Semi-Automated Correction Tools for Mathematics-Based Exercises in MOOC Environments
DOI:
https://doi.org/10.9781/ijimai.2015.3312Keywords:
e-learning, Automatic, Education, LatexAbstract
Massive Open Online Courses (MOOCs) allow the participation of hundreds of students who are interested in a wide range of areas. Given the huge numbers enrolled, it is almost impossible to give complex homework to students and have it carefully corrected and reviewed by a tutor or assistant professor. In this paper, we present a software framework that aims at assisting teachers in MOOCs during correction tasks for mathematics exercises. This framework might suit maths, physics or technical subjects. As a test experience, we apply it to 300+ physics homework bulletins from 80+ students. Test results show our solution can prove very useful in guiding assistant teachers during correction shifts.Downloads
References
[1] John M Aiken, Shih-Yin Lin, Scott S Douglas, Edwin F Greco, Brian D Thoms, Michael F Schatz, and Marcos D Caballero. The initial state of students taking an introductory physics mooc. arXiv preprint arXiv:1307.2533, 2013.
[2] Ron Ausbrooks, Stephen Buswell, David Carlisle, Giorgi Chavchanidze, Stéphane Dalmas, Stan Devitt, Angel Diaz, Sam Dooley, Roger Hunter, Patrick Ion, Michael Kohlhase, Azzeddine Lazrek, Paul Libbrecht, Bruce Miller, Robert Miner, Murray Sargent, Bruce Smith, Neil Soiffer, Robert Sutor, and Stephen Watt. Mathematical Markup Language (MathML) version 3.0. W3C Recommendation, World Wide Web Consortium, 2010.
[3] Marcos Cramer, Peter Koepke, and Bernhard Schröder. Parsing and disambiguation of symbolic mathematics in the Naproche system. Proceedings of the 18th Calculemus and 10th International Conference on Intelligent Computer Mathematics, 2011.
[4] J.H. Davenport, W.M. Farmer, F. Rabe, and J. Urban. Intelligent Computer Mathematics: 18th Symposium, Calculemus, and 10th International Conference on Artificial intelligence, 2011.
[5] Luis de-la Fuente-Valentín, Aurora Carrasco, Kinga Konya, and Daniel Burgos. Emerging technologies landscape on education. a review. International Journal of Interactive Multimedia and Artificial Intelligence, IJIMAI, 2(3), pp.55, 2013.
[6] Mohan Ganesalingam. The Language of Mathematics. PhD thesis, Cambridge University, 2009.
[7] Deyan Ginev. LaTeXML: A LATEX to XML converter, arXMLiv branch. Web Manual. 2011.
[8] Deyan Ginev. The LaTeXml daemon: Editable math for the collaborative web.
[9] Deyan Ginev. The structure of mathematical expressions. PhD thesis, Master Thesis, Jacobs University Bremen, 2011.
[10] Deyan Ginev, Constantin Jucovschi, Stefan Anca, Mihai Grigore, Catalin David, and Michael Kohlhase. An architecture for linguistic and semantic analysis on the arXMLiv corpus. Applications of Semantic Technologies Workshop, 2009.
[11] Helmut Horacek and Magdalena Wolska. Handling errors in mathematical formulas, Lecture Notes in Computer Science, 2006.
[12] Muhammad Humayoun and Christophe Raffalli. Mathnat: mathematical text in a controlled natural language. 9th International Conference on Mathematical Knowledge Management, 2010
[13] Fairouz Kamareddine. Motivations for mathlang, T-O-U Workshop, 2005.
[14] David Kastrup. Revisiting WYSIWYG paradigms for authoring LaTeX. Proceedings of the 2002 Annual Meeting, TUGboat, 2002.
[15] D. Kirsch. Detexify: Erkennung handgemalter LaTeX-symbole. PhD thesis, Westfälische Wilhelms-Universität Münster, 2010.
[16] Lukas Kohlhase and Michael Kohlhase. System description: A semantics-aware LaTeX-to-office converter. Intelligent Computer Mathematics, 2014
[17] Michael Kohlhase, Bogdan A. Matican, and Corneliu-Claudiu Prodescu. Mathwebsearch 0.5: Scaling an open formula search engine. Lecture Notes in Computer Science, 2012.
[18] Michael Kohlhase and Ioan Sucan. A search engine for mathematical formulae, Artificial Intelligence and Symbolic Computation, 2006.
[19] Jacco Krijnen, Doaitse Swierstra, and Marcos O.Viera: Towards an extensible Pandoc, Lecture Notes in Computer Science, 2014.
[20] John MacFarlane. Pandoc user’s guide. http://johnmacfarlane.net/pandoc/README.html, 2013.
[21] Arnold Neumaier and Peter Schodl. A framework for representing and processing arbitrary mathematics. KEOD, 2010.
[22] Jiri Rakosnik and Radoslav Pavlov. European digital mathematics library. Digital Presentation and Preservation of Cultural and Scientific Heritage, 2013.
[23] Lucía Romero, Milagros Gutiérrez, and María Laura Caliusco. Conceptualizing the e-learning assessment domain using an ontology network. International Journal of Interactive Multimedia and Artificial Intelligence, IJIMAI, 1(6), pp. 20, 2012.
[24] Heinrich Stamerjohanns and Michael Kohlhase. Transforming the arxiv to XML. In Intelligent Computer Mathematics, 2008.
[25] Martin Thoma. On-line Recognition of Handwritten Mathematical Symbols. PhD thesis, National Research Center, 2014.
[26] Edward Rolando Núñez Valdez, Juan Manuel Cueva Lovelle, Óscar Sanjuán Martínez, Carlos E Montenegro Marín, and Guillermo Infante Hernandez. Social voting techniques: a comparison of the methods used for explicit feedback in recommendation systems. International Journal of Artificial intelligence, IJIMAI, 1(2), pp.79, 2011.
[27] Magdalena Wolska and Ivana Kruijff-KorBayova. Analysis of mixed natural and symbolic input in mathematical dialogs. 42nd Meeting of the Association for Computational Linguistics, 2004.
[28] Richard Zanibbi and Dorothea Blostein. Recognition and retrieval of mathematical expressions. International Journal on Document Analysis and Recognition, 2012.
[29] ClausZinn. A computational framework for understanding mathematical discourse. IGPL, 2003.
[30] L. de-la Fuente-Valentín, D. Burgos, R. González Crespo. A4Learning: Alumni Alike Activity Analytics to Self-Assess Personal Progress. IEEE International Conference on Advanced Learning Technologies, 2014.
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