Non-cognitive factors of learning as predictors of academic performance in tertiary education

Conference code: 110385; Export Date: 23 February 2015; Correspondence Address: Gray, G.; Institute of Technology Blanchardstown, Blanchardstown Road North, Ireland; References: Achen, C., Intrepreting and using regression. Number 07-029 (1982) Quantitative Applications in the Social Sciences, , Sage Publications, Inc; Baker, R.S.J.D., Yacef, K., The state of educational data mining in 2009: A review and future visions (2010) Journal of Educational Data Mining, 1 (1), pp. 3-17; Bergin, S., (2006) Statistical and Machine Learning Models to Predict Programming Performance., , PhD thesis, Computer Science, NUI Maynooth; Bidjerano, T., Dai, D.Y., The relationship between the big-five model of personality and self-regulated learning strategies (2007) Learning and Individual Differences, 17, pp. 69-81; Biggs, J., Kember, D., Leung, D., The revised two-factor study process questionnaire: R-SPQ-2F (2001) British Journal of Education Psychology, 71, pp. 133-149; Buckingham Shum, S., Deakin Crick, R., Learning dispositions and transferable competencies. Pedagogy, modelling and learning analytics (2012) 2nd International Conference on Learning Analytics and Knowledge, pp. 92-101. , Vancouver, BC, Canada; Carpenter, J., Bithell, J., Bootstrap confidence intervals - When, which, what? A practical guide for medical statisticians (2000) Statistics in Medicine, 19, pp. 1141-1164; Cassidy, S., Exploring individual differences as determining factors in student academic achievement in higher education (2011) Studies in Higher Education, 37 (7), pp. 1-18; Chamorro-Premuzic, T., Furnham, A., Personality, intelligence and approaches to learning as predictors of academic performance (2008) Personality and Individual Differences, 44, pp. 1596-1603; Chatti, M.A., Dychhoff, A.L., Schroeder, U., Thüs, H., A reference model for learning analytics (2012) International Journal of Technology Enhanced Learning. Special Issue on State of the Art in TEL, pp. 318-331; Chow, G.C., Tests of equality between sets of coefficients in two linear regressions (1960) Econometrica, 28 (3), pp. 591-605; Cooper, A.J., Smillie, L.D., Corr, P.J., A confirmatory factor analysis of the mini-IPIP five-factor model personality scale (2010) Personality and Individual Differences, 48 (5), pp. 688-691; Covington, M.V., Goal theory, motivation, and school achievement: An integrative review (2000) Annual Review of Psychology, 51, pp. 171-200; De Feyter, T., Caers, R., Vigna, C., Berings, D., Unraveling the impact of the bigfive personality traits on academic performance. The moderating and mediating effects of self-efficacy and academic motivation (2012) Learning and Individual Differences, 22, pp. 439-448; Dekker, G., Pechenizkiy, M., Vleeshouwers, J., Predicting students drop out: A case study (2009) Proceedings of the 2nd International Conference on Educational Data Mining, Cordoba, Spain, pp. 41-50. , Barnes, T., Desmarais, M. C., Romero, C., and Ventura, S., editors; Drachsler, H., Greller, W., The pulse of learning analytics. Understandings and expectations from the stakeholders (2012) 2nd International Conference on Learning Analytics and Knowledge, pp. 120-129. , Vancouver, BC, Canada, 29 April- 2 May, ACM; Duff, A., Boyle, E., Dunleavy, K., Ferguson, J., The relationship between personality, approach to learning and academic performance (2004) Personality and Individual Differences, 36, pp. 1907-1920; Entwhistle, N., Contrasting perspectives in learning (2005) The Experience of Learning, pp. 3-22. , Marton, F., Hounsell, D., and Entwhistle, N., editors. Edinburgh: University of Edinburgh, Centre for Teaching, Learning and Assessment; Eppler, M.A., Harju, B.L., Achievement motivation goals in relation to academic performance in traditional and nontraditional college students (1997) Research in Higher Education, 38 (5), pp. 557-573; Farsides, T., Woodfield, R., Individual differences and undergraduate academic success: The roles of personality, intelligence, and application (2003) Personality and Individual Differences, 34, pp. 1225-1243; Fleming, N.D., I'm different not dumb. Modes of presentation (VARK) in the tertiary classroom (1995) Research and Development in Higher Education Proceedings of the 1995 Annual Conference of the Higher Education and Research Development Society of Australasia, 18, pp. 308-313; Goldberg, L.R., The development of markers for the big-five factor structure (1992) Psychological Assessment, 4 (1), pp. 26-42; Gray, G., McGuinness, C., Owende, P., An investigation of psychometric measures for modelling academic performance in tertiary education (2013) Sixth International Conference on Educational Data Mining, pp. 240-243. , D'Mello, S. K., Calvo, R. A., and Olney, A., editors, Memphis, Tennessee, July 6-9; Gray, G., McGuinness, C., Owende, P., An application of classification models to predict learner progression in tertiary education (2014) 4th IEEE International Advanced Computing Conference, pp. 549-554. , February; Gray, G., McGuinness, C., Owende, P., Carthy, A., A review of psychometric data analysis and applications in modelling of academic achievement in tertiary education (2014) Journal of Learning Analytics, 1 (1), pp. 75-106; Kang, Y., Harring, J.R., Reexamining the impact of non-normality in two-group comparison procedures Journal of Experimental Education, , in press; Kappe, R., Van Der Flier, H., Using multiple and specific criteria to assess the predictive validity of the bigfive personality factors on academic performance (2010) Journal of Research in Personality, 44, pp. 142-145; Kaufman, J.C., Agars, M.D., Lopez-Wagner, M.C., The role of personality and motivation in predicting early college academic success in non-traditional students at a hispanic-serving institution (2008) Learning and Individual Differences, 18, pp. 492-496; Knight, S., Buckingham Shum, S., Littleton, K., Epistemology, pedagogy, assessment and learning analytics (2013) Third Conference on Learning Analytics and Knowledge (LAK 2013), pp. 75-84. , Leuven, Belgium, April; Komarraju, M., Karau, S.J., Schmeck, R.R., Avdic, A., The bigfive personality traits, learning styles, and academic achievement (2011) Personality and Individual Differences, 51, pp. 472-477; Komarraju, M., Nadler, D., Self-efficacy and academic achievement. Why do implicit beliefs, goals, and effort regulation matter? (2013) Learning and Individual Differences, 25, pp. 67-72; Marton, F., Säljö, R., Approaches to learning (2005) The Experience of Learning, pp. 36-58. , Marton, F., Hounsell, D., and Entwhistle, N., editors. Edinburgh: University of Edinburgh, Centre for Teaching, Learning and Assessment; Mislevy, R.J., Behrens, J.T., Dicerbo, K.E., Design and discovery in educational assessment: Evidence-centered design, psychometrics, and educational data mining (2012) Journal of Educational Data Mining, 4 (1), pp. 11-48; Moran, M.A., Crowley, M.J., The leaving certificate and first year university performance (1979) Journal of Statistical and Social Enquiry in Ireland, 24, pp. 231-266; Ning, H.K., Downing, K., The reciprocal relationship between motivation and self-regulation: A longitudinal study on academic performance (2010) Learning and Individual Differences, 20, pp. 682-686; Pardos, Z.A., Baker, R.S.J.D., San Pedro, M.O.C.A., Gowda, S.M., Gowda, S.M., Affective states and state test. Investigating how affect throughout the school year predicts end of year learning (2013) Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK '13), pp. 117-124. , Leuven, Belgium, April. ACM; Pintrich, P., Smith, D., Garcia, T., McKeachie, W., A manual for the use of the motivated strategies for learning questionnaire (1991) Technical Report 91-B-004, , The Regents of the University of Michigan; Poropat, A.E., A meta-analysis of the five-factor model or personality and academic performance (2009) Psychological Bulletin, 135 (2), pp. 322-338; Robbins, S.B., Lauver Le, K.H., Davis, D., Langley, R., Do psychosocial and study skill factors predict college outcomes? A meta analysis (2004) Psychological Bulletin, 130 (2), pp. 261-288; Sachin, B.R., Vijay, S.M., A survey and future vision of data mining in educationalfield Advanced Computing Communication Technologies (ACCT), 2012 Second International Conference on, pp. 96-100. , Jan 2012; Shute, V., Ventura, M., (2013) Stealth Assessment. Measuring and Supporting Learning in Video Games, , The John D. and Catherine T. MacArthur Foundation Reports on Digital Media and Learning. MIT Press; Siemens, G., Learning analytics. Envisioning a research discipline and a domain of practice (2012) Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 4-8; Siemens, G., Baker, R.S.J.D., Learning analytics and educational data mining. Towards communication and collaboration (2012) Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 252-254; Swanberg, A.B., Martinsen Ø, L., Personality, approaches to learning and achievement (2010) Educational Psychology, 30 (1), pp. 75-88; Tavakol, M., Dennick, R., Making sense of Cronbach's alpha (2011) International Journal of Medical Education, 2, pp. 53-55; Tempelaar, D.T., Cuypers, H., Van De Vrie, E., Heck, A., Van Der Kooij, H., Formative assessment and learning analytics (2013) Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK '13), pp. 205-209. , New York, NY, USA, ACM; Tishman, S., Jay, E., Perkins, D.N., Teaching thinking disposition: From transmission to enculturation (1993) Theory into Practice, 32, pp. 147-153; Vancouver, J.B., Kendall, L.N., When self-efficacy negatively relates to motivation and performance in a learning context (2006) Journal of Applied Psychology, 91 (5), pp. 1146-1153; Volet, S.E., Cognitive and affective variables in academic learning: The significance of direction and effort in students' goals (1996) Learning and Instruction, 7 (3), pp. 235-254