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Individual differences in working memory capacity and attention, and their relationship with students’ approaches to learning

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Abstract

Past research has shown that working memory capacity, attention and students’ approaches to learning are all important predictors for educational achievement. In this study the interrelations between these three variables are investigated. Participants were 128 university students. Results show a negative relationship between attention and deep approaches to learning: students with a lower level of attention seem to use more deep approaches to learning than students with higher levels of attention. It was also found that students with a high working memory capacity score lower on both the surface and deep approaches to learning than students with a low working memory capacity. A possible explanation is that these high capacity students might not need a consistent profound strategy to be successful because they are very good at acquiring, processing and integrating all types of new information before moving it to storage.

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References

  • Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Anderson, J. R., & Matessa, M. (1997). A production system theory of serial memory. Psychological Review, 104(4), 728–748.

    Article  Google Scholar 

  • Anderson, J. R., Reder, J. M., & Lebière, C. (1996). Working memory: Activation limitations on retrieval. Cognitive Psychology, 30, 221–256.

    Article  Google Scholar 

  • Baeten, M., Kyndt, E., Struyven, K., & Dochy, F. (2010). Using student-centred learning environments to stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness. Educational Research Review, 5(3), 243–260.

    Article  Google Scholar 

  • Biggs, J. (1993). What do inventories of students’ learning processes really measure? A theoretical review and clarification. British Journal of Educational Psychology, 63, 3–19.

    Article  Google Scholar 

  • Biggs, J. B. (2001). Enhancing learning: A matter of style or approach? In R. J. Sternberg & L. Zhang (Eds.), Perspectives on thinking, learning, and cognitive styles (pp. 73–102). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Biggs, J., Kember, D., & Leung, D. Y. P. (2001). The revised two-factor study process questionnaire: R-SPQ-2F. British Journal of Educational Psychology, 71, 133–149.

    Article  Google Scholar 

  • Birenbaum, M., & Rosenau, S. (2006). Assessment preferences, learning orientations and learning strategies of pre-service and in-service teachers. Journal of Education for Teaching, 32(2), 213–225.

    Article  Google Scholar 

  • Byrne, M., Flood, B., & Willis, P. (2004). Using the student learning framework to explore the variation in academic performance of European business students. Journal of Further and Higher Education, 28(1), 67–78.

    Article  Google Scholar 

  • Cano, F. (2005). Epistemological beliefs and approaches to learning: Their change through secondary school and their influence on academic performance. British Journal of Educational Psychology, 75, 203–221.

    Article  Google Scholar 

  • Cascallar, E. C., & Musso, M. F. (2008). Classificatory stream analysis in the prediction of expected reading readiness: Understanding student performance. International Journal of Psychology, 43(3–4), 231.

    Google Scholar 

  • Cassidy, S. (2006). Learning style and student self-assessment skill. Education and Training, 48(2–3), 170–177.

    Google Scholar 

  • Chamorro-Premuzic, T., & Arteche, A. (2008). Intellectual competence and academic performance: Preliminary validation of a model. Intelligence, 36, 564–573.

    Article  Google Scholar 

  • Chamorro-Premuzic, T., & Furnham, A. (2008). Personality, intelligence and approaches to learning as predictors of academic performance. Personality and Individual Differences, 44, 1596–1603.

    Article  Google Scholar 

  • Chamorro-Premuzic, T., Furnham, A., & Lewis, M. (2007). Personality and approaches to learning predict preference for different teaching methods. Learning and Individual Differences, 17, 241–250.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Conway, A. R. A., Kane, M. J., & Engle, R. W. (2003). Working memory capacity and its relation to general intelligence. Trends in Cognitive Science, 7(12), 547–552.

    Article  Google Scholar 

  • Crawford, K., Gordon, S., Nicholas, J., & Prosser, M. (1998). Qualitatively different experiences of learning mathematics at university. Learning and Instruction, 8(5), 455–468.

    Article  Google Scholar 

  • Deci, E. L., & Ryan, R. M. (2004). Handbook of self-determination research. Rochester: University of Rochester Press.

    Google Scholar 

  • Diseth, A. (2002). The relationship between intelligence, approaches to learning and academic achievement. Scandinavian Journal of Educational Research, 46(2), 219–230.

    Article  Google Scholar 

  • Diseth, A., & Martinsen, O. (2003). Approaches to learning, cognitive style, and motives as predictors of academic achievement. Educational Psychology, 23(2), 195–207.

    Article  Google Scholar 

  • Diseth, A., Pallesen, S., Brunborg, G. S., & Larsen, S. (2010). Academic achievement among first semester undergraduate psychology students: The role of course experience, effort, motives and learning strategies. Higher Education, 59(3), 335–352.

    Article  Google Scholar 

  • Dunlosky, J., & Kane, M. J. (2007). The contributions of strategy use to working memory span: A comparison of strategy assessment methods. The Quarterly Journal of Experimental Psychology, 60(9), 1227–1245.

    Article  Google Scholar 

  • Engle, R. W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science, 11, 19–23.

    Article  Google Scholar 

  • Entwistle, N. J. (1988). Motivational factors in students’ approaches to learning. In R. R. Schmeck (Ed.), Learning strategies and learning styles (pp. 21–51). New York: Plenum Press.

    Google Scholar 

  • Entwistle, N. J. (1991). Approaches to learning and perceptions of the learning environment. Introduction to the special issue. Higher Education, 22, 201–204.

    Article  Google Scholar 

  • Entwistle, N. J., McCune, V., & Walker, P. (2001). Conceptions, styles, and approaches within higher education: Analytical abstractions and everyday experience. In R. J. Sternberg & L.-F. Zhang (Eds.), Perspectives on cognitive, learning and thinking styles (pp. 103–136). New Jersey: Lawrence Erlbaum Associates.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Gathercole, S. E., Alloway, T. P., Kirkwood, H. J., Elliot, J. G., Holmes, J., & Hilton, K. A. (2008). Attentional and executive function behaviours in children with poor working memory. Learning and Individual Differences, 18, 214–223.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gazzaniga, M., Ivry, R., & Mangun, G. (2002). Cognitive neuroscience: The biology of the mind (2nd ed.). New York: W.W. Norton.

    Google Scholar 

  • Gibbs, G. (1992). Improving the quality of student learning. Bristol, UK: Technical and Education Services.

    Google Scholar 

  • Gijbels, D., Van De Watering, G., Dochy, F., & Van Den Bossche, P. (2005). The relationship between students’ approaches to learning and the assessment of learning outcomes. European Journal of Psychology of Education, 20(4), 327–341.

    Article  Google Scholar 

  • Grimley, M., & Banner, G. (2008). Working memory, cognitive style, and behavioural predictors of GCSE exam success. Educational Psychology, 28(3), 341–351.

    Article  Google Scholar 

  • Gsanger, K., Wa, S., 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.

    Google Scholar 

  • Hoffman, B., & Schraw, G. (2009). The influence of self-efficacy and working memory capacity on problem solving efficiency. Learning and Individual Differences, 19(1), 91–100.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kember, D., Charlesworth, M., Dabies, H., MacKay, J., & Stott, V. (1997). Evaluating the effectiveness of educational innovations: Using the study process questionnaire to show that meaningful learning occurs. Studies in Educational Evaluation, 23(2), 141–157.

    Article  Google Scholar 

  • Klein, K., & Fiss, W. H. (1999). The reliability and stability of the Turner and Engle working memory task. Behavior Research Methods, Instruments and Computers, 31, 429–432.

    Article  Google Scholar 

  • LaBerge, D., Carlson, R. L., Williams, J. K., & Bunney, B. G. (1997). Shifting attention in visual space: Tests of moving-spotlight models versus an activity-distribution model. Journal of Experimental Psychology: Human Perception and Performance, 23(5), 1380–1392.

    Article  Google Scholar 

  • Learning Disabilities Association of Minnesota. (2007). Introduction to working memory. Netnews, 7(4), 1–3.

    Google Scholar 

  • Lynn, R., & Irwing, P. (2008). Sex differences in mental arithmetic, digit span, and ‘g’ defined as working memory capacity. Intelligence, 36(3), 226–235.

    Article  Google Scholar 

  • Marton, F., & Säljö, R. (1997). Approaches to learning. In F. Marton, D. Hounsell, & N. Entwistle (Eds.), The experience of learning. Implications for teaching and studying in higher education (2nd ed., pp. 39–58). Edinburgh: Scottish Academic Press.

    Google Scholar 

  • Masoura, E. V. (2006). Establishing the link between working memory function and learning disabilities. Learning disabilities: A contemporary journal, 4(2), 29–41.

    Google Scholar 

  • McParland, M., Noble, L., & Livingston, G. (2004). The effectiveness of problem-based learning compared to traditional teaching in undergraduate psychiatry. Medical Education, 38, 859–867.

    Article  Google Scholar 

  • Musso, M. F., & Cascallar, E. C. (2009). 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.

  • Naglieri, J. A., & Rohjahn, J. (2001). Gender differences in planning, attention, simultaneous and successive (PASS) cognitive processes and achievement. Journal of Educational Psychology, 93(2), 430–437.

    Article  Google Scholar 

  • Nijhuis, J., Segers, M., & Gijselaers, W. (2008). The extent of variability in learning strategies and students’ perceptions of the learning environment. Learning and Instruction, 18(2), 121–134.

    Article  Google Scholar 

  • Phan, H. P. (2007). An Examination of reflective thinking, learning approaches, and self-efficacy beliefs at the university of the South-Pacific: A path analysis approach. Educational Psychology, 27(6), 789–806.

    Article  Google Scholar 

  • Posner, M. I., & Petersen, S. E. (1990). The attention systems of the human brain. Annual Review of Neuroscience, 13, 25–42.

    Article  Google Scholar 

  • Redick, T. S., & Engle, R. W. (2006). Working memory capacity and attention network test performance. Applied Cognitive Psychology, 20, 713–721.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Schmeck, R. R. (1988). Strategies and styles of learning. An integration of varied perspectives. In R. R. Schmeck (Ed.), Learning strategies and learning styles (pp. 317–347). New York: Plenum Press.

    Google Scholar 

  • Sperling, G., & Weichselgartner, E. (1995). Episodic theory of the dynamics of spatial attention. Psychological Review, 102, 503–532.

    Article  Google Scholar 

  • Stes, A., De Maeyer, S., & Van Petegem, P. (2008). Students’ study approaches: A study into the validity and reliability of a Dutch version of the R-SPQ-2F. European learning styles information network, 13th annual conference. Gent, Belgium.

  • Struyven, K., Dochy, F., Janssens, S., & Gielen, S. (2006). On the dynamics of students’ approaches to learning: The effects of the teaching/learning environment. Learning and Instruction, 16, 279–294.

    Article  Google Scholar 

  • Swanson, H. L., Cochran, K. F., & Ewers, C. A. (1990). Can learning disabilities be determined from working memory performance? Journal of Learning Disabilities, 23(1), 59–67.

    Article  Google Scholar 

  • Trigwell, K., & Prosser, M. (1991). Relating approaches to study and quality of learning outcomes at the course level. British Journal of Educational Psychology, 61(3), 265–275.

    Article  Google Scholar 

  • Turner, M. L., & Engle, R. W. (1989). Is working memory capacity task dependent? Journal of Memory and Language, 28, 127–154.

    Article  Google Scholar 

  • Unsworth, N., & Engle, R. W. (2005). Individual differences in working memory capacity and learning: Evidence from the serial reaction time task. Memory & Cognition, 33(2), 213–220.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Unsworth, N., & Spillers, G. J. (2010). Working memory capacity: Attention control, second memory, or both? A direct test of the dual-component model. Journal of Memory and Language, 62(4), 392–406.

    Article  Google Scholar 

  • Valk, A., & Marandi, T. (2005). How to support deep learning at a university? In F. E. H. Tay, T. S. Chuan, & S. Han-Ming (Eds.), Proceeding of the international conference on education 2005. Singapore: National University of Singapore.

    Google Scholar 

  • Vermetten, Y., Vermunt, J., & Lodewijks, H. (2002). Powerful learning environment? How do university students differ in their response to instructional measures. Learning and Instruction, 12, 263–284.

    Article  Google Scholar 

  • Watkins, D. (2001). Correlates of approaches to learning: A cross-cultural meta-analysis. In R. Sternberg & L. Zhang (Eds.), Perspectives on thinking, learning, and cognitive styles (pp. 165–196). London: Erlbaum.

    Google Scholar 

  • Wilson, K., & Fowler, J. (2005). Assessing the impact of learning environments on students’ approaches to learning: Comparing conventional and action learning designs. Assessment and Evaluation in Higher Education, 30(1), 87–101.

    Article  Google Scholar 

  • Zeegers, P. (2001). Approaches to learning in science: A longitudinal study. British Journal of Educational Psychology, 71, 115–132.

    Article  Google Scholar 

  • Zeegers, P. (2004). Student learning in higher education: A path analysis of academic achievement in science. Higher Education Research & Development, 23(1), 35–56.

    Article  Google Scholar 

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  1. Centre for Research on Professional Learning and Development, Corporate Training and Lifelong Learning, Katholieke Universiteit Leuven, Dekenstraat 2, Box 3772, 3000, Leuven, Belgium

    Eva Kyndt, Eduardo Cascallar & Filip Dochy

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  1. Eva Kyndt
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  2. Eduardo Cascallar
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  3. Filip Dochy
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Correspondence to Eva Kyndt.

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Kyndt, E., Cascallar, E. & Dochy, F. Individual differences in working memory capacity and attention, and their relationship with students’ approaches to learning. High Educ 64, 285–297 (2012). https://doi.org/10.1007/s10734-011-9493-0

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