Superior Factors to Distinguish Students to Be Cared in Introductory Programming Education

  • Hiromitsu Shimakawa Ritsumeikan University
  • Dinh Thi Dong Phuong Ritsumeikan University
Keywords: contextual inquiry, learning status, learning behavior, motivation and learning strategies, persona

Abstract

Every student has its own motivation and learning strategies, which conform a learning status of the student. Appropriate supervision according to the learning status contributes to improvement of the learning of each student. Many of existing works try to figure out learning status directly from observable learning behavior. This paper proposes to utilize internal factors consisting of learning motivation and strategies, to distinguish learning status of students. It presents a way to derive the internal factors from records collected from their usual learning behavior, using the similarity of students over successive years. The experiment results indicates the strong possibility of the distinction from learning behavior. It implies the feasibility of immediate distinction of learning status of students, which enables efficient allocation of teaching power on the spot.

References

H. Beyer and K. Holtzblatt, “Contextual Design: Defining Customer-Centered Systems,” Morgan Kaufmann, 1998.

A.Cooper, “The Inmates are Running the Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity,” Pearson, 2004.

W.Dick, “The Systematic Design of Instruction,” Plentice Hall, 2008.

Dinh Thi Dong Phuong, and Hiromtisu Shimakawa, “Superior Factors to Predict Learning Status,” Proc. of LTLE2015, pp.307-312.

Renaud Gaujoux, and Cathal Seoighe, “A flexible R package for nonnegative matrix factorization,” BMC Bioinformatics, Vol.11, No.1, p. 367. 2010.

S. Graf, C. Ives, N. Rahman, and A. Ferri, “AAT: A Tool for Accessing and Analyzing Students Behavior Data in Learning Systems,” Proc. of LAK, pp.174-179, 2011.

John M Keller, “Motivational Design for Learning and Performance: The ARCS Model Approach,” Springer, 2010.

P.Kinnunen, and L.Malmi, “Why Students Drop Out CS1 Course?,” Proc.of ICERE6, pp.997-108, 2006.

P.Kleinginna, Jr., and A.Kleinginna, “A categorized list of motivation definitions with suggestions for a consensual definition,” Motivation and Emotion, 5, pp.263-291, 1981

MM.McGill, “Learning to program with personal robots: Influences on student motivation,” ACM Trans. Computing Education, Vol.12, No.1, Article no.4, 2012.

P.R.Pintrich, D.A.F.Smith, T.Garcia, and W.J.McKeachie, “A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ),” Nat’l Center for Research to Improve Post secondary Teaching and Learning, 1991.

Robert A. Reiser, “Trends and Issues in Instructional Design and Technology,” Pearson, 2012.

D.H.Schunk, and B.J.Zimmerman, Self-regulated learning: From teaching to selfreflective practice, Guilford Press, 1998.

T.Segaran, “Programming Collective Intelligence,” O’reilly, 2007

R.E.Slavin, “Educational psychology: Theory and Practice,” 10th, Edition, Pearson, 2006.

Published
2016-03-31
Section
Technical Papers (Information and Communication Technology)