The advancement of computers and communication technologies has encouraged an increasing number of studies concerning personalized web-based learning, in which students are able to learn in individual learning instruction; in particular, the students can receive formatted learning material associated with their learning preference. Although such an approach seems interesting to the students, researchers have emphasized the need for well-designed instruction in order to improve the students’ learning achievements. Therefore, it has become an important issue to employ art of teaching to assist the students to learn in a personalized web-based learning environment. Based on this perspective, this study proposes an integrated learning styles and scientific investigation-based approach for improving the learning achievements of students in a personalized web-based learning environment. A personalized web-based learning environment has been developed based on this approach, and an experiment on a physics course has been conducted in northeastern Thailand to evaluate its effectiveness. The experimental results show that the proposed approach improves the students’ learning achievement. Moreover, the students had positive perceptions toward the personalized web-based learning based on the proposed approach.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Akbulut, Y., & Cardak, C. S. (2012). Adaptive educational hypermedia accommodating learning styles: Content analysis of publications from 2000 to 2011. Computers and Education, 58(2), 835–842.
Bai, S. M., & Chen, S. M. (2008). Automatically constructing concept maps based on fuzzy rules for adapting learning systems. Expert Systems with Applications, 35, 41–49.
Cheng, G. (2014). Exploring students’ learning styles in relation to their acceptance and attitudes towards using Second Life in education: Acase study in Hong Kong. Computers and Education, 70, 105–115.
Cheng, S. Y., Lin, C. S., Chen, H. H., & Heh, J. S. (2005). Learning and diagnosis of individual and class conceptual perspectives: An intelligent systems approach using clustering techniques. Computers and Education, 44, 257–283.
Chookaew, S., Panjaburee, P., Wanichsan, D., & Laosinchai, P. (2014). A personalized e-learning environment to promote student’s conceptual learning on basic computer programming. Procedia-Social and Behavioral Sciences, 116(21), 815–819.
Chookaew, S., Wanichsan, D., Hwang, G.-J., & Panjaburee, P. (2015). Effects of a personalised ubiquitous learning support system on university students’ learning performance and attitudes in computer-programming courses. International Journal of Mobile Learning and Organisation, 9(3), 240–257.
Collins, A. (1990). The role of computer technology in restructuring schools. In K. Sheingold & M. S. Tucker (Eds.), Restructuring for learning with technology (pp. 29–46). New York: Center for Technology in Education, Bank Street College and the National Center on Education and the Economy.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
de Jong, T., & van Joolingen, W. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179–201.
Dunn, R., Dunn, K., & Price, G. E. (1984). Productivity environmental preference survey. Lawrence: Price Systems.
Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Journal of Engineering Education, 78(7), 674–681.
Felder, R. M., & Spurlin, J. E. (2005). A validation study of the index of learning styles. Applications, reliability, and validity of the index of learning styles. International Journal of Engineering Education, 21(1), 103–112. From http://www.simplypsychology.org/learning-kolb.html.
Graf, S., Lin, T., & Kinshuk, (2007). The relationship between learning styles and cognitive traits–Getting additional information for improving student modelling. Computers in Human Behavior, 24(2), 122–137.
Huang, E. Y., Lin, S. W., & Huang, T. K. (2012). What type of learning style leads to online participation in the mixed-mode e-learning environment? A study of software usage instruction. Computers and Education, 58(1), 338–349.
Hwang, G. J. (2003). A conceptual map model for developing intelligent tutoring systems. Computers and Education, 40, 217–235.
Hwang, G.-J., & Chang, H. F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers and Education, 56(4), 1023–1031.
Hwang, G. J., Panjaburee, P., Shih, B. Y., & Triampo, W. (2013a). A group decision approach to developing concept effect models for diagnosing student learning problems. British Journal of Educational Technology, 44(3), 453–468.
Hwang, G. J., Sung, H. Y., Hung, C. M., & Huang, I. (2013b). A learning style perspective to investigate the necessity of developing adaptive learning systems. Educational Technology and Society, 16(2), 188–197.
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.
Kaburlasos, V. G., Marinagi, C. C., & Tsoukalas, V. T. (2008). Personalized multi-student improvement based on Bayesian cybernetics. Computers and Education, 51, 1430–1449.
Keefe, J. W. (1987). Learning styles: Theory and practice. Reston: National Association of Secondary School Principals.
Keefe, J. W. (1991). Learning style: Cognitive and thinking skills. Reston: National Association of Secondary School Principals.
Klasnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers and Education, 56(3), 558–899.
Kolb, D. A. (1984). Experimental learning: Experience as the source of learning and development. Enlewood Cliffs: Prentice Hall.
Kolb, D. A., Osland, J. S., & Rubin, I. M. (1995). Organization behaviour (6th ed.). New Jersey: Prince-Hall.
Kollöffel, B. (2012). Exploring the relation between visualizer–verbalizer cognitive styles and performance with visual or verbal learning material. Computer and Education, 58(2), 697–706.
Kubicek, P. J. (2005). Inquiry-based learning, the nature of science, and computer technology: New possibilities in science education. Canadian Journal of Learning and Technology, 31(1). Available at http://cjlt.csj.ualberta.ca/index.php/cjlt/article/view/149/142. Accessed 22 April 2014.
Kuhn, D., Black, J. B., Kesselman, A., & Kaplan, D. (2000). The development of cognitive skills to support inquiry learning. Cognition and Instruction, 18, 495–523.
Kuljis, J., & Liu, F. (2005). A comparison of learning style theories on the suitability for elearning. In M. H. Hamza (Ed.), Proceedings of the IASTED conference on web-technologies, applications, and services (pp. 191–197). Calgary: ACTA Press.
Litzinger, T., Lee, S. H., Wise, J. C., & Felder, R. M. (2007). A psychometric study of the index of learning styles. Journal of Engineering Education, 96(4), 309–319.
Magoulas, G., Papanikolaou, K., & Grigoriadou, M. (2003). Adaptive web-based learning: Accommodating individual differences through system’s adaptation. British Journal of Educational Technology, 34(4), 511–527.
Mampadi, F., Chen, S. Y., Ghinea, G., & Chen, M.-P. (2011). Design of adaptive hypermedia learning systems: A cognitive style approach. Computers and Education, 56(4), 1003–1011.
National Research Council. (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.
Panjaburee, P., Hwang, G. J., Triampo, W., & Shih, B. Y. (2010). A multi-expert approach for developing testing and diagnostic systems based on the concept-effect model. Computers and Education, 55(2), 527–540.
Panjaburee, P. & Srisawasdi, N. (2013a). Criteria and strategies for applying concept-effect relationship model in technological personalized learning environment. In: Proceedings of the 21st International Conference on Computers in Education 2013. Asia-Pacific Society for Computers in Education, Indonesia.
Panjaburee, P. & Srisawasdi, N. (2013b). Guideline for the development of personalized technology-enhanced learning in science, technology, and mathematics education. In: Proceedings of the 21st International Conference on Computers in Education 2013. Asia-Pacific Society for Computers in Education, Indonesia.
Papanikolaou, K. A., Grigoriadou, M., Magoulas, G. D., & Kornilakis, H. (2002). Towards new forms of knowledge communication: The adaptive dimension of a web-based learning environment. Computers and Education, 39, 333–360.
Papanikolaou, K. A., Mabbott, A., Bull, S., & Grigoriadou, M. (2006). Designing learner-controlled educational interactions based on learning/cognitive style and learner behaviour. Interacting with Computers, 18, 356–384.
Popescu, E. (2010). Adaptation provisioning with respect to learning styles in a Web-based educational system: An experimental study. Journal of Computer Assisted Learning, 26(4), 243–257.
Srisawasdi, N. (2012). Introducing students to authentic inquiry investigation by using an artificial olfactory system. In K. C. D. Tan, M. Kim, & S. W. Hwang (Eds.), Issues and challenges in science education research: Moving forward. Dordrecht: Springer.
Srisawasdi, N., & Kroothkeaw, S. (2014). Supporting students’ conceptual learning and retention of light refraction concepts by simulation-based inquiry with dual-situated learning model. Journal of Computers in Education, 1(1), 49–79.
Srisawasdi, N., & Panjaburee, P. (2014). Technology-enhanced Learning in science, technology, and mathematics education: Results on supporting student learning. Procedia-Social and Behavioral Sciences, 116(21), 946–950.
Srisawasdi, N. & Panjaburee, P. (2015). Personal learning activity approach for developing adaptive web-based learning systems. Proceedings of the 23rd International Conference on Computers in Education. China: Asia-Pacific Society for Computers in Education.
Srisawasdi, N., Srikasee, S., & Panjaburee, P. (2012). Development of a constructivist web-based learning system with student personalized conceptual profile. Proceeding from the 20th International Conference on Computers in Education. Asia-Pacific Society for Computers in Education, Singapore.
Steffe, L., & Gale, J. (Eds.). (1995). Constructivism in education. Hillsdale: Lawrence Erlbaum.
Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers and Education, 52(2), 302–312.
Tseng, J. C. R., Chu, H. C., Hwang, G. J., & Tsai, C. C. (2008a). Development of an adaptive learning system with two sources of personalization information. Computers and Education, 51(2), 776–786.
Tseng, S. S., Su, J. M., Hwang, G. J., Hwang, G. H., Tsai, C. C., & Tsai, C. J. (2008b). An object-oriented course framework for developing adaptive learning systems. Educational Technology and Society, 11(2), 171–191.
von Glasersfeld, E. (1989). Cognition, construction of knowledge and teaching. Synthese, 80(1), 121–140.
Wanichsan, D., Panjaburee, P., Laosinchai, P., Triampo, W., & Chookaew, S. (2012). A majority-density approach to developing testing and diagnostic systems with the cooperation of multiple experts based on an enhanced concept-effect relationship model. Expert Systems with Applications, 39(9), 8380–8388.
Wenning, C. J. (2005). Levels of inquiry: Hierarchies of pedagogical practices and inquiry processes. Journal of Physics Teacher Education Online, 2(3), 3–11.
Wenning, C. J. (2010). Levels of inquiry: Using inquiry spectrum learning sequences to teach science. Journal of Physics Teacher Education Online, 5(3), 11–20.
Wu, P. H., Hwang, G. J., Milrad, M., Ke, H. R., & Huang, Y. M. (2012). An innovative concept map approach for improving students’ learning performance with an instant feedback mechanism. British Journal of Educational Technology, 43(2), 217–232.
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. Educational Technology and Society, 16(4), 185–200.
This research project is supported by Mahidol University under Grant Numbers 67/2557 and 103/2558. The development of PhET interactive science simulation in Thai version is supported by the Office for Educational Technology Development Fund, Ministry of Education (MOE), Thailand.
About this article
Cite this article
Panjaburee, P., Srisawasdi, N. An integrated learning styles and scientific investigation-based personalized web approach: a result on conceptual learning achievements and perceptions of high school students. J. Comput. Educ. 3, 253–272 (2016). https://doi.org/10.1007/s40692-016-0066-1
- Teaching and learning strategies
- Interactive learning environments
- Web-based learning
- Applications in science areas