Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks
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Published:
Aug 23, 2013
Keywords:
Predictive systems Academic performance Artificial neural networks
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Abstract
Many studies have explored the contribution of different factors from diverse theoretical perspectives to the explanation of academic performance. These factors have been identified as having important implications not only for the study of learning processes, but also as tools for improving curriculum designs, tutorial systems, and students’ outcomes. Some authors have suggested that traditional statistical methods do not always yield accurate predictions and/or classifications (Everson, 1995; Garson, 1998). This paper explores a relatively new methodological approach for the field of learning and education, but which is widely used in other areas, such as computational sciences, engineering and economics. This study uses cognitive and non-cognitive measures of students, together with background information, in order to design predictive models of student performance using artificial neural networks (ANN). These predictions of performance constitute a true predictive classification of academic performance over time, a year in advance of the actual observed measure of academic performance. A total sample of 864 university students of both genders, ages ranging between 18 and 25 was used. Three neural network models were developed. Two of the models (identifying the top 33% and the lowest 33% groups, respectively) were able to reach 100% correct identification of all students in each of the two groups. The third model (identifying low, mid and high performance levels) reached precisions from 87% to 100% for the three groups. Analyses also explored the predicted outcomes at an individual level, and their correlations with the observed results, as a continuous variable for the whole group of students. Results demonstrate the greater accuracy of the ANN compared to traditional methods such as discriminant analyses. In addition, the ANN provided information on those predictors that best explained the different levels of expected performance. Thus, results have allowed the identification of the specific influence of each pattern of variables on different levels of academic performance, providing a better understanding of the variables with the greatest impact on individual learning processes, and of those factors that best explain these processes for different academic levels.
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Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2013). Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks. Frontline Learning Research, 1(1), 42–71. https://doi.org/10.14786/flr.v1i1.13
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References
Abu Naser, S. S. (2012). Predicting learners performance using artificial neural networks in linear programming intelligent tutoring system. International Journal of Artificial Intelligence & Applications (IJAIA), 3(2), 65-73
Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.
Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Lawrence Erlbaum Associates.
Anderson, J. R. (2002). Spanning seven orders of magnitude: A challenge for cognitive modeling. Cognitive Science, 26, 85–112.
Anderson, J. R. (2007) How can the human mind occur in the physical universe? New York: Oxford University Press.
Anderson, J. R., Bothell, D., Byrne, M. D., Douglass S., Lebiere, C., &Yulin, Q. (2004). An integrated theory of the mind. Psychological Review, 111(4), 1036–1060.
Baddeley, A. D. (1986). Working Memory. Oxford: Clarendon Press.
Bansal, A., Kauffman, R. J., & Weitz, R. R. (1993). Comparing the Modeling Performance of Regression and Neural Networks as Data Quality Varies: a Business Value Approach. Journal of Managemnet Informations Systems, 10(1), 11- 32.
Bekele, R., & McPherson, M. (2011).A Bayesian performance prediction model for mathematics education: A prototypical approach for effective group composition. British Journal of Educational Technology, 42(3), 395–416.
Biggs, J. (1987). Study Process Questionnaire manual. Melbourne, Australia: Australian Council for Educational Research.
Birenbaum, M., Breuer, K., Cascallar, E., Dochy, F., Dori, Y, Ridgway, J, Wiesemes, R. (2006), & Nickmans, G. (Editor). A learning Integrated Assessment System. Educational Research Review, 1, 61-67.
Boekaerts, M., & Cascallar, E. (2006). How far have we moved toward the integration of theory and practice in Self-regulation? Educational Psychology Review, 18(3), 199-210.
Boekaerts, M. & Cascallar, E. C. (2011). Predicting and Explaining Writing Outcomes: Neural Network Methodology at work. Symposium: Predicting academic performance with the use of predictive systems analysis. Proceedings of the Biennial Conference of the European Association for Research on Learning and Instruction (Earli). Exeter, UK, 30 August – 3 September 2011.
Braten, I. & Stromso, H. (2006). Epistemological beliefs, interest, and gender as predictors of Internet-based learning activities. Computers in Human Behavior, 22, 1027-1042.
Cascallar, E. C., Boekaerts, M., & Costigan, T. E. (2006) Assessment in the Evaluation of Self- Regulation as a Process, Educational Psychology Review, 18(3), 297-306.
Cascallar, E. C., & Musso, M. F. (2008). Classificatory stream analysis in the prediction of expected reading readiness: Understanding student performance. International Journal of Psychology, Proceedings of the XXIX International Congress of Psychology ICP 2008, 43(43/44), 231-.231.
Castejón, J. L., & Navas, L. (1992). Determinantes del
rendimiento académico en la educación secundaria. Un modelo causal. [Determinants of academic achievement in secondary education. A causal model]. Análisis y Modificación de Conducta, 18(61), 697-728.
Cattell, R. B. (1971). Abilities: Structure, growth and action. Boston: Houghton Mifflin.
Chamorro-Premuzic, T., & Arteche, A. (2008). Intellectual competence and academic performance: preliminary validation of a model. Intelligence, 36, 564-573.
Colom, R., Escorial, S., Chun Shih, P., & Privado, J. (2007).Fluid intelligence, memory span, and temperament difficulties predict academic performance of young adolescents. Personality and Individual Differences, 42, 1503-1514.
Conway, A. R. A., Cowan, N., Bunting, M. F., Therriault, D., & Minkoff, S. (2002). A latent variable analysis of working memory capacity, short term memory capacity, processing speed, and general fluid intelligence. Intelligence, 30, 163- 183.
Conway, A. R. A., & Engle, R.W. (1996). Individual differences in working memory capacity: More evidence for a general capacity theory. Memory, 4, 577-590.
Conway, A. R. A., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilhelm, O., & Engle, R. W. (2005).Working memory span tasks: A methodological review and user’s guide. Psychonomic Bulletin & Review, 12(5), 769-786
Croy, M., Barnes, T., & Stamper, J. (2008). Towards an intelligent tutoring system for propositional proof construction. In A. Briggle, K. Waelbers, and P. Brey (Eds.), Computing and Philosophy (pp. 145-215). Amsterdam, The Netherlands: IOS Press.
Daneman, M., & Carpenter, P. A. (1980).Individual-differences in working memory and reading. Journal of Verbal Learning and Verbal Behaviour, 19, 450 - 466.
Detienne, K. B., Detienne, D. H., & Joshi, S. A. (2003). Neural networks as statistical tools for business researchers. Organizational Research Methods, 6, 236-265.
Duliba, K. A. (1991) Contrasting Neural Nets with Regression in Predicting Performance in the Transportation Industry. Proceedings of the Twenty-Fourth Annual Hawaii International Conference on System Sciences, 4.
Dupeyrat, C., & Marine, C. (2005). Implicit theories of intelligence, goal orientation, cognitive engagement, and achievement: A test of Dweck's model with returning to school adults. Contemporary Educational Psychology, 30(1), 43-59.
Engle, R.W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science, 11, 19-23.
Engle, R.W., & Kane, M. J. (2004).Executive attention, working memory capacity, and a two-factor theory of cognitive control. In B. Ross (Ed.), The Psychology of Learning and Motivation (pp. 145-199). NewYork, NY: Elsevier.
Eriksen, B. A., & Eriksen, C.W. (1974). Effects of noise letters upon the identification of a target letter in a non search task. Perception and Psychophysics, 16, 143-149.
Everson, H. T. (1995). Modelling the student in intelligent tutoring systems: The promise of a new psychometrics. Instructional Science, 23(5-6), 433-452.
Everson, H. T., Chance, D., & Lykins, S. (1994). Exploring the use of artificial neural networks in educational research. Paper presented at the annual meeting of the American Educational Research Association, New York.
Fan, J., McCandliss, B. D., Summer, T., Raz, A., & Posner, M.I. (2002).Testing the efficiency and independence of attentional networks. Journal of Cognitive Neuroscience, 14(3), 340-347.
Feldman Barrett, L., Tugade, M. M., & Engle, R. W. (2004). Individual differences in working memory capacity and dual-process theories of mind. Psychological Bulletin, 130, 553-573.
Fenollar, P., Roman, S., & Cuestas, P. J. (2007). University students’ academic performance: An integrative conceptual framework and empirical analysis. British Journal of Educational Psychology, 77, 873-891.
Fernandez-Castillo, A., & Gutiérrez-Rojas, M. E. (2009). Selective attention, anxiety, depressive symptomatology and academic performance in adolescents. Electronic Journal of Research in Educational Psychology, 7(1), 49-76.
Fletcher, J. M. (2005). Predicting math outcomes: Reading predictors and comorbidity. Journal of Learning Disabilities, 38(4), 308-312.
Fong, S., Si, Y.-W., & Biuk-Aghai, R. P. (2009). Applying a Hybrid Model of Neural Network and Decision Tree Classifier for Predicting University Admission. Proceedings of the 7th International Conference on Information, Communication, and Signal Processing (ICICS2009), pp. 1-5, Macau, China, IEEE Press.
Garson, G. D. (1998). Neural Networks. An Introductory Guide for Social Scientists. London: Sage Publications Ltd.
Gathercole, S. E., Pickering, S. J., Knight, C., & Stegmann, Z. (2004).Working memory skills and educational attainment: Evidence from national curriculum assessments at 7 and 14 years of age. Applied Cognitive Psychology, 18, 1-16.
Gazzaniga, M., Ivry, R., & Mangun, G. (2002).Cognitive neuroscience: The biology of the mind (2nd ed.). New York, NY: W.W. Norton
Grimley, M., & Banner, G. (2008).Working memory, cognitive style, and behavioural predictors of GCSE exam success. Educational Psychology, 28(3), 341-351.
Grossberg, S. (1980). How does the brain build a cognitive code? Psychological Review, 87, 1- 51.
Grossberg, S. (1982). Studies of mind and brain: Neural principles of learning, perception, development, cognition and motor control. Boston: Reidel Press.
Gsanger, K., W., Homack, S., Siekierski, B., & Riccio, C. (2002).The relation of memory and attention to academic achievement in children. Archives of Clinical Neuropsychology, 17(8), 790.
Hailikari, T., Nevgi, & A., Komulainen, E. (2008). Academic self-beliefs and prior knowledge as predictors of student achievement in Mathematics: a structural model. Educational Psychology, 28(1), 59-71.
Hardgrave, B. C., Wilson, R. L., & Walstrom, K. A. (1994).Predicting Graduate Student Success: A Comparison of Neural Networks and Traditional Techniques. Computer and Operations Research, 21(3), 249-263.
Hazy, T. E., Frank, M. J., & O’ Reilly, R. C. (2006). Banishing the Homunculus: Making Working Memory Work, Neuroscience 139, 105–118.
Heitz, R. P., Redick, T. S., Hambrick, D. Z., Kane, M. J., Conway, A. R. A., & Engle, R. W. (2006). Working memory, executive function, and general fluid intelligence are not the same. Behavioral and Brain Sciences, 29, 135-136.
Jarrold, C., & Towse, J. N. (2006). Individual differences in working memory. Neuroscience, 139, 39-50.
Jimmerson, S. R., Dubrow, E. H., Adam, E., Gunnar, M., & Bozoky, I. K. (2006).Associations among academic achievement, attention, and andrenocortical reactivity in Caribbean village children. Canadian Journal of School Psychology, 21, 120-138.
Kanakana, G., & Olanrewaju, A. (2011).Predicting student performance in engineering education using an artificial neural network at Tshwane University of Technology, Proceedings of the ISEM, Stellenbosch, South Africa.
Kane, M. J., Hambrick, D. Z., Tuholski, S.W., Wilhelm, O., Payne, T.W., & Engle, R.W. (2004). The generality of working memory capacity: A latent variable approach to verbal and visuospatial memory span and reasoning. Journal of Experimental Psychology: General, 133, 189-217.
Kent, R. (2009). Rethinking data analysis – part two. Some alternatives to frequentist approaches. International Journal of Market Research, 51, 181-202.
Kobrin, J. L., Patterson, B. F., Shaw, E. J., Mattern, K. D., & Barbuti, S. M. (2008). Validity of the SAT for predicting first-year college grade point average. College Board Research Report 2008-5.New York: The College Board. Retrieved from http://research.collegeboard.org/rr2008-5.pdf.
Kohavi, R. & Provost, F. (1998).Glossary of terms. Machine Learning, 30(2–3): 271–274.
Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2001). A comprehensive meta-analysis of the predictive validity of the Graduate Record Examinations: Implications for graduate student selection and performance. Psychological Bulletin, 127(1), 162-181.
Kuncel, N. R., Crede, M., Thomas, L. L., Klieger, D.M., Seiler, S.N., & Woo, S.E. (2004). A meta-analysis of the Pharmacy College Admission Test (PCAT) and grade predictors of pharmacy student success. Annual conference of the American Psychological Society, Chicago, IL.
Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic performance, career potential, creativity, and job performance: Can one construct predict them all? Journal of Personality and Social Psychology, 86(1), 148-161.
Kuncel, N. R., Crede, M., Thomas, L. L., Klieger, D. M., Seiler, S. N., & Woo, S. E. (2005). A meta-analysis of the Pharmacy College Admission Test (PCAT) and grade predictors of pharmacy student success. American Journal of Pharmaceutical Education, 69(3), 339-347.
Krumm, S., Ziegler, M., Buehner, M. (2008). Reasoning and working memory as predictors of school grades. Learning and Individual Differences, 18 (2), 248-257.
Kyllonen, P. C., & Christal, R. E. (1990). Reasoning ability is (little more than) working-memory capacity?! Intelligence, 14, 389-433.
Kyllonen, P. C., & Stephens, D. L. (1990).Cognitive abilities as determinants of success in acquiring logic skill. Learning and Individual Differences, 2, 129-160.
Kyndt, E., Cascallar, E., & Dochy, F. (2012). Individual differences in working memory capacity and attention, and their relationship with students’ approaches to learning. Higher Education, 64(3), 285-297.
Kyndt, E., Musso, M., Cascallar, E., & Dochy, F. (2012, Submitted). Predicting academic performance: The role of cognition, motivation and learning approaches. A neural network analysis.Journal of Further and Higher Education.
Landerl, K. (2010). Temporal processing, attention, and learning disorders. Learning & Individual Differences, 20(5), 393-401.
Linn, R. L., & Hastings, C. N. (1984). A meta-analysis of the validity of predictors of performance in law school. Journal of Educational Measurement, 21, 245-259.
Lippman, R. (1987). An introduction to computing with neuralets. IEEE ASSP Magazine, 3(4), 4-22.
Lovett, M. W. (1979). The selective encoding of sentential information in normal reading development. Child Development, 50(3), 897.
Lykins, S., & Chance, D. (1992). Comparing artificial neural networks and multiple regression for predictive application, Proceedings of the Eight Annual Conference on Applied Mathematics, Edmond OK, 155-169
Marquez, L., Hill, T., Worthley, R., & Remus, W. (1991). Neural network models as an alternative to regression. Proceedings of the IEEE 24th Annual Hawaii International Conference on Systems Sciences, 4, 129-135.
Marshall, D. B., & English, D. J. (2000).Neural network modelling of risk assessment in child protective services. Psychological Methods, 5(1), 102-124.
Maucieri, L. P. (2003). Predicting behavior with an artificial neural network: A comparison with linear models of prediction (January 1, 2003). ETD Collection for Fordham University, NY, USA. Retrieved from http://fordham.bepress.com/dissertations/AAI3098134.
Mavrovouniotis, M. L. & Chang, S. (1992).Hierarchical neural networks. Computers & Chemical Engineering, 16(4), 347-369.
Miñano, P., Gilar, R., & Castejón, J. L. (2012) A structural model of cognitive-motivational variables as explanatory factors of academic achievement in Spanish Language and Mathematics. Anales de Psicología, 28(1), 45-54.
Mislevy, R. J. (2013). Measurement is a Necessary but not Sufficient Frame for Assessment. Measurement, 11, 47–49, 2013
Mislevy, R. J., Steinberg, L. S., & Almond, R. A. (2003). On the structure of educational assessments. Measurement: Interdisciplinary Research and Perspectives, 1, 3–67.
Mukta, P., & Usha, A., (2009). A study of academic performance of business school graduates using neural network and statistical techniques. Expert Systems with Applications, 36(4), 7865-7872.
Musso, M. F., & Cascallar, E. C. (2009a). New approaches for improved quality in educational assessments: Using automated predictive systems in reading and mathematics. Journal of Problems of Education in the 21st Century, 17, 134-151.
Musso, M. F., & Cascallar, E. C. (2009b).Predictive systems using artificial neural networks: An introduction to concepts and applications in education and social sciences. In M. C. Richaud & J. E. Moreno (Eds.).Research in Behavioural Sciences (Volume I), (pp. 433-459). Argentina: CIIPME/CONICET.
Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2012). Predicting mathematical performance: The effect of cognitive processes and self-regulation factors. Education Research International.Vol. 12.
Nasr, G. E., Badr, E. A., & Joun, C. (2002). Cross Entropy Error Function In Neural Networks: Forecasting Gasoline Demand. FLAIRS-02 Proceedings of the AAAI. Retrieved from http://www.aaai.org/Papers/FLAIRS/2002/FLAIRS02-075.pdf
Navas, L., Sampascual, G., & Santed, M. A. (2003). Predicción de las calificaciones de los estudiantes: la capacidad explicativa de la inteligencia general y de la motivación. [Prediction of students’ performance scores: the role of the general intelligence and motivation. Journal of General and Applied Psychology], 56(2), 225-237.
Neal, W., & Wurst, J. (2001). Advances in market segmentation. Marketing Research, 13(1), 14-18.
Passolunghi, M. C., & Pazzaglia, F. (2004). Individual differences in memory updating in relation to arithmetic problem solving. Learning and Individual Differences 14(4), 219-230.
Perkins, K., Gupta, L. & Tammana (1995). Predict item difficulty in a reading comprehension test with an artificial neural network. Language Testing, 12(1), 34-53.
Pickering, S. J. (2006). Working memory and education. USA: Academic Press.
Pinninghoff Junemann, M. A., Salcedo Lagos, P. A., & Contreras Arriagada, R. (2007).Neural networks to predict schooling failure/success. In J. Mira & J.R. ´Alvarez (Eds.), IWINAC 2007, Part II, LNCS 4528(pp. 571–579). Berlin / Heidelberg: Springer-Verlag.
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P.R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 452–502). San Diego, CA: Academic Press.
Posner, M. I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 41A, 19-45.
Posner, M. I., & Petersen, S. E. (1990). The attention system of the human brain. Annual Review Neuroscience. 13, 25-42.
Posner, M. I., & Rothbart, M. K. (1998). Attention, self-regulation and consciousness. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 353, 1915–1927.
Ramaswami, M. M., & Bhaskaran, R. R. (2010). A CHAID based performance prediction model in educational data mining. International Journal of Computer Science Issues, 7(1), 10-18.
Redick, T. S., & Engle, R.W. (2006).Working memory capacity and attention network test performance. Applied Cognitive Psychology, 20, 713-721.
Reid, R. (2006). Self-regulated strategy development for written expression with students with attention deficit/ hyperactivity disorder. Exceptional Children, 73(1), 53-67.
Riccio, C. A., Lee, D., Romine, C. Cash, D., & Davis, B. (2002).Relation of memory and attention to academic achievement in adults. Archives of Clinical Neuropsychology, 18(7), 755-756.
Riding, R. J., Grimley, M., Dahraei, H., & Banner, G. (2003).Cognitive style, working memory and learning behaviour and attainment in school subjects. British Journal of Educational Psychology, 73, 749-769.
Roth, P. L., Be Vier, C. A., Switzer, F. S., & Schippmann, J. S. (1996). Meta-analyzing the relationship between grades and job performance. Journal of Applied Psychology, 81, 548-556.
Roth, P. L., & Clarke, R. L. (1998). Meta-analyzing the relation between grades and salary. Journal of Vocational Behavior, 53, 386-400.
Ruban, L. M., & McCoach, D. B. (2005). Gender differences in explaining grades using structural equation modeling. The Review of Higher Education, 28, 475-502.
Rueda, M. R., Posner, M. I., & Rothbart, M. K. (2004). Attentional control and self regulation. In R.F. Baumeister & K.D. Vohs (Eds), Handbook of Self Regulation: Research, Theory, and Applications, New York: Guilford Press, 14: 283-300.
Rumelhart, D., Hinton, G. & Williams, R. (1986). Learning representations by back-propagating errors. Nature, 323, 533- 536.
Rumelhart, D. E., McClelland, J. L., & the PDP research group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Volume I. Cambridge, MA: MIT Press.
Schmidt, F. L. (2002). The role of general cognitive ability and job performance: Why there cannot be a debate. Human Performance, 15, 187–210.
Segers, M., Dochy, F., & Cascallar, E. (2003).Optimizing new modes of assessment: In search of qualities and standards.The Netherlands: Kluwer Academic Publishers.
Simons, J., Dewitte, S., & Lens, W. (2004). The role of different types of instrumentality in motivation, study strategies, and performance: Know why you learn, so you'll know what you learn! British Journal of Educational Psychology, 74, 343-360.
Snyderman, M., & Rothman, S. (1987). Survey of expert opinion on intelligence and aptitude testing. American Psychologist, 42(2), 137-144
Specht, D. (1991). A general regression neural network. IEEE transactions on neural networks, 2(6), 568-576.
St Clair-Thompson, H. L., & Gathercole, S. E. (2006). Executive functions and achievements in school: Shifting, updating, inhibition, and working memory. The Quarterly Journal of Experimental Psychology, 59(4), 745-759.
Strucchi, E. (1991). Inventario de Estrategias de Aprendizaje y de Estudio. [Learning Strategies Inventory and Study]. Buenos Aires: Psicoteca.
Turner, E. A., Chandler, M., & Heffer, R. W. (2009). Influence of parenting styles, achievement motivation, and self-efficacy on academic performance in college students. Journal of College Student Development, 50, 3, 337-346.
Unsworth, N., Heitz, R. P., Schrock, J. C., & Engle, R. W. (2005). An automated version of the operation span task. Behavior Research Methods, 37(3), 498-505.
Vandamme, J. P., Meskens, N., & Superby, J. F. (2007). Predicting academic performance by data mining methods.Education Economic, 15(4), 405-41.
Walczak, S. (1994). Categorizing university student applicants with neural networks. IEEE International conference on neural networks, 6, 3680-3685.
Weinstein, C. E., & Mayer, R.E. (1986). The teaching of learning strategies. In M.C. Wittrock (Ed.), Handbook of research on teaching (3rd ed.). Macmillan, New York.
Weinstein, C. E. & Palmer, D. R. (2002). LASSI: User’s Manual (2nd Edition). Clearwater, FL: H&H Publishing Company, Inc.
Weinstein, C. E., Palmer, D. R., & Schulte, A. C. (1987).Learning and study strategies inventory. Clearwater, FL: H & H Publishing company, Inc.
Weinstein, C. E., Schulte, A. C, & Cascallar, E. C. (1982). The learning and studies strategies inventory (LASSI): Initial design and development. Technical Report, US Army Research Institute for the Social and Behavioural Sciences, Alexandria, VA.
Weiss, S. M. & Kulikowski, C. A. (1991). Computer systems that learn. San Mateo, CA: Morgan Kaufmann Publishers.
Welsh, M.C., Satterlee-Cartmell, T., & Stine, M. (1999). Towers of Hanoi and London: Contribution of working memory and inhibition to performance. Brain Cognition, 41(2), 231-242.
White, H. & Racine, J. (2001): Statistical inference, the bootstrap, and neural network modelling with application to foreign exchange rates. IEEE Transactions on Neural Networks: Special Issue on Neural Networks in Financial Engineering, 12, 657-673.
Wilson, R. L. & Hardgrave, B. C. (1995). Predicting graduate student success in a MBA program: Regression vs. classification. Educational and Psychological Measurement, 55, 186-195.
Zambrano Matamala, C., Rojas Díaz, D., Carvajal Cuello, K., & Acuña Leiva, G. (2011). Análisis de rendimiento académico estudiantil usando data warehouse y redes neuronales. [Analysis of students’ academic performance using data warehouse and neural networks] Ingeniare. Revista Chilena de Ingeniería, 19(3), 369-381.
Zeegers, P. (2004). Student learning in higher education: A path analysis of academic achievement in science. Higher Education Research & Development, 23(1), 35-56.
Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.
Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Lawrence Erlbaum Associates.
Anderson, J. R. (2002). Spanning seven orders of magnitude: A challenge for cognitive modeling. Cognitive Science, 26, 85–112.
Anderson, J. R. (2007) How can the human mind occur in the physical universe? New York: Oxford University Press.
Anderson, J. R., Bothell, D., Byrne, M. D., Douglass S., Lebiere, C., &Yulin, Q. (2004). An integrated theory of the mind. Psychological Review, 111(4), 1036–1060.
Baddeley, A. D. (1986). Working Memory. Oxford: Clarendon Press.
Bansal, A., Kauffman, R. J., & Weitz, R. R. (1993). Comparing the Modeling Performance of Regression and Neural Networks as Data Quality Varies: a Business Value Approach. Journal of Managemnet Informations Systems, 10(1), 11- 32.
Bekele, R., & McPherson, M. (2011).A Bayesian performance prediction model for mathematics education: A prototypical approach for effective group composition. British Journal of Educational Technology, 42(3), 395–416.
Biggs, J. (1987). Study Process Questionnaire manual. Melbourne, Australia: Australian Council for Educational Research.
Birenbaum, M., Breuer, K., Cascallar, E., Dochy, F., Dori, Y, Ridgway, J, Wiesemes, R. (2006), & Nickmans, G. (Editor). A learning Integrated Assessment System. Educational Research Review, 1, 61-67.
Boekaerts, M., & Cascallar, E. (2006). How far have we moved toward the integration of theory and practice in Self-regulation? Educational Psychology Review, 18(3), 199-210.
Boekaerts, M. & Cascallar, E. C. (2011). Predicting and Explaining Writing Outcomes: Neural Network Methodology at work. Symposium: Predicting academic performance with the use of predictive systems analysis. Proceedings of the Biennial Conference of the European Association for Research on Learning and Instruction (Earli). Exeter, UK, 30 August – 3 September 2011.
Braten, I. & Stromso, H. (2006). Epistemological beliefs, interest, and gender as predictors of Internet-based learning activities. Computers in Human Behavior, 22, 1027-1042.
Cascallar, E. C., Boekaerts, M., & Costigan, T. E. (2006) Assessment in the Evaluation of Self- Regulation as a Process, Educational Psychology Review, 18(3), 297-306.
Cascallar, E. C., & Musso, M. F. (2008). Classificatory stream analysis in the prediction of expected reading readiness: Understanding student performance. International Journal of Psychology, Proceedings of the XXIX International Congress of Psychology ICP 2008, 43(43/44), 231-.231.
Castejón, J. L., & Navas, L. (1992). Determinantes del
rendimiento académico en la educación secundaria. Un modelo causal. [Determinants of academic achievement in secondary education. A causal model]. Análisis y Modificación de Conducta, 18(61), 697-728.
Cattell, R. B. (1971). Abilities: Structure, growth and action. Boston: Houghton Mifflin.
Chamorro-Premuzic, T., & Arteche, A. (2008). Intellectual competence and academic performance: preliminary validation of a model. Intelligence, 36, 564-573.
Colom, R., Escorial, S., Chun Shih, P., & Privado, J. (2007).Fluid intelligence, memory span, and temperament difficulties predict academic performance of young adolescents. Personality and Individual Differences, 42, 1503-1514.
Conway, A. R. A., Cowan, N., Bunting, M. F., Therriault, D., & Minkoff, S. (2002). A latent variable analysis of working memory capacity, short term memory capacity, processing speed, and general fluid intelligence. Intelligence, 30, 163- 183.
Conway, A. R. A., & Engle, R.W. (1996). Individual differences in working memory capacity: More evidence for a general capacity theory. Memory, 4, 577-590.
Conway, A. R. A., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilhelm, O., & Engle, R. W. (2005).Working memory span tasks: A methodological review and user’s guide. Psychonomic Bulletin & Review, 12(5), 769-786
Croy, M., Barnes, T., & Stamper, J. (2008). Towards an intelligent tutoring system for propositional proof construction. In A. Briggle, K. Waelbers, and P. Brey (Eds.), Computing and Philosophy (pp. 145-215). Amsterdam, The Netherlands: IOS Press.
Daneman, M., & Carpenter, P. A. (1980).Individual-differences in working memory and reading. Journal of Verbal Learning and Verbal Behaviour, 19, 450 - 466.
Detienne, K. B., Detienne, D. H., & Joshi, S. A. (2003). Neural networks as statistical tools for business researchers. Organizational Research Methods, 6, 236-265.
Duliba, K. A. (1991) Contrasting Neural Nets with Regression in Predicting Performance in the Transportation Industry. Proceedings of the Twenty-Fourth Annual Hawaii International Conference on System Sciences, 4.
Dupeyrat, C., & Marine, C. (2005). Implicit theories of intelligence, goal orientation, cognitive engagement, and achievement: A test of Dweck's model with returning to school adults. Contemporary Educational Psychology, 30(1), 43-59.
Engle, R.W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science, 11, 19-23.
Engle, R.W., & Kane, M. J. (2004).Executive attention, working memory capacity, and a two-factor theory of cognitive control. In B. Ross (Ed.), The Psychology of Learning and Motivation (pp. 145-199). NewYork, NY: Elsevier.
Eriksen, B. A., & Eriksen, C.W. (1974). Effects of noise letters upon the identification of a target letter in a non search task. Perception and Psychophysics, 16, 143-149.
Everson, H. T. (1995). Modelling the student in intelligent tutoring systems: The promise of a new psychometrics. Instructional Science, 23(5-6), 433-452.
Everson, H. T., Chance, D., & Lykins, S. (1994). Exploring the use of artificial neural networks in educational research. Paper presented at the annual meeting of the American Educational Research Association, New York.
Fan, J., McCandliss, B. D., Summer, T., Raz, A., & Posner, M.I. (2002).Testing the efficiency and independence of attentional networks. Journal of Cognitive Neuroscience, 14(3), 340-347.
Feldman Barrett, L., Tugade, M. M., & Engle, R. W. (2004). Individual differences in working memory capacity and dual-process theories of mind. Psychological Bulletin, 130, 553-573.
Fenollar, P., Roman, S., & Cuestas, P. J. (2007). University students’ academic performance: An integrative conceptual framework and empirical analysis. British Journal of Educational Psychology, 77, 873-891.
Fernandez-Castillo, A., & Gutiérrez-Rojas, M. E. (2009). Selective attention, anxiety, depressive symptomatology and academic performance in adolescents. Electronic Journal of Research in Educational Psychology, 7(1), 49-76.
Fletcher, J. M. (2005). Predicting math outcomes: Reading predictors and comorbidity. Journal of Learning Disabilities, 38(4), 308-312.
Fong, S., Si, Y.-W., & Biuk-Aghai, R. P. (2009). Applying a Hybrid Model of Neural Network and Decision Tree Classifier for Predicting University Admission. Proceedings of the 7th International Conference on Information, Communication, and Signal Processing (ICICS2009), pp. 1-5, Macau, China, IEEE Press.
Garson, G. D. (1998). Neural Networks. An Introductory Guide for Social Scientists. London: Sage Publications Ltd.
Gathercole, S. E., Pickering, S. J., Knight, C., & Stegmann, Z. (2004).Working memory skills and educational attainment: Evidence from national curriculum assessments at 7 and 14 years of age. Applied Cognitive Psychology, 18, 1-16.
Gazzaniga, M., Ivry, R., & Mangun, G. (2002).Cognitive neuroscience: The biology of the mind (2nd ed.). New York, NY: W.W. Norton
Grimley, M., & Banner, G. (2008).Working memory, cognitive style, and behavioural predictors of GCSE exam success. Educational Psychology, 28(3), 341-351.
Grossberg, S. (1980). How does the brain build a cognitive code? Psychological Review, 87, 1- 51.
Grossberg, S. (1982). Studies of mind and brain: Neural principles of learning, perception, development, cognition and motor control. Boston: Reidel Press.
Gsanger, K., W., Homack, S., Siekierski, B., & Riccio, C. (2002).The relation of memory and attention to academic achievement in children. Archives of Clinical Neuropsychology, 17(8), 790.
Hailikari, T., Nevgi, & A., Komulainen, E. (2008). Academic self-beliefs and prior knowledge as predictors of student achievement in Mathematics: a structural model. Educational Psychology, 28(1), 59-71.
Hardgrave, B. C., Wilson, R. L., & Walstrom, K. A. (1994).Predicting Graduate Student Success: A Comparison of Neural Networks and Traditional Techniques. Computer and Operations Research, 21(3), 249-263.
Hazy, T. E., Frank, M. J., & O’ Reilly, R. C. (2006). Banishing the Homunculus: Making Working Memory Work, Neuroscience 139, 105–118.
Heitz, R. P., Redick, T. S., Hambrick, D. Z., Kane, M. J., Conway, A. R. A., & Engle, R. W. (2006). Working memory, executive function, and general fluid intelligence are not the same. Behavioral and Brain Sciences, 29, 135-136.
Jarrold, C., & Towse, J. N. (2006). Individual differences in working memory. Neuroscience, 139, 39-50.
Jimmerson, S. R., Dubrow, E. H., Adam, E., Gunnar, M., & Bozoky, I. K. (2006).Associations among academic achievement, attention, and andrenocortical reactivity in Caribbean village children. Canadian Journal of School Psychology, 21, 120-138.
Kanakana, G., & Olanrewaju, A. (2011).Predicting student performance in engineering education using an artificial neural network at Tshwane University of Technology, Proceedings of the ISEM, Stellenbosch, South Africa.
Kane, M. J., Hambrick, D. Z., Tuholski, S.W., Wilhelm, O., Payne, T.W., & Engle, R.W. (2004). The generality of working memory capacity: A latent variable approach to verbal and visuospatial memory span and reasoning. Journal of Experimental Psychology: General, 133, 189-217.
Kent, R. (2009). Rethinking data analysis – part two. Some alternatives to frequentist approaches. International Journal of Market Research, 51, 181-202.
Kobrin, J. L., Patterson, B. F., Shaw, E. J., Mattern, K. D., & Barbuti, S. M. (2008). Validity of the SAT for predicting first-year college grade point average. College Board Research Report 2008-5.New York: The College Board. Retrieved from http://research.collegeboard.org/rr2008-5.pdf.
Kohavi, R. & Provost, F. (1998).Glossary of terms. Machine Learning, 30(2–3): 271–274.
Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2001). A comprehensive meta-analysis of the predictive validity of the Graduate Record Examinations: Implications for graduate student selection and performance. Psychological Bulletin, 127(1), 162-181.
Kuncel, N. R., Crede, M., Thomas, L. L., Klieger, D.M., Seiler, S.N., & Woo, S.E. (2004). A meta-analysis of the Pharmacy College Admission Test (PCAT) and grade predictors of pharmacy student success. Annual conference of the American Psychological Society, Chicago, IL.
Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic performance, career potential, creativity, and job performance: Can one construct predict them all? Journal of Personality and Social Psychology, 86(1), 148-161.
Kuncel, N. R., Crede, M., Thomas, L. L., Klieger, D. M., Seiler, S. N., & Woo, S. E. (2005). A meta-analysis of the Pharmacy College Admission Test (PCAT) and grade predictors of pharmacy student success. American Journal of Pharmaceutical Education, 69(3), 339-347.
Krumm, S., Ziegler, M., Buehner, M. (2008). Reasoning and working memory as predictors of school grades. Learning and Individual Differences, 18 (2), 248-257.
Kyllonen, P. C., & Christal, R. E. (1990). Reasoning ability is (little more than) working-memory capacity?! Intelligence, 14, 389-433.
Kyllonen, P. C., & Stephens, D. L. (1990).Cognitive abilities as determinants of success in acquiring logic skill. Learning and Individual Differences, 2, 129-160.
Kyndt, E., Cascallar, E., & Dochy, F. (2012). Individual differences in working memory capacity and attention, and their relationship with students’ approaches to learning. Higher Education, 64(3), 285-297.
Kyndt, E., Musso, M., Cascallar, E., & Dochy, F. (2012, Submitted). Predicting academic performance: The role of cognition, motivation and learning approaches. A neural network analysis.Journal of Further and Higher Education.
Landerl, K. (2010). Temporal processing, attention, and learning disorders. Learning & Individual Differences, 20(5), 393-401.
Linn, R. L., & Hastings, C. N. (1984). A meta-analysis of the validity of predictors of performance in law school. Journal of Educational Measurement, 21, 245-259.
Lippman, R. (1987). An introduction to computing with neuralets. IEEE ASSP Magazine, 3(4), 4-22.
Lovett, M. W. (1979). The selective encoding of sentential information in normal reading development. Child Development, 50(3), 897.
Lykins, S., & Chance, D. (1992). Comparing artificial neural networks and multiple regression for predictive application, Proceedings of the Eight Annual Conference on Applied Mathematics, Edmond OK, 155-169
Marquez, L., Hill, T., Worthley, R., & Remus, W. (1991). Neural network models as an alternative to regression. Proceedings of the IEEE 24th Annual Hawaii International Conference on Systems Sciences, 4, 129-135.
Marshall, D. B., & English, D. J. (2000).Neural network modelling of risk assessment in child protective services. Psychological Methods, 5(1), 102-124.
Maucieri, L. P. (2003). Predicting behavior with an artificial neural network: A comparison with linear models of prediction (January 1, 2003). ETD Collection for Fordham University, NY, USA. Retrieved from http://fordham.bepress.com/dissertations/AAI3098134.
Mavrovouniotis, M. L. & Chang, S. (1992).Hierarchical neural networks. Computers & Chemical Engineering, 16(4), 347-369.
Miñano, P., Gilar, R., & Castejón, J. L. (2012) A structural model of cognitive-motivational variables as explanatory factors of academic achievement in Spanish Language and Mathematics. Anales de Psicología, 28(1), 45-54.
Mislevy, R. J. (2013). Measurement is a Necessary but not Sufficient Frame for Assessment. Measurement, 11, 47–49, 2013
Mislevy, R. J., Steinberg, L. S., & Almond, R. A. (2003). On the structure of educational assessments. Measurement: Interdisciplinary Research and Perspectives, 1, 3–67.
Mukta, P., & Usha, A., (2009). A study of academic performance of business school graduates using neural network and statistical techniques. Expert Systems with Applications, 36(4), 7865-7872.
Musso, M. F., & Cascallar, E. C. (2009a). New approaches for improved quality in educational assessments: Using automated predictive systems in reading and mathematics. Journal of Problems of Education in the 21st Century, 17, 134-151.
Musso, M. F., & Cascallar, E. C. (2009b).Predictive systems using artificial neural networks: An introduction to concepts and applications in education and social sciences. In M. C. Richaud & J. E. Moreno (Eds.).Research in Behavioural Sciences (Volume I), (pp. 433-459). Argentina: CIIPME/CONICET.
Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2012). Predicting mathematical performance: The effect of cognitive processes and self-regulation factors. Education Research International.Vol. 12.
Nasr, G. E., Badr, E. A., & Joun, C. (2002). Cross Entropy Error Function In Neural Networks: Forecasting Gasoline Demand. FLAIRS-02 Proceedings of the AAAI. Retrieved from http://www.aaai.org/Papers/FLAIRS/2002/FLAIRS02-075.pdf
Navas, L., Sampascual, G., & Santed, M. A. (2003). Predicción de las calificaciones de los estudiantes: la capacidad explicativa de la inteligencia general y de la motivación. [Prediction of students’ performance scores: the role of the general intelligence and motivation. Journal of General and Applied Psychology], 56(2), 225-237.
Neal, W., & Wurst, J. (2001). Advances in market segmentation. Marketing Research, 13(1), 14-18.
Passolunghi, M. C., & Pazzaglia, F. (2004). Individual differences in memory updating in relation to arithmetic problem solving. Learning and Individual Differences 14(4), 219-230.
Perkins, K., Gupta, L. & Tammana (1995). Predict item difficulty in a reading comprehension test with an artificial neural network. Language Testing, 12(1), 34-53.
Pickering, S. J. (2006). Working memory and education. USA: Academic Press.
Pinninghoff Junemann, M. A., Salcedo Lagos, P. A., & Contreras Arriagada, R. (2007).Neural networks to predict schooling failure/success. In J. Mira & J.R. ´Alvarez (Eds.), IWINAC 2007, Part II, LNCS 4528(pp. 571–579). Berlin / Heidelberg: Springer-Verlag.
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P.R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 452–502). San Diego, CA: Academic Press.
Posner, M. I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 41A, 19-45.
Posner, M. I., & Petersen, S. E. (1990). The attention system of the human brain. Annual Review Neuroscience. 13, 25-42.
Posner, M. I., & Rothbart, M. K. (1998). Attention, self-regulation and consciousness. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 353, 1915–1927.
Ramaswami, M. M., & Bhaskaran, R. R. (2010). A CHAID based performance prediction model in educational data mining. International Journal of Computer Science Issues, 7(1), 10-18.
Redick, T. S., & Engle, R.W. (2006).Working memory capacity and attention network test performance. Applied Cognitive Psychology, 20, 713-721.
Reid, R. (2006). Self-regulated strategy development for written expression with students with attention deficit/ hyperactivity disorder. Exceptional Children, 73(1), 53-67.
Riccio, C. A., Lee, D., Romine, C. Cash, D., & Davis, B. (2002).Relation of memory and attention to academic achievement in adults. Archives of Clinical Neuropsychology, 18(7), 755-756.
Riding, R. J., Grimley, M., Dahraei, H., & Banner, G. (2003).Cognitive style, working memory and learning behaviour and attainment in school subjects. British Journal of Educational Psychology, 73, 749-769.
Roth, P. L., Be Vier, C. A., Switzer, F. S., & Schippmann, J. S. (1996). Meta-analyzing the relationship between grades and job performance. Journal of Applied Psychology, 81, 548-556.
Roth, P. L., & Clarke, R. L. (1998). Meta-analyzing the relation between grades and salary. Journal of Vocational Behavior, 53, 386-400.
Ruban, L. M., & McCoach, D. B. (2005). Gender differences in explaining grades using structural equation modeling. The Review of Higher Education, 28, 475-502.
Rueda, M. R., Posner, M. I., & Rothbart, M. K. (2004). Attentional control and self regulation. In R.F. Baumeister & K.D. Vohs (Eds), Handbook of Self Regulation: Research, Theory, and Applications, New York: Guilford Press, 14: 283-300.
Rumelhart, D., Hinton, G. & Williams, R. (1986). Learning representations by back-propagating errors. Nature, 323, 533- 536.
Rumelhart, D. E., McClelland, J. L., & the PDP research group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Volume I. Cambridge, MA: MIT Press.
Schmidt, F. L. (2002). The role of general cognitive ability and job performance: Why there cannot be a debate. Human Performance, 15, 187–210.
Segers, M., Dochy, F., & Cascallar, E. (2003).Optimizing new modes of assessment: In search of qualities and standards.The Netherlands: Kluwer Academic Publishers.
Simons, J., Dewitte, S., & Lens, W. (2004). The role of different types of instrumentality in motivation, study strategies, and performance: Know why you learn, so you'll know what you learn! British Journal of Educational Psychology, 74, 343-360.
Snyderman, M., & Rothman, S. (1987). Survey of expert opinion on intelligence and aptitude testing. American Psychologist, 42(2), 137-144
Specht, D. (1991). A general regression neural network. IEEE transactions on neural networks, 2(6), 568-576.
St Clair-Thompson, H. L., & Gathercole, S. E. (2006). Executive functions and achievements in school: Shifting, updating, inhibition, and working memory. The Quarterly Journal of Experimental Psychology, 59(4), 745-759.
Strucchi, E. (1991). Inventario de Estrategias de Aprendizaje y de Estudio. [Learning Strategies Inventory and Study]. Buenos Aires: Psicoteca.
Turner, E. A., Chandler, M., & Heffer, R. W. (2009). Influence of parenting styles, achievement motivation, and self-efficacy on academic performance in college students. Journal of College Student Development, 50, 3, 337-346.
Unsworth, N., Heitz, R. P., Schrock, J. C., & Engle, R. W. (2005). An automated version of the operation span task. Behavior Research Methods, 37(3), 498-505.
Vandamme, J. P., Meskens, N., & Superby, J. F. (2007). Predicting academic performance by data mining methods.Education Economic, 15(4), 405-41.
Walczak, S. (1994). Categorizing university student applicants with neural networks. IEEE International conference on neural networks, 6, 3680-3685.
Weinstein, C. E., & Mayer, R.E. (1986). The teaching of learning strategies. In M.C. Wittrock (Ed.), Handbook of research on teaching (3rd ed.). Macmillan, New York.
Weinstein, C. E. & Palmer, D. R. (2002). LASSI: User’s Manual (2nd Edition). Clearwater, FL: H&H Publishing Company, Inc.
Weinstein, C. E., Palmer, D. R., & Schulte, A. C. (1987).Learning and study strategies inventory. Clearwater, FL: H & H Publishing company, Inc.
Weinstein, C. E., Schulte, A. C, & Cascallar, E. C. (1982). The learning and studies strategies inventory (LASSI): Initial design and development. Technical Report, US Army Research Institute for the Social and Behavioural Sciences, Alexandria, VA.
Weiss, S. M. & Kulikowski, C. A. (1991). Computer systems that learn. San Mateo, CA: Morgan Kaufmann Publishers.
Welsh, M.C., Satterlee-Cartmell, T., & Stine, M. (1999). Towers of Hanoi and London: Contribution of working memory and inhibition to performance. Brain Cognition, 41(2), 231-242.
White, H. & Racine, J. (2001): Statistical inference, the bootstrap, and neural network modelling with application to foreign exchange rates. IEEE Transactions on Neural Networks: Special Issue on Neural Networks in Financial Engineering, 12, 657-673.
Wilson, R. L. & Hardgrave, B. C. (1995). Predicting graduate student success in a MBA program: Regression vs. classification. Educational and Psychological Measurement, 55, 186-195.
Zambrano Matamala, C., Rojas Díaz, D., Carvajal Cuello, K., & Acuña Leiva, G. (2011). Análisis de rendimiento académico estudiantil usando data warehouse y redes neuronales. [Analysis of students’ academic performance using data warehouse and neural networks] Ingeniare. Revista Chilena de Ingeniería, 19(3), 369-381.
Zeegers, P. (2004). Student learning in higher education: A path analysis of academic achievement in science. Higher Education Research & Development, 23(1), 35-56.