January 2012 Volume 15 Number 1 - Educational Technology ...

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continue performing their own job-tasks and could not focus exclusively on the learning itself, the analysis of learning flow may need to follow a different approach in this context. Conclusion The purpose of this study was to investigate the direct effects of self-efficacy, intrinsic value, test anxiety, and perceived usefulness and ease of use on learning flow and achievement in corporate e-learning. In addition, the indirect effects of self-efficacy, intrinsic value, test anxiety, and perceived usefulness and ease of use on achievement, as mediated by learning flow, were also examined. The findings suggested that self-efficacy, intrinsic value, and perceived usefulness and ease of use affected learning flow, while intrinsic value, test anxiety, and perceived usefulness and ease of use were significant predictors of achievement. The results revealed perceived usefulness and ease of use to be the most influential factor for both learning flow and achievement. This suggests that instructors and instructional designers need to employ strategies that increase learners’ self-efficacy and intrinsic motivation in order to facilitate learning flow. To improve academic achievement by providing internal motivation to the learners, learning tasks should be designed so that they are relevant and valuable to learners. Most of all, the design of the learning environment should be centered around learners so that every feature and function of the online system is useful and easy to use. Finally, four implications for further research to broaden the understanding and address the study limitations are presented. First, the role of self-efficacy in a comprehensive model incorporating both learner characteristics and external factors should be re-examined to confirm the present study results. Second, learning flow in a variety of research contexts should be investigated. As reported in this study, learning flow in a corporate setting may have a different function compared to that in an academic setting. Third, the measurement of learning flow needs to be elaborated in further research. Since the self-reporting instrument used in this study may not reflect the actual level of immersive learning experience, observation or interview should be performed to increase the robustness of the methodology. Lastly, the present study results can only be applied to the Korean corporate e-learning setting. In order to generalize the model, future study should included samples selected from other countries or various learning contexts. Acknowledgement This work was supported by National Research Foundation of Korea Grant funded by the Korean Government (20100027744). References Arbaugh, J. B., & Duray, R. (2002). Technological and Structural Characteristics, Student Learning and Satisfaction with Web- Based Courses. Management Learning, 33(3), 331-347. Ames, C. & Archer, J. (1988). Achievement goals in the classroom: Students' learning strategies and the motivation process. Journal of Educational Psychology, 80, 260-267. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215. Bandalos, D. L. (2002). The effects of item parceling on goodness-of-fit and parameter estimate bias in structural equation modeling. Structural Equation Modeling, 9(1), 78-102. Baron, R. M. & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality & Social Psychology, 51(6), 1173-1182. Bloom, B. S. (1983). Human characteristics and school learning. McGraw-Hill. Cassady, J. C., & Johnson, R. E. (2002). Cognitive Test Anxiety and Academic Performance. Contemporary Educational Psychology, 27(2), 270-295. Chang, H. H., & Wang, I. C. (2009). An investigation of user communication behavior in computer mediated environments. Computers in Human Behavior, 24(5), 2336-2356. 323
Connell, J. P., Spencer, M. B., & Aber, J. L. (1994). Educational risk and resilience in African-American youth: Context, self, action, and outcomes in school. Child Development, 65, 493–506. Csikszentmihalyi, M. (1997). Finding flow: the Psychology of engagement with everyday life. New York: Basic Books. Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109-132. Gore, P. A. Jr. (2006). Academic self-efficacy as a predictor of college outcomes: Two incremental validity studies. Journal of Career Assessment, 14(1), 92-115. Hair, J. F., Anderson, R. E., Tatham, R. L.,& Black, W. C. (1992). Multivariate Data Analysis with Readings. New York: MacMillan. Harroff, P. A., & Valentine, T. (2006). Dimensions of program quality in web-based adult education. The American Journal of Distance Education, 20(1), 7-22. Herrington, J., Oliver, R., & Reeves, T. C. (2003). Patterns of engagement in authentic online learning environments. Australian Journal of Educational Technology, 19(1), 59-71. Jackson, S. A., Marsh, H. (1996). Development and validation of a scale to measure optimal experience: The Flow State Scale. Journal of Sport and Exercise Psychology, 18, 17-35. Johnson, R. D., & Hornik, S., & Salas, E. (2008). An empirical examination of factors contributing to the creation of successful elearning environments. International Journal of Human-Computer Studies, 66, 356-369. Joo, Y., Kim, S., & Kim, S. (2008). The effects of self-efficacy, self-regulated learning and online task value on satisfaction and achievement in corporate cyber education. Korean Journal of Vocational Education and Training, 11(3), 151-170. Judge, T. A., Jackson, C. L., Shaw, J. C., Scott, B. A., & Rich, B. L. (2007). Self-efficacy and work-related performance: The integral role of individual differences. Journal of Applied Psychology, 92(1), 107-127. Kiili, K. (2005). Participatory multimedia learning: Engaging learner. Australasian Journal of Educational Technology, 21(3), 303-322. Kishton, J. M. & Widaman, K. F. (1994). Unidimensional versus domain representative parceling of questionnaire items: An empirical example. Educational and Psychological Measurement, 54(3), 757–765. Kim, M. (2005). Factors affecting learning flow and satisfaction in graduate- level e-Leaning program. Journal of Korean Education, 32(1), 165-201. Kleijn, W. C., van der Ploeg, H. M., & Topman,R. M. (1994). Cognition, study habits, test anxiety, and academic performance. Psychological Reports, 75, 1219–1226. Kline, R. B. (2005) Principles and practice of structural equation modeling(2nd ed.). New York: Guilford Press. Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873. Marks, H. M. (2000). Student engagement in instructional activity: Patterns in the elementary, middle, and high school years. American Educational Research Journal, 37, 153–184. Mandler, L. G. & Sarason, S. B. (1952). The effect of differential instructions on anxiety and learning. Journal of Abnormal Social Psychology, 47(2), 166-173. Matin, A. J., & Jackson, S. A. (2008). Brief approaches to assessing task absorption and enhanced subjective experience: Examining 'short' and 'core' flow in diverse performance domains. Motivation and Emotion, 32, 141-157. Meece, J., Blumenfeld, P. C., & Hoyle, R. (1988). Students' goal orientations and cognitive engagement in classroom activities. Journal of Educational Psychology, 80, 514-523. Miltiadou, M. & 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. Morris, L. V., Wu, S. S., & Finnegan, C. L. (2005). Predicting retention in online general education courses. American Journal of Distance Education, 19(1), 23-36. Nystrand, M., & Gamoran, A. (1991). Instructional discourse, student engagement, and literature achievement. Research in the Teaching of English, 25, 261–290. Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research , 66(4), 543-578. 324
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January 2012 Volume 15 Number 1
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Supporting Organizations Centre for
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Analyzing the Learning Process of a
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Angeli, C., & Valanides, N. (2012).
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ased on an integration and evaluati
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ased on the assumption that no sing
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Diagram type D thinking (shown in F
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collaborative task in terms of inte
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Table 4. Descriptive statistics for
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Hong, N. S., Jonassen, D. H., & McG
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commercial off-the-shelf digital ga
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The post-questionnaire was used aft
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Phase Table 1. Phases and activitie
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Research results Describing student
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Assessment results for each one of
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References Annetta, L.A., Minogue,
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Huang, T.-W. (2012). Aberrance Dete
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Detection power Typically, the rela
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Results Detection rates As seen in
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2 .00 .05 3 .02 .10 1 .60 .40 ECI 4
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their easily understandable devices
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Ifenthaler, D. (2012). Determining
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activities is assumed to be reflect
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that the problem representations (i
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deductive reasoning inventory (33 m
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HIMATT structural measures The part
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outcomes, r = .297, p < .01. Accord
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Azevedo, R. (2009). Theoretical, co
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Seel, N. M. (1999b). Educational se
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Initially this paper explores defin
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gamers were boys, and 35% were girl
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Teachers underwent eight hours of p
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A correlation analysis was used to
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playing the game. This would mean P
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Yu, F. Y. (2012). Any Effects of Di
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Considering that identity concealme
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In the nickname group, the student
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Attitudes toward assessors Percepti
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Liu, Z. F., Chiu, C. H., Lin, S. S.
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Appendix A: Attitudes toward Peer-A
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Appendix C: Perception toward the I
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Han, H., & Johnson, S. D. (2012). R
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Research participants The target po
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that its value of cross-loading is
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The canonical variable for the emot
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Literature in the field of online l
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Cornelius, R. R. (1996). The scienc
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Shih, W.-C., Tseng, S.-S., Yang, C.
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To provide personalized (e-)learnin
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Figure 3. An illustrative example o
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elementary schools. Thus they are b
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Table 5. Descriptive statistics for
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References Abowd, G. D., & Atkeson,
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Tseng, K.-H., Chang, C.-C., Lou, S.
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Figure 1. The five stages of KT (Gi
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The use of learning strategies, mon
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Additionally, the above two scales
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Figure 3. Canonical structure betwe
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highly positive perception of CM an
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Langan-Fox, J., Platania-Phung, C.,
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Gömleksiz, M. N. (2012). Elementar
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any statistically significant diffe
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consideration in an educational set
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conditions of a learning environmen
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Kahle, J. B. (1983). The disadvanta
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Appendix A Science and Technology S
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(Guuawardena, Nola, Wilson, Lopez-I
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Questionnaire on student satisfacti
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G4 53 (12.5) 48.9 (13.4) 0.39 G5 51
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across the five levels of knowledge
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Santhanam, R., Sasidharan, S., Webs
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This paper reports only the early e
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Figure 1 illustrates how the abovem
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teacher/instructor continues to dom
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The instructor plays a significant
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Data collection and data sources Fi
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y turning over the responsibility t
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Brophy, J. (1999). Toward a model o
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Lawanto, O., Santoso, H. B., & Liu,
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such as grades and evaluation by ot
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students’ interest, expectancy fo
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constructs that measure students’
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Bandura, A. (1978). Reflections on
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Lin, J. M.-C., & Liu, S.-F. (2012).
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Table 1. The MSWLogo commands learn
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other words, we had prepared a set
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lab. When she returned and found th
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Attitudes toward Programming and Co
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Table 5 successfully, it took four
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Kuter, S., Altinay Gazi, Z., & Alti
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not only the means for trainees’
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Data Collection Techniques and Anal
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organizations was highlighted by on
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provides the means for professional
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Kohonen, V. (2001). Towards experie
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As Rogers (1995) postulates in his
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Instrument and data collection Afte
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“Raising our computer skills in C
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teachers deploy ICT tools in langua
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Ma, W., Anderson, R., & Streith, K.
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APPENDIX A. Questionnaire for Dista
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consistency with other ideas, and a
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conceptions, justifying their belie
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each group. The experimental group
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specifically, instructional approac
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Table 3 shows that the mean frequen
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previous Web-based instructional le
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Millar, R., & Osborne, J.F. (1998).
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Several studies explore the roles t
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understand the content structures o
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teacher was not allowed to provide
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two coding schemes, as illustrated
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also very limited. Teachers and sof
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learning activities. British Journa
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2009; Bernard & Cathryn, 2006) or p
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In this study, the focus of the ins
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The student’s degree of mastery i
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The questionnaire for the acceptanc
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the t-test result, it is found that
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Scale Questionnaire item Mean S.D.
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Hwang, G. J. (2003). A conceptual m
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sharing, problem solving, and achie
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Virtual learning environment In the
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Class Table 1. The survey questions
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“…the distance and the lack of
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collaboration within EVS, thus prev
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Huang, T.-H., Liu, Y.-C., & Chang,
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y personalised context examples in
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Students’ Problem-Solving Guidanc
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Research Method Figure 8. The scree
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Table 4. Abstract of Pairwise Compa
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questions designed for the system h
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Lee, J. (2012). Patterns of Interac
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This study focused on online fora i
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Results and Discussion Due date-cen
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In addition, all groups developed 8
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future. Therefore, they had an oppo
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surprising that most interaction wa
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Page 279 and 280: systems by using 18 personalization
Page 281 and 282: Short-Term Sensory Memory is a temp
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Page 291 and 292: Finally, Figure 10(A) and Figure 10
Page 293 and 294: Appendix # Question Description Par
Page 295 and 296: mental models, are cognitive struct
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Page 299 and 300: Data analysis To test the model fit
Page 301 and 302: may be considered an effective inte
Page 303 and 304: Hsu, I.-C. (2012). Intelligent Disc
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Page 307 and 308: RLO denotes a remote learning objec
Page 309 and 310: Figure 2. The flow-oriented LOFinde
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Page 313 and 314: Similarly, the relation metadata of
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Page 317 and 318: References ADL. (2006). Sharable Co
Page 319 and 320: determinants of how people think, b
Page 321 and 322: Mediating effect of learning flow A
Page 323 and 324: The instrument used to measure lear
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Page 333 and 334: Learning is a cognitive activity th
Page 335 and 336: The survey consisted of 30 question
Page 337 and 338: Created data mining model needs to
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Page 341 and 342: Conclusion Conducted research showe
Page 343 and 344: Romero, C., Ventura S., García E.,
Page 345 and 346: prompts as scaffolding strategy to
Page 347 and 348: Reflection Types This study attempt
Page 349 and 350: All the three groups completed the
Page 351 and 352: Results Learning Outcomes Pre- and
Page 353 and 354: Map Analysis in Transfer Test Figur
Page 355 and 356: Generic Prompts and Specific Prompt
Page 357 and 358: Biswas, G., Schwartz, D., Bransford
Page 359 and 360: Chen, Y.-H., Looi, C.-K., Lin, C.-P
Page 361 and 362: of correct response, answer until c
Page 363 and 364: Figure 7 shows a screenshot of one
Page 365 and 366: Collaboration Questionnaire results
Page 367 and 368: following diagrams. The double-arro
Page 369 and 370: “Most students were encouraged to
Page 371 and 372: Bangert-Drowns, R.L., Kulick, C. C.
Page 373 and 374: primary research questions. First,
Page 375 and 376: Individual learning Figure 2. Scree
Page 377 and 378: Experimental Tools This study emplo
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Table 3 shows that almost all items
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I usually engaged myself in listeni
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References Bloom, B. S. (Ed.). (195
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APPENDIX 2. Taxonomy for Informatio
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understand how we can effectively u
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5. Is there a relationship between
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correlation between teachers’ IWB
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Q7. IWB provides advantages to me t
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training regarding this topic. This
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Conclusion This study provides a so
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Smith, H. J., Higgins, S., Wall, K.
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