Enhancing Competence and Autonomy in Computer-Based Instruction Using a Skill-Challenge Balancing Strategy

Abstract

The present study aims to show if a skill-challenge balancing (SCB) instruction strategy can assist learners to motivationally engage in computer-based learning. Csikszentmihalyi's flow theory (self-control, curiosity, focus of attention, and intrinsic interest) was applied to an account of the optimal learning experience in SCB-based learning activities. Two empirical studies were carried out, where a group of learners were taught “Computer Networks” as part of a statutory curriculum at a tertiary institution. The empirical results suggested that a degree of self-control to compensate for the fully automatic SCB instruction strategy (i.e., competence and autonomy) would be of a greater value for learning motivation enhancement in adaptive computer-based learning systems.

Get full access to this article

View all access options for this article.

References

Bransford

Brown

Cocking

(2000). How people learn: Brain, mind, and experience & school. Washington, DC: National Academy Press.

Chauncey

Azevedo

(2010). Emotions and motivation on performance during multimedia learning: How do I feel and why do I care? In Aleven

Kay

Mostow

(Eds.), ITS 2010, Part I, LNCS 6094 (pp. 369–378). Berlin Heidelberg: Springer-Verlag.

Chen

(2008). Intelligent web-based learning system with personalized learning path guidance. Computers & Education, 51, 787–814.

Chen

K.-C.

Jang

S.-J.

(2010). Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behaviour, 26, 741–752.

Cooper

G. F.

Herskovits

(1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4), 309–347.

Csikszentmihalyi

(1990). Flow: The psychology of optimal experience. New York, NY: Harper & Row.

Dabbagh

Kitsantas

(2005). Using web-based pedagogical tools as scaffolds for self-regulated learning. Instructional Science, 33(5–6), 513–540.

deCharms

(1976). Enhancing motivation: Change in the classroom. New York, NY: Irvington.

Deci

E. L.

Eghrari

Patrick

B. C.

Leone

(1994). Facilitating internalization: The self-determination theory perspective. Journal of Personality, 62(1), 119–142.

10.

Deci

E. L.

Ryan

R. M.

(1985). Intrinsic motivation and self-determination in human behavior. New York, NY: Plenum.

11.

Deci

E. L.

Ryan

R. M.

(2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268.

12.

del Soldato

du Boulay

(1995). Implementation of motivational tactics in tutoring systems. Journal of Artificial Intelligence in Education, 6(4), 337–378.

13.

Everitt

Landau

Leese

(2001). Cluster analysis. London: Arnold.

14.

Fisher

W. D.

(1958). On grouping for maximum homogeneity. Journal of the American Statistical Association, 53(284), 789–798.

15.

Gagliardi

(2011). Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction. Artificial Intelligence in Medicine, 52(3), 123–139.

16.

Garris

Ahlers

Driskell

J. E.

(2002). Games, motivation, and learning: A research and practice model. Simulation & Gaming, 33, 441–467.

17.

Gopalakrishnan

Lustgarten

J. L.

Visweswaran

Cooper

G. F.

(2010). Bayesian rule learning for biomedical data mining. Bioinformatics, 26(5), 668–675.

18.

Gunawardena

C. N.

(1995). Social presence theory and implications for interaction and collaborative learning in computer conferences. International Journal of Educational Telecommunications, 1(2), 147–166.

19.

Guthrie

McRae

Klauda

(2007). Contributions of concept-oriented reading instruction to knowledge about interventions for motivations in reading. Educational Psychologist, 42, 237–250.

20.

Hadwin

A. F.

Wozney

Pantin

(2005). Scaffolding the appropriation of self-regulatory activity: A socio-cultural analysis of changes in teacher-student discourse about a graduate research portfolio. Instructional Science, 33, 413–450.

21.

Hair

J. F.

Anderson

R. E.

Tatham

R. L.

Black

W. C.

(1995). Multivariate data analysis (4th ed.). New Jersey: Prentice-Hall.

22.

Hativa

Lesgold

(1991). The computer as a tutor—Can it adapt to the individual learner? Instructional Science, 20(1), 49–78.

23.

Holmes

Gardner

(2006). E-learning: Concept and practice. London, UK: Sage.

24.

Howland

J. L.

Moore

J. L.

(2002). Student perceptions as distance learners in Internet-based courses. Distance Education, 23(2), 183–195.

25.

Johnson

G. M.

(2005). Student alienation, academic achievement, and WebCT use. Educational Technology & Society, 8(2), 179–189.

26.

Kalyuga

(2005). Prior knowledge principle in multimedia learning. In Mayer

R. E.

(Ed.), The Cambridge handbook of multimedia learning (pp. 325–357). New York, NY: Cambridge University Press.

27.

Katuk

Kim

Ryu

(2013). Experience beyond knowledge: Pragmatic e-learning systems design with learning experience. Computers in Human Behaviour, 29, 747–758.

28.

Kwak

(2013). Constructs of student-centered online learning on learning satisfaction of a diverse online student body: A structural equation modeling approach. Journal of Educational Computing Research, 48(1), 97–122.

29.

Keller

J. M.

(2008). First principles of motivation to learning and e-learning. Distance Education, 29(2), 175–185.

30.

Kim

Lee

Ryu

H. K.

(2013). Personality and its effects on learning performance: Design guidelines for an adaptive e-learning system based on a user model. International Journal of Industrial Ergonomics, 43(5), 450–461.

31.

Kirriemuir

McFarlane

(2004). Literature review in games and learning. FutureLab report, Bristol: Future Lab.

32.

Koehler

Thompson

Phye

(2011). A design study of a multimedia instructional grammar program with embedded tracking. Instructional Science, 39(6), 939–974.

33.

Konstantopoulos

Chung

(2009). What are the long-term effects of small classes on the achievement gap? Evidence from the lasting benefits study. American Journal of Education, 116(1), 125–154.

34.

Kopcha

Sullivan

(2008). Learner preferences and prior knowledge in learner-controlled computer-based instruction. Educational Technology Research and Development, 56(3), 265–286.

35.

Kreijns

Kirschner

P. A.

Jochems

(2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: A review of the research. Computers in Human Behavior, 19(3), 335–353.

36.

Malone

(1981). Toward a theory of intrinsically motivating instruction. Cognitive Science, 4, 333–369.

37.

Martens

R. L.

Gulikers

Bastiaens

(2004). The impact of intrinsic motivation on e-learning in authentic computer tasks. Journal of Computer Assisted Learning, 20, 368–376.

38.

Mayer

R. E.

(2005). Introduction to multimedia learning. In Mayer

R. E.

(Ed.), The Cambridge handbook of multimedia learning (pp. 1–18). New York, NY: Cambridge University Press.

39.

Miltiadou

Savenye

W. C.

(2003). Applying social cognitive constructs of motivation to enhance student success in online distance education. Educational Technology Review, 11(1), 78–95.

40.

Mitchell

T. J. F.

Chen

S. Y.

Macredie

R. D.

(2005). Hypermedia learning and prior knowledge: Domain expertise vs. system expertise. Journal of Computer Assisted Learning, 21(1), 53–64.

41.

Moore

M. G.

(1993). Theory of transactional distance. In Keegan

(Ed.), Theoretical principles of distance education. New York, NY: Routledge.

42.

Prensky

(2000). Digital game-based learning. New York, NY: McGraw-Hill.

43.

Roth

Assor

Kanat-Maymon

Kaplan

(2007). Autonomous motivation for teaching: How self-determined teaching may lead to self-determined learning. Journal of Educational Psychology, 99, 761–774.

44.

Ryan

R. M.

Deci

E. L.

(2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78.

45.

Samruayruen

Enriquez

Natakuatoong

Samruayruen

(2013). Self-regulated learning: A key of a successful learner in online learning environments in Thailand. Journal of Educational Computing Research, 48(1), 45–69.

46.

Standage

Duda

J. L.

Ntoumanis

(2005). A test of self-determination theory in school physical education. British Journal of Educational Psychology, 75, 411–433.

47.

Xie

Debacker

T. K.

Ferguson

(2006). Extending the traditional classroom through online discussion: The role of student motivation. Journal of Educational Computing Research, 34(1), 67–89.

48.

Zimmerman

B. J.

Martinez-Pons

(1990). Student differences in self-regulated learning: Relating grade, sex, and giftedness to self-efficacy and strategy use. Journal of Educational Psychology, 82(1), 51–59.

49.

Zhou

Z. J.

C. H.

D. L.

Yang

J. B.

Zhou

D. H.

(2011). Bayesian reasoning approach based recursive algorithm for online updating belief rule based expert system of pipeline leak detection. Expert Systems with Applications, 38(4), 3937–3943.