Uma Abordagem de Regressão Múltipla para Validação de Variáveis de Autorregulação da Aprendizagem em Ambientes de LMS
Resumo
O presente estudo visa investigar a aplicabilidade do modelo de Regressão Múltipla para a validação de variáveis comportamentais de autorregulação da aprendizagem em contexto de EAD. Foi realizada uma pesquisa com 408 participantes de cursos na modalidade EAD com idade média de 30 anos. A coleta dos dados foi feita através de uma abordagem híbrida entre questionário e variáveis de arquivos de log, com o objetivo de analisar se a afirmação dos alunos, respondida através do questionário, era explicada pelo seu comportamento dentro da plataforma de LMS. Como resultado, foi validado um conjunto de treze variáveis significativas referentes ao comportamento de autorregulação da aprendizagem dos alunos na plataforma Moodle.
Palavras-chave:
Regressão Múltipla, Autorregulação da Aprendizagem, EAD, Moodle
Referências
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Segedy, J R, J S Kinnebrew, e G Biswas. “Using coherence analysis to characterize self-regulated learning behaviours in open-ended learning environments.” 2015: 13-48.
Sonnenberg, C, e M Bannert. “Discovering the Effects of Metacognitive Prompts on the Sequential Structure of SRL-Processes Using Process Mining Techniques.” Journal of Learning Analytics, 2015: 72-100.
Urdan, A T. Qualidade de Serviços médicos na perspectiva do cliente. Revista de Administração de Empresas, 2001.
Winne, P H, e D Jamieson-Noel. “Exploring students’ calibration of self reports about study tactics and achievement.” Contemporary Educational Psychology, 2002: 551–572.
Winne, P H, e N E Perry. “Measuring self-regulated learning.” Handbook of self-regulation, 2000: 531–566.
You, J. W. “Identifying significant indicators using LMS data to predict course achievement in online learning.” The Internet and Higher Education, 2016: 23-30.
Yukselturk, E, e S Bulut. “Gender differences in self-regulated online learning environment.” Journal of Educational Technology & Society, 2009: 12-22.
Zimmerman, B J, e M Martinez-Pons. Construct validation of a strategy model of student self-regulated learning. Journal of Educational Psychology, 1988.
Ali, L, M Hatala, D Gašević, e P H Winne. “Leveraging MSLQ Data for Predicting Students Achievement Goal Orientations.” Journal of Learning Analytics, 2014: 157-160.
Azevedo, R. “Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning.” Educational Psychologist, 2005: 199-209.
Barnard, L, W Y Lan, S M Crooks, e V O Paton, V. O. The relationship of epistemological beliefs with self-regulatory skills in the online course environment. Journal of Online and Learning Teaching, 2008.
Barnard, L, W Y Lan, Y M To, e V O Paton. “Measuring self-regulation in online and blended learning environments.” The Internet and Higher Education, 2009: 1-6.
Barnard, L, W Y Lan, Y M To, V O Paton, e S L Lai. Measuring self-regulation in online and blended learning environments. The Internet and Higher Education, 2009.
Bolfarine, H, e O H Bussab. Elementos de amostragem. São Paulo: Edgard Blücher, 2005.
Bondareva, D, C Conati, R Feyzi-Behnagh, J M Harley, R Azevedo, e F Bouchet. “Inferring learning from gaze data during interaction with an environment to support self-regulated learning.” In Artificial Intelligence in Education , 2013: 229-238.
Broadbent, J, e W L Poon. “Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review.” The Internet and Higher Education, nº 27 (2015): 1-13.
Cameron,, A C, e P K Trivedi. Regression analysis of count data. Cambridge University Press, 1998.
Chen, C. S. Self-regulated learning strategies and achievement in an introduction to information systems course. Information Technology, Learning and Performance Journal, 2002.
Dabbagh, N, e A Kitsantas. “Supporting self-regulation in student-centered web-based learning environments.” International Journal on E-learning, 2004: 40-47.
Dabbagh, N, e A Kitsantas. “Using web-based pedagogical tools as scaffolds for self-regulated learning.” Instructional Science, 2005: 513-540.
Freitas, A. L P, e S G Rodrigues. A avaliação da confiabilidade de questionário: uma análise utilizando o coeficiente alfa de Cronbach. XII SIMPEP, 2005.
Mato, C A, e R T Veiga. Avaliação da qualidade percebida de serviços: um estudo em uma organização não-governamental. São Paulo: Caderno de Pesquisa em Administração, 2000.
Miguel, P. A C, e G E Salomi. Uma revisão dos modelos de medição de qualidade em serviços. Rio de Janeiro: Revista Produção, 2004.
Milan, G S, e G Trez. Pesquisa de satisfação: um modelo para planos de saúde. RAE Eletrônica, Revista de Administração de Empresas, 2005.
Nussbaumer, A, E C Hillemann, C Gütl, e D Albert. “A competence-based service for supporting self-regulated learning in virtual environments.” Journal of Learning Analytics, 2015: 101-133.
Oviedo, H C, e A Campo-Arias. Aproximación al uso del coeficiente alfa de Cronbach. Revista Colombiana de Psiquiatría, 2005.
Panadero, E, P A Kirschner, S Järvelä, J Malmberg, e H Järvenoja. “How Individual Self-Regulation Affects Group Regulation and Performance A Shared Regulation Intervention.” Small Group Research, 2015.
Pintrich, P. R. The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 1999.
Sabourin, J L, B W Mott, e J C Lester. “Early Prediction of Student Self-Regulation Strategies by Combining Multiple Models.” International Educational Data Mining Society, 2012.
Salomi, G E, P. A C Miguel, e A J Abackerli. SERVQUAL x SERVPERF: comparação entre instrumentos para avaliação da qualidade de serviços internos. Gestão & Produção, 2005.
Sanchez-Santillan, M, M Paule-Ruiz, R Cerezo, e V Alvarez-García. “MeL: Modelo de adaptación dinámica del proceso de aprendizaje en eLearning.” anales de psicología, 2016: 106-114.
Schoor, C, e M Bannert. “Exploring regulatory processes during a computer-supported collaborative learning task using process mining.” Computers in Human Behavior, 2012: 1321-1331.
Schunk, D H. Self-regulated learning The educational legacy of Paul R. Pintrich. Educational Psychologist, 2005.
Schunk, D H, e B J Zimmerman. “Self-regulated learning: From teaching to self-reflective practice.” Guilford Press, 1998.
Segedy, J R, J S Kinnebrew, e G Biswas. “Using coherence analysis to characterize self-regulated learning behaviours in open-ended learning environments.” 2015: 13-48.
Sonnenberg, C, e M Bannert. “Discovering the Effects of Metacognitive Prompts on the Sequential Structure of SRL-Processes Using Process Mining Techniques.” Journal of Learning Analytics, 2015: 72-100.
Urdan, A T. Qualidade de Serviços médicos na perspectiva do cliente. Revista de Administração de Empresas, 2001.
Winne, P H, e D Jamieson-Noel. “Exploring students’ calibration of self reports about study tactics and achievement.” Contemporary Educational Psychology, 2002: 551–572.
Winne, P H, e N E Perry. “Measuring self-regulated learning.” Handbook of self-regulation, 2000: 531–566.
You, J. W. “Identifying significant indicators using LMS data to predict course achievement in online learning.” The Internet and Higher Education, 2016: 23-30.
Yukselturk, E, e S Bulut. “Gender differences in self-regulated online learning environment.” Journal of Educational Technology & Society, 2009: 12-22.
Zimmerman, B J, e M Martinez-Pons. Construct validation of a strategy model of student self-regulated learning. Journal of Educational Psychology, 1988.
Publicado
24/10/2016
Como Citar
RODRIGUES, Rodrigo Lins; SILVA, João C. Sedraz; RAMOS, Jorge L. C.; DE SOUZA, Fernando d. F.; GOMES, Alex Sandro.
Uma Abordagem de Regressão Múltipla para Validação de Variáveis de Autorregulação da Aprendizagem em Ambientes de LMS. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 27. , 2016, Uberlândia/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 916-925.
DOI: https://doi.org/10.5753/cbie.sbie.2016.916.
