Gestão de recursos do EaD: como adequar as tecnologias aos perfis de assimilação Outros Idiomas

Gestores de Educação a Distância (EaD) confrontam-se com o dilema de usar as novas Tecnologias da Informação e Comunicação (TIC) de maneira efetiva e eficiente. Por outro lado, teorias de aprendizado, entre elas a Psicologia Cognitiva, descrevem como os meios e processos afetam o aprendizado de indivíduos. Com base nessas teorias, propomos que indivíduos podem ser classificados quanto ao perfil de assimilação em dois grupos: Assimilação Analítica e Assimilação Relacional, e analisamos como os tipos de tecnologias de EaD, classificadas como textuais, audiovisuais, interativas (síncronas) e colaborativas (assíncronas), afetam a percepção de efetividade da tecnologia no aprendizado para cada grupo. Foram encontradas evidências empíricas que suportam que cada grupo percebe diferentemente os tipos de tecnologias, no que se refere à efetividade no aprendizado. Importantes implicações quanto ao uso efetivo e eficiente de recursos no EaD são propostos para os gestores.
Citação ABNT:
SANCHEZ, L. H. A.; SANCHEZ, O. P.; ALBERTIN, A. L. Gestão de recursos do EaD: como adequar as tecnologias aos perfis de assimilação. Revista de Administração de Empresas, v. 55, n. 5, p. 511-526, 2015.
Citação APA:
Sanchez, L. H. A., Sanchez, O. P., & Albertin, A. L. (2015). Gestão de recursos do EaD: como adequar as tecnologias aos perfis de assimilação. Revista de Administração de Empresas, 55(5), 511-526.
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Alavi, M. (1994). Computer-mediated collaborative learning: An empirical evaluation. MIS Quarterly, 18(2), 159-174. doi:10.2307/249763

Ashby, F. G., Isen, A. M., & Turken, A. U. (1999). A neuropsychological theory of positive affect and its influence on cognition. Psychological Review, 106(3), 529-550. doi:10.1037/0033-295x.106.3.529

Ausubel, D. P. (1960). The use of advance organizers in the learning and retention of meaningful verbal material. Journal of Educational Psychology, 51(5), 267-272. doi:10.1037/h0046669

Ausubel, D. P. (1980). Schemata, cognitive structure, and advance organizers: A reply to Anderson, Spiro, and Anderson. American Educational Research Journal, 17(3), 400-404.

Ausubel, D. P., & Fitzgerald, D. (1961). Meaningful learning and retention: Intrapersonal cognitive variables. Review of Educational Research, 31(5), 500-510.

Baddeley, A. D. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive Science, 4(11), 417-423. doi:10.1016/S1364-6613(00)01538-2

Bagozzi, R. P. (2011). Measurement and meaning in information systems and organizational research: Methodological and philosophical foundations. MIS Quarterly, 35(2), 261-292.

Celik, V., & Yesilyurt, E. (2013). Attitudes to technology, perceived computer self-efficacy and computer anxiety as predictors of computer supported education. Computers & Education, 60(1), 148158. doi:10.1016/j.compedu.2012.06.008

Chin, W. W., Johnson, N., & Schwarz, A. (2008). A fast form approach to measuring technology acceptance and other constructs. MIS Quarterly, 32(4), 687-703.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319340. doi:10.2307/249008

Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627-668. doi:10.1037/0033-2909.125.6.627

Devellis, R. F. (2012). Scale development: theory and applications: Applied social research methods series (3rd ed.). Los Angeles: SAGE Publications.

Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. doi:10.2307/3151312

Frederickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden and build theory of positive emotions. American Psychologist, 56(3), 218-226. doi:10.1037/0003-066x.56.3.218

Gable, P., & Harmon-Jones, E. (2010). The motivational dimensional model of affect: Implications for breadth of attention, memory, and cognitive categorisation. Cogniton and Emotion, 24(2), 322-337. doi:10.1080/02699930903378305

Goldstein, E. B. (2010). Cognitive psychology: Connecting mind, research and everyday experience (3rd ed.). Belmont: Cengage Learning.

Ho, C.-H. (2010). Continuance intention of e-learning platform: Toward an integrated model. International Journal of Electronic Business Management, 8(3), 206-215.

Jarvenpaa, S. L., & Leidner, D. E. (1999). Communication and trust in global virtual teams. Organization Science, 10(6), 791-815. doi:10.1287/orsc.10.6.791

Joo, Y. J., Lim, K. Y., & Kim, J. (2013). Locus of control, self-efficacy, and task value as predictors of learning outcome in an online university context. Computers & Education, 63, 149-158. doi:10.1016/j. compedu.2012.10.027

Kruger-Ross, M. J., & Waters, R. D. (2013). Predicting online learning success: Applying the situational theory of publics to the virtual classroom. Computers & Education, 61, 176-184. doi:10.1016/j. compedu.2012.09.015

Leahy, W., & Sweller, J. (2004). Cognitive load and the imagination effect. Applied Cognitive Psychology, 18(7), 857-875. doi:10.1002/acp.1061

Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers & Education, 61, 193-208. doi:10.1016/j. compedu.2012.10.001

Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation– confirmation model. Computers & Education, 54(2), 506-516. doi:10.1016/j.compedu.2009.09.002

Liaw, S.-S., & Huang, H.-M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24. doi:10.1016/j.compedu.2012.07.015

Lundeberg, M. A., & Moch, S. D. (1995). Influence of social interaction on cognition: Connected learning in science. The Journal of Higher Education, 66(3), 312-335. doi:10.2307/2943894

Martin, N. (2008). The roles of semantic and phonological in short-term memory and learning: Evidence from aphasia. In A. Thorn & M. Page. Interactions between short-term and long term memory in the verbal domain (Cap. 11, p. 317). New York: Psychology Press.

Mayer, R. E., & Anderson, R. B. (1991). Animations need narrations: An experimental test of a dual-coding hypothesis. Journal of Educational Psychology, 83(4), 484-490. doi:10.1037/0022-0663.83.4.484

Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology, 93(1), 187-198. doi:10.1037/0022-0663.93.1.187

Minsky, M. (1975). A framework for representing knowledge. In P. Winston. The psychology of computer vision (p. 282). New York: McGraw-Hill.

Moreno, R., & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91(2), 358-368. doi:10.1037/0022-0663.91.2.358

Netemeyer, R. G., Bearden, W. O., & Sharma, S. (2003). Scaling procedures: Issues and applications. Thousand Oaks: Sage Publicantions.

Neuman, W. L. (2006). Social research methods: Qualitative and quantitative approaches (6th ed.). Boston: Pearson.

Overby, E. (2008). Process virtualization theory and the impact of information technology. Organization Science, 19(2), 277-291. doi:10.1287/orsc.1070.0316

Pessin, J. (1933). The comparative effects of social and mechanical stimulation on memorizing. The American Journal of Psychology, 45(2), 263-270. doi:10.2307/1414277

Piccoli, G., Ahmad, R., Ives, B. (2001). Web-based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(4), 401-426.

Rumelhart, D. E. (1980). Schemata: The basic building blocks of cognition. In Spiro R. J., Bruce, B. C. & Brewer, W. F. Theoretical issues in reading comprehension: Perspectives from cognitive psychology, linguistics, artifical intelligence, and education (p. 672). Hillsdale, NJ: Routledge.

Rumelhart, D. E., & Norman, D. A. (1976). Accretion, tuning and restructuring: Three modes of learning [Report n. 7.602, p. 31]. ERICEducation Resources Information Center, Washington, DC.

Rummer, R., Schweppe, J., Fürstenberg, A. Scheiter K, & Zindler A. (2011). The perceptual basis of the modality effect in multimedia learning. Journal of Experimental Psychology: Applied, 17(2), 159-173. doi:10.1037/a0023588

Schank, R. C. (1982). Dynamic memory: A theory of reminding and learning in computers and people. NY: Cambridge University Press.

Shuell, T. J. (1986). Cognitive conceptions of learning. Review of Educational Research, 56(4), 411-436. doi:10.3102/00346543056004411

Skogs, J. (2013). Subject line preferences and other factors contributing to coherence and interaction in student discussion forums. Computers & Education, 60(1), 172-183. doi:10.1016/j.compedu.2012.07.005

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. doi:10.1016/03640213(88)90023-7

Sweller, J., Merrienboer, J. G. van, & Paas, F. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251-296. doi:10.1023/a:1022193728205

Venkatesh, V., Morris, M. G., Davis, G. B.. & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.

Wan, Z., Compeau, D., & Haggerty, N. (2012). The effects of self-regulated learning processes on e-learning outcomes in organizational settings. Journal of Management Information Systems, 29(1), 307-340. doi:10.2753/mis0742-1222290109

Wang, C.-C., Lo, S.-K., & Fang, W. (2008). Extending the technology acceptance model to mobile telecommunication innovation: The existance of network externalities. Journal of Consumer Behaviour, 7(2), 101-110. doi:10.1002/cb.240

Whittlesea, B. W. A., Brooks, L. R., & Westcott, C. (1994). After the learning is over: Factors controlling the selective application of general and particular knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(2), 259-274. doi:10.1037/0278-7393.20.2.259