Tamanho Ótimo de Amostra para Análise do Desempenho de Estudantes em Ambientes Educacionais Ubíquos
Resumo
O uso da Computação Ubíqua no escopo acadêmico permite automatizar atividades pedagógicas por meio da criação de Ambientes Educacionais Ubíquos. Na literatura, pesquisas que estudam tais tecnologias tendem a realizar comparações entre usuários, mas não averiguam inicialmente se o tamanho das amostras estudadas é capaz de representar as populações das quais são retiradas. Tais análises resultam em informações inexatas, devido à falta de precisão experimental dos métodos utilizados nos estudos. Esta pesquisa explorou essa inconsistência e determinou um tamanho ótimo de amostra de 25 indivíduos para a análise do desempenho de alunos que não utilizam tecnologias educacionais no cotidiano e de 20 estudantes para turmas que possuem contato com sistemas educacionais durante os semestres letivos. Tais resultados se mostram relevantes para pesquisas que fazem uso de fatores humanos, pois demonstram que amostras com quantidades reduzidas de observações são capazes de compreender grande parte do comportamento das variáveis sob análise.
Palavras-chave:
Computação Ubíqua, Desempenho de Estudantes, Tamanho de Amostra
Referências
ABNT (1977). NBR 5891: Regras de Arredondamento na Numeração Decimal. NBR 4: Norma Brasileira Probatória, 1:1.
Aihua, Z. (2010). Study of Ubiquitous Learning Environment Based on Ubiquitous Computing. In Proc. of the 3rd IEEE U-Media, pages 136–138, Jinhua, CN. IEEE.
Araújo, R. D., Brant-Ribeiro, T., Cattelan, R. G., Amo, S. A. e Ferreira, H. N. (2013). Personalization of Interactive Digital Media in Ubiquitous Educational Environments. In Proc. of the IEEE SMC’13, pages 3955–3960, Manchester, UK. IEEE.
Barneveld, A. V., Arnold, K. E. e Campbell, J. P. (2012). Analytics in Higher Education: Establishing a Common Language. EDUCAUSE Learning Initiative, 1:1 – 11.
Barros, I. d. e Tavares, M. (1995). Estimativa do Tamanho Ótimo de Parcelas Experimentais Através de Cálculos Algébricos. Bragantia, 54(1):209–215.
Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, F. A. M., Wahid, U., Greven, C., Chakrabarti, A. e Schroeder, U. (2014). Learning Analytics: Challenges and Future Research Directions. eleed, 10(1).
Cui, Y.-J., Davis, S., Cheng, C.-K. e Bai, X. (2004). A Study of Sample Size with Neural Network. In Proc. of the ICMLC’04, volume 6, pages 3444–3448 vol.6.
Dickson, P. E., Warshow, D. I., Goebel, A. C., Roache, C. C. e Adrion, W. R. (2012). Student Reactions to Classroom Lecture Capture. In Proc. of the 17th ACM ITiCSE, pages 144–149. ACM.
Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics, 7(1):1–26.
Eu, J. H. (1989). A Sampling Approach to Real-Time Performance Monitoring of Digital Transmission Systems. In Proc. of the 8th IPCCC, pages 207–211.
Ferreira, H. N. M., Araújo, R. D., de Amo, S. e Cattelan, R. G. (2012). Classroom Experience: A Platform for Multimedia Capture and Access in Instrumented Educational Environments. In Proc. of the 2012 SBSC, pages 59–64. IEEE.
Ferreira, S. A. e Andrade, A. (2014). Learning Analytics in Practice: Development and Implementation of A Support System to the Management of the Teaching Activity. International Journal of Education and Practice, 2(4):67–95.
Gomez, K. A. e Gomez, A. A. (1984). Statistical Procedures for Agricultural Research. John Wiley & Sons, New York, USA, 2nd edition.
Hwang, W. e Salvendy, G. (2010). Number of People Required for Usability Evaluation: The 10 ± 2 Rule. Communications of the ACM, 53(5):130–133.
Internet World Stats (2014). World Internet User Statistics and World Population Stats; 2014. URL: [link]. Acessado em 17/07/2015.
Kinshuk e Graf, S. (2012). Ubiquitous Learning. In Encyclopedia of the Sciences of Learning, pages 3361–3363. Springer USA.
Last, M. (2009). Improving Data Mining Utility with Projective Sampling. In Proc. of the 15th ACM KDD, pages 487–496, New York, NY. ACM.
Lessman, K. J. e Atkins, R. E. (1963). Optimum Plot Size and Relative Efficiency of Lattice Designs for Grain Sorghum Yield Tests. Crop science, 3(6):477–481.
Meier, V. D. e Lessman, K. J. (1971). Estimation of Optimum Field Plot Shape and Size for Testing Yield in Crambe abyssinica Hochst. Crop Science, 11(5):648–650.
Mertens, D. (2014). Research and Evaluation in Education and Psychology: Integrating Diversity With Quantitative, Qualitative, and Mixed Methods. SAGE Publications, 4th edition.
Nielsen, J. (2000). Why you Only Need to Test With 5 Users. Jakob Nielsen’s Alertbox.
Onwuegbuzie, A. J. e Collins, K. M. (2007). A Typology of Mixed Methods Sampling Designs in Social Science Research. Qualitative Report, 12(2):281–316.
Pelkowitz, L. e Schwarts, S. (1987). Asymptotically Optimum Sample Size for Quickest Detection. IEEE Trans. Aerosp. Electron. Syst., AES-23(2):263–272.
Pham, H. (1992). Optimal Design of Life Testing for ULSI Circuit Manufacturing. IEEE Trans. Semiconduct. Manufact., 5(1):68–70.
Schmettow, M. (2012). Sample Size in Usability Studies. Commun. ACM, 55(4):64–70.
Settle, A., Dettori, L. e Davidson, M. J. (2011). Does Lecture Capture Make a Difference for Students in Traditional Classrooms. In Proc. of the ITiCSE’11, pages 78–82. ACM.
Siemens, G. e Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE review, 46(5):30.
Silva, R. d., Xavier, A., Leite, H. G. e Pires, I. E. (2003). Determinação do Tamanho Ótimo da Parcela Experimental pelos Métodos da Máxima Curvatura Modificado, do Coeficiente de Correlação Intraclasse e da Análise Visual em Testes Clonais de Eucalipto. Revista Árvore, 27(5):669–676.
Slavin, R. e Smith, D. (2009). The Relationship Between Sample Sizes and Effect Sizes in Systematic Reviews in Education. Educational Evaluation and Policy Analysis, 31(4):500–506.
Steel, R., Torrie, J. e Dickey, D. (1997). Principles and Procedures of Statistics: A Biometrical Approach. McGraw-Hill series in probability and statistics. McGraw-Hill, New York, USA, 3rd edition.
Weiser, M. (1991). The Computer for the 21st Century. Sci. Am., 265(3):66–75.
Wieling, M. B. e Hofman, W. H. A. (2010). The Impact of Online Video Lecture Recordings and Automated Feedback on Student Performance. Computers & Education, 54(4):992–998.
Zhao, X., Wan, X. e Okamoto, T. (2010). Adaptive Content Delivery in Ubiquitous Learning Environments. In Proc. of the 6th IEEE WMUTE, pages 19–26.
Aihua, Z. (2010). Study of Ubiquitous Learning Environment Based on Ubiquitous Computing. In Proc. of the 3rd IEEE U-Media, pages 136–138, Jinhua, CN. IEEE.
Araújo, R. D., Brant-Ribeiro, T., Cattelan, R. G., Amo, S. A. e Ferreira, H. N. (2013). Personalization of Interactive Digital Media in Ubiquitous Educational Environments. In Proc. of the IEEE SMC’13, pages 3955–3960, Manchester, UK. IEEE.
Barneveld, A. V., Arnold, K. E. e Campbell, J. P. (2012). Analytics in Higher Education: Establishing a Common Language. EDUCAUSE Learning Initiative, 1:1 – 11.
Barros, I. d. e Tavares, M. (1995). Estimativa do Tamanho Ótimo de Parcelas Experimentais Através de Cálculos Algébricos. Bragantia, 54(1):209–215.
Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, F. A. M., Wahid, U., Greven, C., Chakrabarti, A. e Schroeder, U. (2014). Learning Analytics: Challenges and Future Research Directions. eleed, 10(1).
Cui, Y.-J., Davis, S., Cheng, C.-K. e Bai, X. (2004). A Study of Sample Size with Neural Network. In Proc. of the ICMLC’04, volume 6, pages 3444–3448 vol.6.
Dickson, P. E., Warshow, D. I., Goebel, A. C., Roache, C. C. e Adrion, W. R. (2012). Student Reactions to Classroom Lecture Capture. In Proc. of the 17th ACM ITiCSE, pages 144–149. ACM.
Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics, 7(1):1–26.
Eu, J. H. (1989). A Sampling Approach to Real-Time Performance Monitoring of Digital Transmission Systems. In Proc. of the 8th IPCCC, pages 207–211.
Ferreira, H. N. M., Araújo, R. D., de Amo, S. e Cattelan, R. G. (2012). Classroom Experience: A Platform for Multimedia Capture and Access in Instrumented Educational Environments. In Proc. of the 2012 SBSC, pages 59–64. IEEE.
Ferreira, S. A. e Andrade, A. (2014). Learning Analytics in Practice: Development and Implementation of A Support System to the Management of the Teaching Activity. International Journal of Education and Practice, 2(4):67–95.
Gomez, K. A. e Gomez, A. A. (1984). Statistical Procedures for Agricultural Research. John Wiley & Sons, New York, USA, 2nd edition.
Hwang, W. e Salvendy, G. (2010). Number of People Required for Usability Evaluation: The 10 ± 2 Rule. Communications of the ACM, 53(5):130–133.
Internet World Stats (2014). World Internet User Statistics and World Population Stats; 2014. URL: [link]. Acessado em 17/07/2015.
Kinshuk e Graf, S. (2012). Ubiquitous Learning. In Encyclopedia of the Sciences of Learning, pages 3361–3363. Springer USA.
Last, M. (2009). Improving Data Mining Utility with Projective Sampling. In Proc. of the 15th ACM KDD, pages 487–496, New York, NY. ACM.
Lessman, K. J. e Atkins, R. E. (1963). Optimum Plot Size and Relative Efficiency of Lattice Designs for Grain Sorghum Yield Tests. Crop science, 3(6):477–481.
Meier, V. D. e Lessman, K. J. (1971). Estimation of Optimum Field Plot Shape and Size for Testing Yield in Crambe abyssinica Hochst. Crop Science, 11(5):648–650.
Mertens, D. (2014). Research and Evaluation in Education and Psychology: Integrating Diversity With Quantitative, Qualitative, and Mixed Methods. SAGE Publications, 4th edition.
Nielsen, J. (2000). Why you Only Need to Test With 5 Users. Jakob Nielsen’s Alertbox.
Onwuegbuzie, A. J. e Collins, K. M. (2007). A Typology of Mixed Methods Sampling Designs in Social Science Research. Qualitative Report, 12(2):281–316.
Pelkowitz, L. e Schwarts, S. (1987). Asymptotically Optimum Sample Size for Quickest Detection. IEEE Trans. Aerosp. Electron. Syst., AES-23(2):263–272.
Pham, H. (1992). Optimal Design of Life Testing for ULSI Circuit Manufacturing. IEEE Trans. Semiconduct. Manufact., 5(1):68–70.
Schmettow, M. (2012). Sample Size in Usability Studies. Commun. ACM, 55(4):64–70.
Settle, A., Dettori, L. e Davidson, M. J. (2011). Does Lecture Capture Make a Difference for Students in Traditional Classrooms. In Proc. of the ITiCSE’11, pages 78–82. ACM.
Siemens, G. e Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE review, 46(5):30.
Silva, R. d., Xavier, A., Leite, H. G. e Pires, I. E. (2003). Determinação do Tamanho Ótimo da Parcela Experimental pelos Métodos da Máxima Curvatura Modificado, do Coeficiente de Correlação Intraclasse e da Análise Visual em Testes Clonais de Eucalipto. Revista Árvore, 27(5):669–676.
Slavin, R. e Smith, D. (2009). The Relationship Between Sample Sizes and Effect Sizes in Systematic Reviews in Education. Educational Evaluation and Policy Analysis, 31(4):500–506.
Steel, R., Torrie, J. e Dickey, D. (1997). Principles and Procedures of Statistics: A Biometrical Approach. McGraw-Hill series in probability and statistics. McGraw-Hill, New York, USA, 3rd edition.
Weiser, M. (1991). The Computer for the 21st Century. Sci. Am., 265(3):66–75.
Wieling, M. B. e Hofman, W. H. A. (2010). The Impact of Online Video Lecture Recordings and Automated Feedback on Student Performance. Computers & Education, 54(4):992–998.
Zhao, X., Wan, X. e Okamoto, T. (2010). Adaptive Content Delivery in Ubiquitous Learning Environments. In Proc. of the 6th IEEE WMUTE, pages 19–26.
Publicado
26/10/2015
Como Citar
BRANT-RIBEIRO, Taffarel; CATTELAN, Renan Gonçalves.
Tamanho Ótimo de Amostra para Análise do Desempenho de Estudantes em Ambientes Educacionais Ubíquos. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 26. , 2015, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 31-40.
DOI: https://doi.org/10.5753/cbie.sbie.2015.31.
