Abstract
Analyzing the data that is collected in a knowledge survey serves the teacher for determining the student’s learning needs at the beginning of the course and for finding a relationship between these needs and the capacities acquired during the course. In this paper we propose using graphical exploratory analysis for projecting all the data in a map, where each student will be placed depending on his/her knowledge profile, allowing the teacher to identify groups with similar background problems, segment heterogeneous groups and perceive the evolution of the abilities acquired during the course.
The main innovation of our approach consists in regarding the answers of the tests as imprecise data. We will consider that either a missing or unknown answer, or a set of conflictive answers to a survey, is best represented by an interval or a fuzzy set. This representation causes that each individual in the map is no longer a point but a figure, whose shape and size determine the coherence of the answers and whose position with respect to its neighbors determine the similarities and differences between the students.
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References
Alcala-Fdez, L., et al.: KEEL: A Software Tool to Assess Evolutionary Algorithms to Data Mining Problems. Soft Computing 13(3), 307–318 (2009)
Cohen, P.A.: Student ratings of instruction and student achievement: a meta-analysis of multisection validity studies. Review of Educational Research 51(3), 281–309 (1981)
Denoeux, T., Masson, M.-H.: Multidimensional scaling of interval-valued dissimilarity data. Pattern Recognition Lett. 21, 83–92 (2000)
Hebert, P.A., Masson, M.H., Denoeux, T.: Fuzzy multidimensional scaling. Computational Statistics and Data Analysis 51, 335–359 (2006)
Knipp, D.: Knowledge surveys: What do students bring to and take from a class? United States Air Force Academy Educator (Spring 2001)
Nuhfer, E.: Bottom-Line Disclosure and Assessment. Teaching Professor 7(7), 8–16 (1993)
Romero, C., Ventura, S., Garca, E.: Data mining in course management systems: Moodle case study and tutorial. Computers & Education 51(1), 368–384 (2008)
Sanchez, L., Couso, I., Casillas, J.: Modeling vague data with genetic fuzzy systems under a combination of crisp and imprecise criteria. In: MCDM 2007, Honolulu, Hawaii, USA (2007)
Wirth, K., Perkins, D.: Knowledge Surveys: The ultimate course design and assessment tool for faculty and students. In: Proceedings: Innovations in the Scholarship of Teaching and Learning Conference, April 1-3, p. 19. St. Olaf College/Carleton College (2005)
Nagel, L., Kotz, T.: Supersizing e-learning: What a CoI survey reveals about teaching presence in a large online class. The Internet and Higher Education (2009)
Zeki Saka, A.: Hitting two birds with a stone: Assessment of an effective approach in science teaching and improving professional skills of student teachers. Social and Behavioral Sciences 1(1), 1533–1544 (2009)
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Sánchez, L., Couso, I., Otero, J. (2010). Graphical Exploratory Analysis of Educational Knowledge Surveys with Missing and Conflictive Answers Using Evolutionary Techniques. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13803-4_6
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DOI: https://doi.org/10.1007/978-3-642-13803-4_6
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