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Virtual teaching and learning environments: automatic evaluation with artificial neural networks

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Abstract

Forecasting techniques have been widely used in automatic assessment in virtual teaching and learning environments, allowing educators to make decisions in both present and future planning. However, due to the element of uncertainty intrinsic to the forecasting methods, a number of studies have been carried out in order to find a more efficient model that allows for exploring and inferring the relation of a dependent variable with independent variables. In this context, we propose an alternative to solve the problem of automatic evaluation with the use of artificial neural networks that are adjusted, or trained, so that a certain input leads to a specific target output. Therefore, the research seeks to achieve the following: (1) review the state-of-the-art work published in this area, (2) propose a better forecasting model as compared to the existing model, (3) perform a comparative analysis with the previous experiments of multiple linear regression (MLR) and symbolic regression, (4) propose future research guidelines. To this end, a case study was applied to clarify the benefits of the artificial neural networks, emphasizing its efficiency and simplicity of implementation. With a margin of error of less than 2%, this proposal simulates a specialist, and automatically evaluates a student’s answer. As a result, the proposed model performance overcomes the methods of multiple linear regression and symbolic regression efficiently, eliminating the problem of randomness, reducing processing time and at the same time providing a model with higher accuracy and lower error rates.

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References

  1. McCarthy, J., Minsky, M.L., Rochester, N., Shannon, C.E.: A proposal for the dartmouth summer research project on artificial intelligence, August 31, 1955. AI Mag. 27(4), 12 (2006)

    Google Scholar 

  2. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall Press, Upper Saddle River, NJ (2009)

    MATH  Google Scholar 

  3. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 318–362. MIT Press, Cambridge, MA (1986)

    Google Scholar 

  4. Sharda, R.: Neural networks for the MS/OR analyst: an application bibliography. Interfaces (Providence) 24(2), 116–130 (1994)

    Article  Google Scholar 

  5. White, H.: Learning in artificial neural networks: a statistical perspective. Neural Comput. 1, 425–464 (1989)

    Article  Google Scholar 

  6. Ripley, B.D.: Statistical aspects of neural networks. In: Barndorff-Nielsen, O.E., Jensen, J.L., Kendall, W.S. (eds.) Networks and Chaos-Statistical and Probabilistic Aspects, pp. 40–123. Chapman and Hall, London (1993)

    Chapter  Google Scholar 

  7. Cheng, B., Titterington, D.: Neural networks: a review from a statistical perspective. Stat. Sci. 9(1), 2–54 (1994)

    Article  MathSciNet  Google Scholar 

  8. Hu, M.J.-C.: Application of the adaline system to weather forecasting. Doctoral dissertation, Department of Electrical Engineering, Stanford University (1964)

  9. Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35–62 (1998)

    Article  Google Scholar 

  10. Wutsqa, D.U., Kusumawati, R., Subekti, R.: The application of Elman recurrent neural network model for forecasting consumer price index of education, recreation and sports in Yogyakarta. In: 2014 10th International Conference on Natural Computation (ICNC), pp. 192–196 (2014)

  11. Leva, S., Dolara, A., Grimaccia, F., Mussetta, M., Ogliari, E.: Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power. Math. Comput. Simul. 131, 88–100 (2017)

    Article  MathSciNet  Google Scholar 

  12. Kim, Y., Jang, H., Kim, J., Lee, J.: Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network. Appl. Energy 185, 916–928 (2017)

    Article  Google Scholar 

  13. Song, Q., Zheng, Y.-J., Xue, Y., Sheng, W.-G., Zhao, M.-R.: An evolutionary deep neural network for predicting morbidity of gastrointestinal infections by food contamination. Neurocomputing 226, 16–22 (2017)

    Article  Google Scholar 

  14. Silva, C., Fonseca, J.: Educational data mining: a literature review. In: Rocha, Á., Serrhini, M., Felgueiras, C. (eds.) Europe and MENA Cooperation Advances in Information and Communication Technologies, pp. 87–94. Springer, Switzerland (2017)

    Chapter  Google Scholar 

  15. Costa, E.B., Fonseca, B., Santana, M.A., de Araújo, F.F., Rego, J.: Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Comput. Human Behav. 73, 247–256 (2017)

    Article  Google Scholar 

  16. Ajiboye, A.R., Abdullah-Arshah, R., Qin, H.: Using an enhanced feed-forward BP network for predictive model building from students’ data. Intell. Autom. Soft Comput. 22(2), 169–175 (2016)

    Article  Google Scholar 

  17. Wang, T., Mitrovic, A.: Using neural networks to predict student’s performance. Comput. Educ. 1, 969–973 (2002)

    Article  Google Scholar 

  18. Fausett, L.V., Elwasif, W.: Predicting performance from test scores using backpropagation and counterpropagation. Neural Netw. 1994. IEEE World Congr. Comput. Intell. 5, 3398–3402 (1994)

    Google Scholar 

  19. Calvo-Flores, M.D., Galindo, E.G., Jiménez, M.C.P., Pérez, O.: Predicting students’ marks from Moodle logs using neural network models. In: Proceedings of IV International Conference Multimedia Information and Communication Technology Education, vol. 1, pp. 586–590, (2006)

  20. Oladokun, V., Adebanjo, A.T., Charles-Owaba, O.: Predicting students’ academic performance using artificial neural network: a case study of an engineering course. Pac. J. Sci. Technol. 9(1), 72–79 (2009)

    Google Scholar 

  21. Sikder, M.F., Uddin, M.J., Halder, S.: Predicting students’ yearly performance using neural network: a case study of BSMRSTU. In: 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), pp. 524–529 (2016)

  22. Sebastian, S.: Evaluating students performance by artificial neural network using WEKA. Int. J. Comput. Appl. 119(23), 975–8887 (2015)

    Google Scholar 

  23. Kardan, A.A., Sadeghi, H., Ghidary, S.S., Sani, M.R.F.: Prediction of student course selection in online higher education institutes using neural network. Comput. Educ. 65, 1–11 (2013)

    Article  Google Scholar 

  24. Tang, S., Peterson, J.C., Pardos, Z.A.: Deep neural networks and how they apply to sequential education data. In: Proceedings of the Third (2016) ACM Conference on Learning @ Scale, pp. 321–324 (2016)

  25. Piech, C., Spencer, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., Sohl-Dickstein, J., Academy, K.: Deep Knowledge Tracing. In: Advances in Neural Information Processing Systems, pp. 1–13 (2015)

  26. Ognjanovic, I., Gasevic, D., Dawson, S.: Using institutional data to predict student course selections in higher education. Internet High. Educ. 29, 49–62 (2016)

    Article  Google Scholar 

  27. Nakhkob, B., Khademi, M.: Predicted increase enrollment in higher education using neural networks and data mining techniques. J. Adv. Comput. Res. Q. 7(4), 2345–606 (2016)

    Google Scholar 

  28. Lino, A.D.P.: LABSQL: laboratório de ensino de SQL. Master’s Thesis, Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal do Pará (2007)

  29. Ahadi, A., Behbood, V., Vihavainen, A., Prior, J., Lister, R.: Students syntactic mistakes in writing seven different types of SQL queries and its application to predicting students success. In: Proceedings of the 47th ACM Technical Symposium on Computing Science Education - SIGCSE ’16

  30. Lino, A., Rocha, A., Sizo, A.: Virtual teaching and learning environments: automatic evaluation with symbolic regression. J. Intell. Fuzzy Syst. 31(4), 2061–2072 (2016)

    Article  Google Scholar 

  31. The Mathworks Inc., MATLAB - MathWorks, www.mathworks.com/products/matlab, http://www.mathworks.com/products/matlab/ (2016)

  32. Basili, V.R., Rombach, H.D.: The TAME project: towards improvement-oriented software environments. Softw. Eng. IEEE Trans. 14(6), 758–773 (1988)

    Article  Google Scholar 

  33. Halstead, M.H.: Elements of Software Science (Operating and Programming Systems Series), 7th edn. Elsevier, New York (1977)

    MATH  Google Scholar 

  34. McCabe, T.J.: A complexity measure. IEEE Trans. Softw. Eng. 2(4), 308–320 (1976)

    Article  MathSciNet  Google Scholar 

  35. Pressman, R.S.: Software Engineering: A Practitioner’s Approach. Palgrave Macmillan, Basingstoke (2005)

    MATH  Google Scholar 

  36. Calero, C., Piattini, M., Genero, M.: A case study with relational database metrics. In: Computer Systems and Applications, ACS/IEEE International Conference on 2001, pp. 485–487 (2001)

  37. Piattini, M., Martínez, A.: Measuring for database programs maintainability. In: Piattini, M., Martínez, A. (eds.) Database and Expert Systems Applications, pp. 65–78. Springer, New York (2000)

    Chapter  Google Scholar 

  38. Lino, A.D.P., Silva, A.S., Harb, M.P.A.A., Favero, E.L., Brito, S.R.: Avaliação automática de consultas SQL em ambiente virtual de ensino aprendizagem, \(2^{a}\) Conferência Ibérica de Sistemas e Tecnologias de Informação, pp. 89–100. CISTI, Porto, Portugal (2007)

  39. Welty, C., Stemple, D.W.: Human factors comparison of a procedural and a nonprocedural query language. ACM Trans. Database Syst. 6(4), 626–649 (1981)

    Article  Google Scholar 

  40. Kroenke, D.M.: Banco de dados: fundamentos, projeto e implementação, 6th ed. Livros Técnicos e Científicos (1999)

  41. Dunlop, P., Smith, S.: Estimating key characteristics of the concrete delivery and placement process using linear regression analysis. Civ. Eng. Environ. Syst. 20(4), 273–290 (2003)

    Article  Google Scholar 

  42. Fávero, L.P.L.: Análise de Dados: Modelos de Regressão com Excel®, Stata® e SPSS®.In: Rio de Janeiro (ed.):, vol. 1. (2015)

  43. Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)

    Article  Google Scholar 

  44. Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(6), 989–993 (1994)

    Article  Google Scholar 

  45. Foresee, F.D., Hagan, M.T.: Gauss-Newton approximation to Bayesian learning in neural networks, 1997. In: International Conference on, 1997 vol. 3, pp. 1930–1935 (1997)

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Authors and Affiliations

  1. Department of Informatics Engineering, University of Coimbra, 3030-290, Coimbra, Portugal

    Adriano Lino, Álvaro Rocha & Amanda Sizo

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  1. Adriano Lino
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  2. Álvaro Rocha
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  3. Amanda Sizo
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Correspondence to Adriano Lino.

Additional information

This work was conducted with CNPq support, National Council for Scientific and Technological Development - Brazil. We also appreciate the financial support of AISTI (Iberian Association for Information Systems and Technologies).

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Lino, A., Rocha, Á. & Sizo, A. Virtual teaching and learning environments: automatic evaluation with artificial neural networks. Cluster Comput 22 (Suppl 3), 7217–7227 (2019). https://doi.org/10.1007/s10586-017-1122-y

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  • DOI: https://doi.org/10.1007/s10586-017-1122-y

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