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The effectiveness of an online learning system based on aptitude scores: An effort to improve students’ brain activation

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Abstract

The differences in learning preferences can be attributed to the differences in individuals’ cognitive capacities which may lead them to undertake a certain behavior. It is argued that characterizing the learning complexity based on the volume of information presented to learners can eliminate any avoidable load on working memory. This study examined the effectiveness of an online continuous adaptive mechanism (OCAM) based on changes in learner aptitude scores across learning sessions. The representation of the learning content in these sessions was designed for a low-, medium-, and high-aptitude individual. The brain activation of 12 students (6 male and 4 female; aged 20–25 years), obtained from using the proposed system, was examined using an electroencephalogram (EEG). The result showed that OCAM helped learners to understand the content being presented according to their aptitude scores, thus improving their brain activation. Findings from this study can be used to inform online system designers and developers about the importance of incorporating aptitude scores for customizing the representation of learning materials in an online environment.

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References

  • Al-Omari, M., Carter, J., & Chiclana, F. (2016). A hybrid approach for supporting adaptivity in e-learning environments. The International Journal of Information and Learning Technology, 33(5), 333–348.

    Article  Google Scholar 

  • Al-Samarraie, H., & Ahmad, Y. (2016). Use of design patterns according to hand dominance in a mobile user interface. Journal of Educational Computing Research, 54(6), 769–792.

    Article  Google Scholar 

  • Al-Samarraie, H., Teo, T., & Abbas, M. (2013). Can structured representation enhance students' thinking skills for better understanding of E-learning content? Computers & Education, 69, 463–473.

    Article  Google Scholar 

  • Al-Samarraie, H., Selim, H., & Zaqout, F. (2016). The effect of content representation design principles on users’ intuitive beliefs and use of e-learning systems. Interactive Learning Environments, 24(8), 1758–1777.

    Article  Google Scholar 

  • Al-Samarraie, H., Selim, H., Teo, T., & Zaqout, F. (2017). Isolation and distinctiveness in the design of e-learning systems influence user preferences. Interactive Learning Environments, 25(4), 452–466.

    Article  Google Scholar 

  • Altintas, T., Gunes, A., & Sayan, H. (2016). A peer-assisted learning experience in computer programming language learning and developing computer programming skills. Innovations in Education and Teaching International, 53(3), 329–337.

    Article  Google Scholar 

  • Barlow-Jones, G., & van der Westhuizen, D. (2017). Problem solving as a predictor of programming performance. Paper presented at the Annual Conference of the Southern African Computer Lecturers’ Association, Problem Solving as a Predictor of Programming Performance.

  • Becker, K. L. (2005). Individual and organisational unlearning: Directions for future research. International Journal of Organisational Behaviour, 9(7), 659–670.

    Google Scholar 

  • Beckerman, T. M., & Good, T. L. (1981). The classroom ratio of high-and low-aptitude students and its effect on achievement. American Educational Research Journal, 18(3), 317–327.

    Article  Google Scholar 

  • Blaschke, L. M. (2012). Heutagogy and lifelong learning: A review of heutagogical practice and self-determined learning. The International Review of Research in Open and Distributed Learning, 13(1), 56–71.

    Article  Google Scholar 

  • Bong, M. (2001). Role of self-efficacy and task-value in predicting college students' course performance and future enrollment intentions. Contemporary Educational Psychology, 26(4), 553–570.

    Article  MathSciNet  Google Scholar 

  • Buckley, W. (2008). Society as a complex adaptive system. Emergence: Complexity and Organization, 10(3), 86.

    Google Scholar 

  • Clark, R. C., & Mayer, R. E. (2011). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. John Wiley & Sons.

  • Cocea, M., & Weibelzahl, S. (2007). Eliciting motivation knowledge from log files towards motivation diagnosis for adaptive systems. Paper presented at the International Conference on User Modeling, Eliciting Motivation Knowledge from Log Files Towards Motivation Diagnosis for Adaptive Systems.

  • Dolmans, D. H., De Grave, W., Wolfhagen, I. H., & Van Der Vleuten, C. P. (2005). Problem-based learning: Future challenges for educational practice and research. Medical Education, 39(7), 732–741.

    Article  Google Scholar 

  • Freeman, F. G., Mikulka, P. J., Prinzel, L. J., & Scerbo, M. W. (1999). Evaluation of an adaptive automation system using three EEG indices with a visual tracking task. Biological Psychology, 50(1), 61–76.

    Article  Google Scholar 

  • Frost, S., & McCalla, G. I. (2015). An approach to developing instructional planners for dynamic open-ended learning environments. Paper presented at the AIED Workshops.

  • Garrison, D. R. (2011). E-learning in the 21st century: A framework for research and practice. Taylor & Francis.

  • Georgouli, K. (2002). The design of a ‘motivating’intelligent assessment system. Paper presented at the International Conference on Intelligent Tutoring Systems.

  • Goldberg, B., Brawner, K., Sottilare, R., Tarr, R., Billings, D. R., & Malone, N. (2012). Use of evidence-based strategies to enhance the extensibility of adaptive tutoring technologies. Paper presented at the Interservice/industry training, simulation, and education conference (I/ITSEC).

  • Grand, J. A., Braun, M. T., Kuljanin, G., Kozlowski, S. W., & Chao, G. T. (2016). The dynamics of team cognition: A process-oriented theory of knowledge emergence in teams. Journal of Applied Psychology, 101(10), 1353–1385.

    Article  Google Scholar 

  • Greene, B. A., & Miller, R. B. (1996). Influences on achievement: Goals, perceived ability, and cognitive engagement. Contemporary Educational Psychology, 21(2), 181–192.

    Article  Google Scholar 

  • Guzdial, M. (2015). What's the best way to teach computer science to beginners? Communications of the ACM, 58(2), 12–13.

    Article  Google Scholar 

  • Halder, S., Varkuti, B., Bogdan, M., Kübler, A., Rosenstiel, W., Sitaram, R., & Birbaumer, N. (2013). Prediction of brain-computer interface aptitude from individual brain structure. Frontiers in Human Neuroscience, 7, 105.

    Article  Google Scholar 

  • Halford, G. S., Wilson, W. H., & Phillips, S. (1998). Processing capacity defined by relational complexity: Implications for comparative, developmental, and cognitive psychology. Behavioral and Brain Sciences, 21(06), 803–831.

    Article  Google Scholar 

  • Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior, 54, 170–179.

    Article  Google Scholar 

  • Hamilton, E., & Cherniavsky, J. (2006). Issues in synchronous versus asynchronous elearning platforms. In H. F. O’Neil & R. S. Perez (Eds), Web-based learning: Theory, research and practice. Mahway:Erlbaum.

  • Higgins, E. T. (2005). Value from regulatory fit. Current Directions in Psychological Science, 14(4), 209–213.

    Article  Google Scholar 

  • Highsmith, J. (2013). Adaptive software development: A collaborative approach to managing complex systems. Addison-Wesley.

  • Holland, J. H. (2006). Studying complex adaptive systems. Journal of Systems Science and Complexity, 19(1), 1–8.

    Article  MathSciNet  MATH  Google Scholar 

  • Hsu, C.-K., Hwang, G.-J., & Chang, C.-K. (2013). A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students. Computers & Education, 63, 327–336.

    Article  Google Scholar 

  • Hwang, G.-J., Sung, H.-Y., Hung, C.-M., Huang, I., & Tsai, C.-C. (2012). Development of a personalized educational computer game based on students’ learning styles. Educational Technology Research and Development, 60(4), 623–638.

    Article  Google Scholar 

  • Ironsmith, M., & Eppler, M. A. (2007). Faculty forum: Mastery learning benefits low-aptitude students. Teaching of Psychology, 34(1), 28–31.

    Google Scholar 

  • Kandler, C., Riemann, R., Angleitner, A., Spinath, F. M., Borkenau, P., & Penke, L. (2016). The nature of creativity: The roles of genetic factors, personality traits, cognitive abilities, and environmental sources. Journal of Personality and Social Psychology, 111(2), 230–249.

    Article  Google Scholar 

  • Kanfer, R., & Ackerman, P. L. (1989). Motivation and cognitive abilities: An integrative/aptitude-treatment interaction approach to skill acquisition. Journal of Applied Psychology, 74(4), 657–690.

    Article  Google Scholar 

  • Kiss, C., & Nikolov, M. (2005). Developing, piloting, and validating an instrument to measure young learners’ aptitude. Language Learning, 55(1), 99–150.

    Article  Google Scholar 

  • Koper, R., & Tattersall, C. (2005). Preface to learning design: A handbook on modelling and delivering networked education and training. Journal of Interactive Media in Education, 2005(1).

  • Lambert, L. (2015). Factors that predict success in CS1. Journal of Computing Sciences in Colleges, 31(2), 165–171.

    Google Scholar 

  • Linder, U., & Rochon, R. (2003). Using chat to support collaborative learning: Quality assurance strategies to promote success. Educational Media International, 40(1–2), 75–90.

    Article  Google Scholar 

  • Moons, J., & De Backer, C. (2013). The design and pilot evaluation of an interactive learning environment for introductory programming influenced by cognitive load theory and constructivism. Computers & Education, 60(1), 368–384.

    Article  Google Scholar 

  • Olson, I. R., & Berryhill, M. (2009). Some surprising findings on the involvement of the parietal lobe in human memory. Neurobiology of Learning and Memory, 91(2), 155–165.

    Article  Google Scholar 

  • Park, O.-C., & Lee, J. (2003). Adaptive instructional systems. Educational Technology Research and Development, 25, 651–684.

    Google Scholar 

  • Pfurtscheller, G., Neuper, C., Brunner, C., & Da Silva, F. L. (2005). Beta rebound after different types of motor imagery in man. Neuroscience Letters, 378(3), 156–159.

    Article  Google Scholar 

  • Regian, J. W., Shute, V. J., & Shute, V. (2013). Cognitive approaches to automated instruction. Routledge.

  • Rodríguez, V., & Ayala, G. (2012). Adaptivity and Adpatability of learning Object's Interface. International Journal of Computer Applications, 37(1), 6–13.

    Article  Google Scholar 

  • Ross, B., Chase, A.-M., Robbie, D., Oates, G., & Absalom, Y. (2018). Adaptive quizzes to increase motivation, engagement and learning outcomes in a first year accounting unit. International Journal of Educational Technology in Higher Education, 15(1), 30.

    Article  Google Scholar 

  • Shute, V., & Towle, B. (2003). Adaptive e-learning. Educational Psychologist, 38(2), 105–114.

    Article  Google Scholar 

  • Siegle, G. J., Ichikawa, N., & Steinhauer, S. (2008). Blink before and after you think: Blinks occur prior to and following cognitive load indexed by pupillary responses. Psychophysiology, 45(5), 679–687.

    Article  Google Scholar 

  • Snow, R. E. (1989). Toward assessment of cognitive and conative structures in learning. Educational Researcher, 18(9), 8–14.

    Article  Google Scholar 

  • Snow, R. E. (1992). Aptitude theory: Yesterday, today, and tomorrow. Educational Psychologist, 27(1), 5–32.

    Article  Google Scholar 

  • Söderlind, J., & Geschwind, L. (2017). More students of better quality? Effects of a mathematics and physics aptitude test on student performance. European Journal of Engineering Education, 42(4), 445–457.

    Article  Google Scholar 

  • Soleimani, H., & Rezazadeh, M. (2013). The effect of increase in task cognitive complexity on Iranian EFL learners’ accuracy and linguistic complexity: A test of Robinson’s cognition hypothesis. Applied Research on English Language, 53(12), 41.

    Google Scholar 

  • Sterman, M. B., Kaiser, D. A., & Veigel, B. (1996). Spectral analysis of event-related EEG responses during short-term memory performance. Brain Topography, 9(1), 21–30.

    Article  Google Scholar 

  • Stern, M. K., & Woolf, B. P. (2000). Adaptive content in an online lecture system. Paper presented at the Adaptive hypermedia and adaptive Web-based systems, Adaptive Content in an Online Lecture System.

  • Swanson, H. L. (1990). Influence of metacognitive knowledge and aptitude on problem solving. Journal of Educational Psychology, 82(2), 306–314.

    Article  Google Scholar 

  • Taraban, R., Anderson, E. E., DeFinis, A., Brown, A. G., Weigold, A., & Sharma, M. (2007). First steps in understanding engineering students' growth of conceptual and procedural knowledge in an interactive learning context. Journal of Engineering Education, 96(1), 57–68.

    Article  Google Scholar 

  • Towle, B., & Halm, M. (2005). Designing adaptive learning environments with learning design Learning design (pp. 215–226). Springer.

  • Truong, H. M. (2016). Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in Human Behavior, 55, 1185–1193.

    Article  Google Scholar 

  • Yang, T.-C., Hwang, G.-J., & Yang, S. J.-H. (2013). Development of an adaptive learning system with multiple perspectives based on students' learning styles and cognitive styles. Journal of Educational Technology & Society, 16(4), 185–200.

    Google Scholar 

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Acknowledgements

This study was supported by Research University Grant (No. 1001/PMEDIA/8016063) of Universiti Sains Malaysia.

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Correspondence to Hosam Al-Samarraie.

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Eldenfria, A., Al-Samarraie, H. The effectiveness of an online learning system based on aptitude scores: An effort to improve students’ brain activation. Educ Inf Technol 24, 2763–2777 (2019). https://doi.org/10.1007/s10639-019-09895-2

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