International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 5, No. 10, 8 Oct. 2013
Cover page and Table of Contents: PDF (size: 361KB)
PSICO-A: A Computational System for Learning Psychology
Full Text (PDF, 361KB), PP.1-8
Views: 0 Downloads: 0
Author(s)
Index Terms
PSICO-A, Psychology, Education, Intelligent Tutoring Systems, Digital Games, Simulations.
Abstract
PSICO-A is a new educational system, based on the web, for learning psychology. Its computational architecture consists of a front-end and a back-end. The first one contains a design mode, a reflective mode, a game mode and a simulation mode. These modes are connected to the back-end, which is composed of a rule engine, an evaluation module, a communication module, an expert module, a student module and a metacognitive module. The back-end is the heart of the system analysing the performance of pupils. PSICO-A assembles Boolean equations introducing algorithms such as those of Levenshtein, Hamming, Porter and Oliver. The system design used the programming language PHP5 for a clear and fast interface. PSICO-A is an innovative system because it is the first system in psychology designed for assessing the value of computer-based learning games compared with simulations for teaching the subject. Other systems use virtual environments for teaching subjects like mathematics, physics or ecology to children but the role of digital games and simulations in learning psychology is to date an unexplored field. A preliminary analysis of the motivational value of the system has been performed with sample of undergraduate students, verifying its advantages in terms of to encouraging scientific exploration. An internal evaluation of the system, using the game mode, has been conducted.
Cite This Paper
Javier González Marqués, Carlos Pelta, "PSICO-A: A Computational System for Learning Psychology", International Journal of Modern Education and Computer Science (IJMECS), vol.5, no.10, pp.1-8, 2013. DOI:10.5815/ijmecs.2013.10.01
Reference
[1]Sleeman, D. and Brown, J.S. (Eds.), Intelligent Tutoring Systems, Academic Press, London, 1982.
[2]Polson, M.C. and Richardson, J.J. (Eds.), Foundations of Intelligent Tutoring Systems, Lawrence Erlbaum Associates, Mahwah, NJ, 1988.
[3]Urretavizcaya, M., “Sistemas inteligentes en el ámbito de la educación”, Revista Iberoamericana de Inteligencia Artificial, 5: 5-12, 2001.
[4]Cataldi, Z., Salgueiro, F., Britos, P., Sierra, E. and García Martínez, R., “Selecting pedagogical protocols using SOM”, Research in Computing Science Journal, 21: 205-214, 2006.
[5]Nkambou, R., Bourdeau, J., and Mizoguchi, R. (Eds.), Advances in Intelligent Tutoring Systems, Springer-Verlag, Berlin, 2010.
[6]Ausubel, D., Novak, J., and Hanesian, H., Educational Psychology: A Cognitive View (2nd Ed.), Holt, Rinehart & Winston, New York, 1978.
[7]Azevedo, R., Witherspoon, A., Chauncey, A., Burkett, C., and Fike, A., “MetaTutor: a metacognitive tool for enhancing self-regulated learning”. In R. Pirrone, R. Azevedo, and G. Biswas (Eds.), Proceedings of the AAAI Fall Symposium on Cognitive and Metacognitive Educational Systems (pp. 14-19). AAAI Press, Menlo Park, CA, 2009.
[8]Biswas, G., Schwartz, D.L., Leelawong, K., Vye, N. and TAG-V, “Learning by teaching: A new agent paradigm for educational software”, Applied Artificial Intelligence, 19: 363-392, 2005, http://dx.doi.org/10.1080/08839510590910200.
[9]Bai, X., Black, J.B., and Vitale, J., “REAL: Learn with the Assistance of a reflective agent”, Agent-based systems for human learning Conference, Hawaii, 2007.
[10]Zimmerman, B.J., and D.H. Schunk (Eds.), Self-regulated learning and academic achievement: Theory, research and practice, Springer-Verlag, New York, 1989.
[11]Bembenutty, H., Self-regulated learning: New directions for teaching and learning, Jossey-Bass, San Francisco, 2011.
[12]Azevedo, R., Witherspoon, A., Graesser, A., McNamara, D., Chauncey, A., Siler, E., Cai, Z., Rus, V., and Lintean, M., “MetaTutor: Analyzing self-regulated learning in a tutoring system for biology”. In V. Dimitrova, R. Mizoguchi, B. du Bolay, and A. Graesser (Eds.), Building learning systems that care: from knowledge representation to affective modelling (pp. 635-637), IOS Press, Amsterdam, 2009, ftp://129.219.222.66/pdf/MetaTutor%20Analyzing%20SelfRegulated%20Learning%20in%20a%20Tutoring%20System%20
for%20Biology.pdf.
[13]Gartner, A., Conway, M. and Riessman, F., Children teach children. Learning by teaching, Harper & Row, New York, 1971.
[14]Barr, R. and Tagg, J., “From teaching to learning: a new paradigm for undergraduate education”, Change, 27: 12-25, 1995.
[15]Grzega, J., and Schöner, M., “The didactic model Ldl (Lernen durch Lehren) as a way of preparing students for communication in a knowledge society”, Journal of Education for Teaching, 34: 167-175, 2008,http://www.joachimgrzega.de/GrzegaSchoenerLd.pdf.
[16]Blair, K., Schwartz, D., Biswas, G., and Leelawong, K., “Pedagogical agents for learning by teaching: teachable agents”, Educational Technology and Society, Special Issue on Pedagogical Agents, 2006.
[17]Davis, J.M., Leelawong, K., Belynne, K., Bodenheimer, R., Biswas, G., Vye, N., and Bransford, J., “Intelligent user interface design for teachable agent systems”, International Conference on Intelligent User Interfaces (pp. 26-33), ACM, Miami, Florida, doi: 10.1.1.14.8457.pdf.
[18]Kinnebrew, J.S., and Biswas, G., “Modeling and measuring self-regulated learning in teachable agent environments”, Journal of E-learning and Knowledge society, 7: 19-35, 2011.
[19]Tan, J., Beers, C., Gupta, R., and Biswas, G., “Computer games as intelligent learning environments: a river ecosystem adventure”. In C.-K. Looi et al. (Eds.), Artificial Intelligence in education, IOS Press, Amsterdam, 2005.
[20]Biswas, G., Roscoe, R., Jeong, H., and Sulcer, B., “Promoting self-regulated learning skills in agent-based learning environments”. In S.C. Kong et al., Proceedings of the 17th International Conference on Computers in Education, Hong Kong, Asia-Pacific Society for Computers in education, 2009.
[21]Bai, X., and Black, J.B., “REAL: a generic Intelligent Tutoring System framework”. In C. Crawford et al. (Eds.), Proceedings of Society for Information Technology and Teacher Education International Conference (pp. 1279-1283), AACE, Chesapeake, 2005.
[22]Bai, X., and Black, J.B., “Enhancing Intelligent Tutoring Systems with the agent paradigm”. In V.V.A.A., Gaming and simulations: Concepts, methodologies, Tools and Applications (pp. 46-66), IGI Global, London, 2011.
[23]Black, J.B., “Imaginary worlds”. In M.A. Gluck, J.R. Anderson, and S.M. Kosslyn (Eds.), Memory and mind, Lawrence Erlbaum Associates, Mahwah, NJ, 2007, JblackImagination(2).doc.
[24]Novak, J., A theory of education, Cornell University Press, New York, 1977.
[25]Black, J.B., Types of knowledge representation, CCTE Report, New York, Teachers College, Columbia University.
[26]Dunlosky, J. and Metcalfe, J., Metacognition, SAGE, London, 2008.
[27]Karpicke, J.D. and Blunt, J.R., “Retrieval practice produces more learning than elaborative studying with concepts mapping”, Science, 331: 772:775, 2011, http://dx.doi.org/10.1126/science.1199327.
[28]Lerdorf, R., Tatroe, K., and MacIntyre, P., Programming PHP, O´Reilly Media, New York, 2006.
[29]Crespi, L.P., “Variation of incentive and performance in the white rat”, The American Journal of Psychology, 55: 467-517, 1945.
[30]Hull, L.C., Principles of behavior: an introduction to behavior theory, D. Appleton-Century Company, New York, 1943.
[31]Aldrich, C., Learning online with games, simulation, and virtual worlds, John Wiley & Sons, San Francisco, 2009.
[32]De Freitas, S.I., “Using games and simulations for supporting learning”, Learning Media and Technology, 31: 343-358, 2006.
[33]Oliver, I., Programming classics: implementing the world´s best algorithms, Prentice-Hall, Saddle River, NJ, 1994.
[34]Levenshtein, V., “Binary codes capable of correcting deletions, insertions and reversals”, Soviet Physics Doklady, 10: 707-710, 1966, http://profs.sci.univr.it/Liptak/ALBioinfo/files/levenhstein66.pdf.
[35]Hamming, R.W., “Error detecting and error correcting codes”, Bell System Technical Journal, 29: 147-160, 1950, http://www.lee.eng.uerj/hamming.pdf.
[36]Porter, M.F., “An algorithm for suffix strippin”, Program, 14: 130-137, 1980, http://dx.doi.org/10.11.
[37]Estrella, P. and Duboue, P.A., “Experiments on language normalization for Spanish to English machine translation”, Revista Iberoamericana de Inteligencia Artificial, 9: 23-37, 2005, http://dx.doi.org/10.4114/IA.V9126.843.
[38]Nelson, T.O., and Narens, L., “A new technique for investigating the feeling of knowing”, Acta Psychologica, 46: 69-80, 1980.
[39]Mayor, J., Suengas, A., and González Marqués, J., estrategias metacognitivas: aprender a aprender y aprender a pensar, Síntesis, Madrid, 1993.
[40]Bai, X., Black, J.B., Vikaros, L., Vitale, J., Li, D., and Xia, Q., “Learning in one´s own imaginary world”, American Educational Research Association, Chicago, 2007, doi: 10.1.1.135.1024 (1). pdf.
[41]Drever, E., Using semi-structured interviews in small-scale research. The Scottish Council for Research in Education, Edinburgh, 1997.
[42]Bruner, J., Actual minds, possible worlds, Harvard University Press, 1980.
[43]Glaserfeld, E., Constructivism in education, Pergamon Press, Oxford, 1989.