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Issue No.01 - Jan.-March (2012 vol.5)
pp: 52-61
N. Pattanasri , Dept. of Social Inf., Kyoto Univ., Kyoto, Japan
M. Mukunoki , Acad. Center for Comput. & Media Studies, Kyoto Univ., Kyoto, Japan
M. Minoh , Acad. Center for Comput. & Media Studies, Kyoto Univ., Kyoto, Japan
Comprehension assessment is an essential tool in classroom learning. However, the judgment often relies on experience of an instructor who makes observation of students' behavior during the lessons. We argue that students should report their own comprehension explicitly in a classroom. With students' comprehension made available at the slide level, we apply a machine learning technique to classify presentation slides according to comprehension levels. Our experimental result suggests that presentation-based features are as predictive as bag-of-words feature vector which is proved successful in text classification tasks. Our analysis on presentation-based features reveals possible causes of poor lecture comprehension.
support vector machines, educational administrative data processing, learning (artificial intelligence), pattern classification, psychology, poor lecture comprehension, slide comprehension estimation, support vector machine, comprehension assessment, classroom learning, student comprehension, machine learning technique, presentation slide classification, comprehension level, presentation-based features, bag-of-words feature vector, text classification, Support vector machines, Machine learning, Materials, Training, Accuracy, Kernel, Feature extraction, SVM., Lecture analytics, lecture comprehension, learning skills
N. Pattanasri, M. Mukunoki, M. Minoh, "Learning to Estimate Slide Comprehension in Classrooms with Support Vector Machines", IEEE Transactions on Learning Technologies, vol.5, no. 1, pp. 52-61, Jan.-March 2012, doi:10.1109/TLT.2011.22
[1] A. Anand and P.N. Suganthan, "Multiclass Cancer Classification by Support Vector Machines with Class-Wise Optimized Genes and Probability Estimates," J. Theoretical Biology, vol. 259, no. 3, pp. 533-540, 2009.
[2] V.R. Carvalho and W.W. Cohen, "On the Collective Classification of Email "Speech Acts," Proc. 28th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 345-352, 2005.
[3] N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer, "SMOTE: Synthetic Minority Over-Sampling Technique," J. Artificial Intelligence Research, vol. 16, no. 1, pp. 321-357, 2002.
[4] F. Coffield, D. Moseley, E. Hall, and K. Ecclestone, "Learning Styles and Pedagogy in Post-16 Learning: A Systematic and Critical Review," technical report, Learning and Skills Research Centre, 2004.
[5] C. Cortes and V. Vapnik, "Support-Vector Networks," J. Machine Learning, vol. 20, no. 3, pp. 273-297, Sept. 1995.
[6] R.M. Felder and L.K. Silverman, "Learning and Teaching Styles in Engineering Education," J. Eng. Education, vol. 78, no. 7, pp. 674-681, 1988.
[7] N. Fleming, VARK: A Guideline to Learning Styles, http:/vark-learn. com, 2004.
[8] S. Godbole and S. Roy, "Text Classification, Business Intelligence, and Interactivity: Automating C-Sat Analysis for Services Industry," Proc. 14th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 911-919, 2008.
[9] N. Jindal and B. Liu, "Opinion Spam and Analysis," Proc. Int'l Conf. Web Search and Web Data Mining, pp. 219-230, 2008.
[10] J.R. Kirby, P.J. Moore, and N.J. Schofield, "Verbal and Visual Learning Styles," Contemporary Educational Psychology, vol. 13, no. 2, pp. 169-184, 1988.
[11] D.A. Kolb, Experiential Learning: Experience as a Source of Learning and Development. Prentice Hall, 1984.
[12] M. Kozhevnikov, "Cognitive Styles in the Context of Modern Psychology: Toward an Integrated Framework of Cognitive Style," Psychological Bull., vol. 133, no. 3, pp. 464-481, 2007.
[13] L.S. Larkey, "Automatic Essay Grading Using Text Categorization Techniques," Proc. 21st Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 90-95, 1998.
[14] L.J. Massa and R.E. Mayer, "Testing the ATI Hypothesis: Should Multimedia Instruction Accommodate Verbalizer-Visualizer Cognitive Style?" Learning and Individual Differences, vol. 16, no. 4, pp. 321-335, 2006.
[15] Y. Matsumoto et al., Morphological Analysis System ChaSen version 2.2.4 Manual, chasen-2.2.4.pdf, 2001.
[16] S. Neill and C. Caswell, Body Language for Competent Teachers. Routledge, 2004.
[17] E.B. Page, "Computer Grading of Student Prose, Using Modern Concepts and Software," J. Experimental Education, vol. 62, pp. 127-142, 1994.
[18] B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs Up?: Sentiment Classification Using Machine Learning Techniques," Proc. Conf. Empirical Methods in Natural Language Processing, pp. 79-86, 2002.
[19] H. Pashler, M. McDaniel, D. Rohrer, and R. Bjork, "Learning Styles: Concepts and Evidence," Psychological Science in the Public Interest, vol. 9, no. 3, pp. 105-119, 2008.
[20] R.P. Schumaker and H. Chen, "Textual Analysis of Stock Market Prediction Using Breaking Financial News: The AZFin Text System," ACM Trans. Information Systems, vol. 27, no. 2, pp. 1-19, 2009.
[21] F. Sebastiani, "Machine Learning in Automated Text Categorization," J. ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.
[22] L. Valenzeno, M.W. Alibali, and R. Klatzky, "Teachers' Gestures Facilitate Students' Learning: A Lesson in Symmetry," Contemporary Educational Psychology, vol. 28, pp. 187-204, 2003.
[23] I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, second ed. Morgan Kaufmann, 2005.
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