colleges and high schools in several countries (Yaron et al., Science, 328(5978), 584–585, 2010).

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Notes

  1. 1.

    In chemistry, ‘M’ denotes the measure of Molar concentration of a substance.

  2. 2.

    Intermediate flasks are commonly used in Virtual Labs to help measure solutions accurately, as in a physical laboratory.

  3. 3.

    For brevity, we omit the recipes for the MSI action. The complete recipe library for the dilution problem can be found in Sect. 11.8.

  4. 4.

    We used the Prefuse package to implement this application Heer et al. [45].

  5. 5.

    We also asked participants to explain each of their answers. The full questionnaire can be found in Sect. 11.9.

  6. 6.

    The researcher was physically present in the laboratory with all of the graduate students from Ben-Gurion University, and used VoIP technology (Skype) to connect with the other three participants.

Abbreviations

AI:

Artificial intelligence

CCD:

Create correct device action

ELEs:

Exploratory learning environments

ITS:

Intelligent tutoring systems

MS:

Mix solution

MSC:

Mixing solution components

MSI:

Mixing the solution using an intermediate flask

SDP:

Solving the dilution problem

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Author information

Authors and Affiliations

  1. Harvard University, School of Engineering and Applied Sciences, 29 Oxford Street, Cambridge, MA, 02138, USA

    Ofra Amir

  2. Department of Information Systems Engineering Ben-Gurion Boulevard, Ben-Gurion University, Building 93, Beer Sheva, 84105, Israel

    Kobi Gal

  3. Department of Chemistry, Carnegie-Melon University, 4400 Fifth Avenue, Pittsburgh, PA, 15213, USA

    David Yaron & Michael Karabinos

  4. Department of Chemistry and Biochemistry, University of Arkansas, A 119 Chemistry Building, Fayetteville, AR, 72701, USA

    Robert Belford

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  1. Ofra Amir
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  2. Kobi Gal
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  3. David Yaron
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  4. Michael Karabinos
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  5. Robert Belford
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Correspondence to Ofra Amir .

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  1. Escuela Superior de Ingeniería Mecánica y Eléctrica, Zacatenco (ESIME-Z), World Outreach Light to the Nations Ministries (WOLNM), Instituto Politécnico Nacional (IPN), Gustavo A. Madero, Mexico City, Distrito Federal, Mexico

    Alejandro Peña-Ayala

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Amir, O., Gal, K., Yaron, D., Karabinos, M., Belford, R. (2014). Plan Recognition and Visualization in Exploratory Learning Environments. In: Peña-Ayala, A. (eds) Educational Data Mining. Studies in Computational Intelligence, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-319-02738-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-02738-8_11

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