Investigando a Relação Entre a Nota no Vestibular com o Desempenho em Introdução à Programação
Resumo
Há uma demanda em identificar estudantes que irão necessitar de suporte pedagógico em introdução à programação como estratégia para combater a evasão e retenção. Diferentes características dos estudantes foram analisadas com este propósito, dentre elas o uso das notas de ingresso na instituição de ensino. No entanto, observa-se uma lacuna de dados do contexto educacional brasileiro. Apresenta-se neste estudo a análise estatística dos dados acadêmicos de 292 estudantes. As pontuações no vestibular foram correlacionadas com o desempenho alcançado em introdução à programação. Observou-se uma fraca correlação positiva, que corrobora outros estudos na literatura. Discute-se as implicações deste resultado para a construção de modelos de predição de desempenho acadêmico em programação. Além disto, um procedimento de meta-análise foi realizado para sumarizar o corpo de conhecimento de outros estudos sobre essa temática.
Palavras-chave:
aprendizado de programação, predição de desempenho, nota no vestibular
Referências
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Lawrence D Cohn and Betsy J Becker. 2003. How meta-analysis increases statistical power. Psychological methods 8, 3 (2003), 243.
James S Cole and Robert M Gonyea. 2010. Accuracy of self-reported SAT and ACT test scores: Implications for research. Research in Higher Education 51, 4(2010), 305–319.
Thomas D Cook, Harris Cooper, David S Cordray, Heidi Hartmann, Larry V Hedges, and Richard J Light. 1992. Meta-analysis for explanation: A casebook. Russell Sage Foundation.
Philip Davies. 1999. What is evidence-based education? British journal of educational studies 47, 2 (1999), 108–121.
Joost CF de Winter, Samuel D Gosling, and Jeff Potter. 2016. Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological methods 21, 3 (2016), 273.
Joseph A Durlak. 2009. How to select, calculate, and interpret effect sizes. Journal of pediatric psychology 34, 9 (2009), 917–928.
AF ElGamal. 2013. An educational data mining model for predicting student performance in programming course. International Journal of Computer Applications 70, 17 (2013), 22–28.
Andy P Field. 2005. Is the meta-analysis of correlation coefficients accurate when population correlations vary? Psychological methods 10, 4 (2005), 444.
Anabela Gomes and António Mendes. 2008. A study on student’s characteristics and programming learning. In EdMedia+ Innovate Learning. Association for the Advancement of Computing in Education (AACE), 2895–2904.
Sandra Katz, John Aronis, David Allbritton, Christine Wilson, and Mary Lou Soffa. 2003. A study to identify predictors of achievement in an introductory computer science course. In Proceedings of the 2003 SIGMIS conference on Computer personnel research: Freedom in Philadelphia–leveraging differences and diversity in the IT workforce. ACM, 157–161.
Jennifer L Kobrin, Brian F Patterson, Emily J Shaw, Krista D Mattern, and Sandra M Barbuti. 2008. Validity of the SAT® for Predicting First-Year College Grade Point Average. Research Report No. 2008-5. College Board (2008).
Lynn Lambert. 2015. Factors that predict success in CS1. Journal of Computing Sciences in Colleges 31, 2 (2015), 165–171.
RR Leeper and JL Silver. 1982. Predicting success in a first programming course. ACM SIGCSE Bulletin 14, 1 (1982), 147–150.
Simon Marginson and Thi Kim Anh Dang. 2017. Vygotsky’s sociocultural theory in the context of globalization. Asia Pacific Journal of Education 37, 1 (2017), 116–129.
Rodrigo Pessoa Medeiros, Geber Lisboa Ramalho, and Taciana Pontual Falcão. 2018. A systematic literature review on teaching and learning introductory programming in higher education. IEEE Transactions on Education 99 (2018), 1–14.
André Pereira, Leandro Carvalho, and Eduardo Souto. 2019. Analisando a influência de atributos demográficos no desempenho de estudantes em uma disciplina de introdução à programação. In Anais do XXVII Workshop sobre Educação em Computação. SBC, 360–369.
Lawrence D Cohn and Betsy J Becker. 2003. How meta-analysis increases statistical power. Psychological methods 8, 3 (2003), 243.
James S Cole and Robert M Gonyea. 2010. Accuracy of self-reported SAT and ACT test scores: Implications for research. Research in Higher Education 51, 4(2010), 305–319.
Thomas D Cook, Harris Cooper, David S Cordray, Heidi Hartmann, Larry V Hedges, and Richard J Light. 1992. Meta-analysis for explanation: A casebook. Russell Sage Foundation.
Philip Davies. 1999. What is evidence-based education? British journal of educational studies 47, 2 (1999), 108–121.
Joost CF de Winter, Samuel D Gosling, and Jeff Potter. 2016. Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological methods 21, 3 (2016), 273.
Joseph A Durlak. 2009. How to select, calculate, and interpret effect sizes. Journal of pediatric psychology 34, 9 (2009), 917–928.
AF ElGamal. 2013. An educational data mining model for predicting student performance in programming course. International Journal of Computer Applications 70, 17 (2013), 22–28.
Andy P Field. 2005. Is the meta-analysis of correlation coefficients accurate when population correlations vary? Psychological methods 10, 4 (2005), 444.
Anabela Gomes and António Mendes. 2008. A study on student’s characteristics and programming learning. In EdMedia+ Innovate Learning. Association for the Advancement of Computing in Education (AACE), 2895–2904.
Sandra Katz, John Aronis, David Allbritton, Christine Wilson, and Mary Lou Soffa. 2003. A study to identify predictors of achievement in an introductory computer science course. In Proceedings of the 2003 SIGMIS conference on Computer personnel research: Freedom in Philadelphia–leveraging differences and diversity in the IT workforce. ACM, 157–161.
Jennifer L Kobrin, Brian F Patterson, Emily J Shaw, Krista D Mattern, and Sandra M Barbuti. 2008. Validity of the SAT® for Predicting First-Year College Grade Point Average. Research Report No. 2008-5. College Board (2008).
Lynn Lambert. 2015. Factors that predict success in CS1. Journal of Computing Sciences in Colleges 31, 2 (2015), 165–171.
RR Leeper and JL Silver. 1982. Predicting success in a first programming course. ACM SIGCSE Bulletin 14, 1 (1982), 147–150.
Simon Marginson and Thi Kim Anh Dang. 2017. Vygotsky’s sociocultural theory in the context of globalization. Asia Pacific Journal of Education 37, 1 (2017), 116–129.
Rodrigo Pessoa Medeiros, Geber Lisboa Ramalho, and Taciana Pontual Falcão. 2018. A systematic literature review on teaching and learning introductory programming in higher education. IEEE Transactions on Education 99 (2018), 1–14.
André Pereira, Leandro Carvalho, and Eduardo Souto. 2019. Analisando a influência de atributos demográficos no desempenho de estudantes em uma disciplina de introdução à programação. In Anais do XXVII Workshop sobre Educação em Computação. SBC, 360–369.
Publicado
26/04/2021
Como Citar
SILVA, Leonardo Soares; SILVA, Joanne Gabriela dos Santos; SANTOS, Milena Siqueira.
Investigando a Relação Entre a Nota no Vestibular com o Desempenho em Introdução à Programação. In: SIMPÓSIO BRASILEIRO DE EDUCAÇÃO EM COMPUTAÇÃO (EDUCOMP), 1. , 2021, On-line.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 66-71.
DOI: https://doi.org/10.5753/educomp.2021.14472.
