PSICO-Aun sistema computacional integrado para la enseñanza de la psicología

  1. González Marqués, Javier
  2. Pelta Resano, Carlos Carlos
Aldizkaria:
Edutec: Revista electrónica de tecnología educativa

ISSN: 1135-9250

Argitalpen urtea: 2016

Zenbakia: 57

Mota: Artikulua

DOI: 10.21556/EDUTEC.2016.57.661 DIALNET GOOGLE SCHOLAR lock_openSarbide irekia editor

Beste argitalpen batzuk: Edutec: Revista electrónica de tecnología educativa

Laburpena

PSICO-A is a new educational system for teaching psychology. It is an innovative system because is the first system in psychology designed for learning didactic units of the subject. PSICO-A is based on many pedagogical influences, such as concept maps, free retrieval practice, effective feedback, simulations, digital games and metacognition. Besides its computational architecture is interesting because is composed of interconnected modules sequencing the tasks and because its back-end is a powerful tool for analysing the performance of pupils. We conducted an internal evaluation of the system comparing the learning outcomes of three groups corresponding to high-school students. Students in the first group were provided with text-based resources and the other experimental conditions involved students who worked in collaboration with PSICO-A. PSICO-A improved the learning. 

Erreferentzia bibliografikoak

  • ALONSO, J. (2012). Psicología. Bachillerato. Madrid: McGraw-Hill.
  • AUSUBEL, D.P (1968). Educational psychology: a cognitive view. N. York: Holt, Rinehart and Winston.
  • AZEVEDO R., WITHERSPOON, A., GRAESSER A., McNAMARA D., CHAUNCEY, A., SILER, E., CAI, Z., RUS, V. y LINTEAN, M. (2009). MetaTutor: analyzing self-regulated learning in a tutoring system for biology. En V. DIMITROVA, R. MIZOGUCHI, B. du BOLAY y A. GRAESSER (Eds.), Building learning systems that care: from knowledge representation to affective modeling (pp. 635-637). Amsterdam: IOS Press. DOI: ftp://129.219. 222.66/pdf/MetaTutor%%20Analyzing-%-%20Self-Regulated.pdf.
  • BAI, X. y BLACK, J. (2005). REAL: a generic intelligent tutoring system framework. En C. Crawford (Ed.), Proceedings of Society for information technology and teacher Education international conference (pp. 1279-1283). Chesapeake, VA: AACE.
  • BIMBO, A. del y VICARIO, J. (1995). Specification by-example of virtual agents behaviour, IEEE Transactions on Visualization and Computer Graphics, 350-360. DOI: 10.1109/2945.485622.
  • BLACK, J.B. (1992). Types of knowledge representation. N. York: CCTE Report Teachers College, Columbia University.
  • BRIGGS, G., SHAMMA, D.A, CAÑAS, A.J., CARFF, R., SCARGLE, J. y NOVAK, J.D (2004). Concept maps applied to Mars exploration public outreach. En A.J CAÑAS, J. NOVAK y F. GONZÁLEZ (Eds.), Concept maps: Theory, methodology, technology. Proceedings of the first international conference on concept mapping (Vol. I) (pp. 109-116). Pamplona: UPN.
  • BROWN, P.C., ROEDIGER , L.H. y McDANIEL, M.A. (2014). Make it stick: the science of successful learning. Belknap Press, Harvard.
  • CRESPI, L.P (1942). Quantitative variation of incentive and performance in the white rat, The American Journal of Psychology , 55, 467-517.
  • DAVIS, J.M, LEELAWONG, K., BELYNNE, K., BISWAS, G., VYE, N., BODENHEIMER, R y BRANSFORD, J. (2003). Intelligent user interface design for teachable agent systems, ICIUI, pp. 26-33, Miami FL. DOI: 10.1.1.14.8457.pdf.
  • DUNLOSKY, J. y METCALFE, J. (2008). Metacognition, Londres: SAGE.
  • ESTRELLA, P. y DUBOUE, P. A. (2005). Experiments on language normalization for spanish to english machine translation, RIIA, 9, 23-37. DOI: http://dx.doi.org/10.4114/IA.V9126.843.
  • GEE, J.P. (2007). What video games have to teach us about learning and literacy. N. York: Palgrave Macmillan.
  • GENTNER, D. y STEVENS, A.L (1983). Introduction. En D. GENTNER y A.L. STEVENS (Eds.), Mental models (pp. 1-6). Hillsdale, NJ: LEA.
  • GILBERT, S.W. (2011). Models-based science teaching. Arlington:NSTApress.
  • HAMMING, R.W (1950). Error detecting and error correcting codes,BSTJ,29, 147-160. DOI:http://www.lee.eng.uerj.br/gil/redesII/hamming.pdf.
  • HULL, C.L (1952). A behavior system: an introduction to behavior theory concerning the individual organism. N. Haven, CT: Yale University Press.
  • JONASSEN, D. y LAND, S. (2012). Theoretical foundations of learning environments. Londres: Routledge.
  • KARPICKE, J.D y BLUNT, J.R (2011). Retrieval practice produces more learning than elaborative studying with concept mapping, Science,331,772-775.DOI:http:/dx.doi.org/10.1126/science.1199327
  • LERDORF, R, TATROE, K. y MacINTYRE P. (2006). Programming PHP. N. York: O´Reilly Media.
  • LEVENSHTEIN, V (1966). Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady, 10,707-710. DOI:http://profs.sci.univr.it/-liptak/ALBioinfo/fil/levenshtein66.pdf.
  • MAYOR, J., SUENGAS, A. y GONZÁLEZ MARQUÉS, J. (1993). Estrategias metacognitivas: aprender a aprender y aprender a pensar. Madrid: Síntesis.
  • NOVAK, J. (1977). A theory of education. Ithaca, N. York: Cornell University Press.
  • OLIVER, I. (1994). Programming classics: implementing the world´s best algorithms. Upper-Saddle River. N. York: Prentice-Hall.
  • PAIVIO, A. (1971). Imagery and verbal processes. N. York: Holt, Rinehart, and Winston.
  • PORTER, M.F. (1980). An algorithm for suffix stripping. Program,14, 130-137.DOI: http://dx.doi.org/10.1108/eb046814.
  • SCALISE K. y WILSON, M. (2012). Assessment in game-based learning. En D. IFENTHALER, D. ESERYEL y G. XUN (Eds.), Measurement principles for gaming. Foundations, innovations, and perspectives (pp. 287-317). N. York: Springer-Verlag.
  • SLAMECKA, N.J y GRAF, P. (1978). The generation effect: delineation of a phenomenon. Journal of Experimental Psychology: Human Learning & Memory, 4, 592-604. DOI:10.1037/0278-7393.4.6.592.