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The effects of learner factors on higher-order thinking in the smart classroom environment

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Abstract

Higher-order thinking is confirmed as the basic requirements in the era of knowledge age, both in learning and the workplace. Integrating information technology into the classroom is a common method to improve learners’ higher-order thinking by changing the environment. Smart classroom is a technology-enriched environment, in which technological affordances are used to facilitate learners’ higher-order thinking activities. Higher-order thinking is influenced by learner factors such as learning strategies and attitudes, which have changed in the smart classroom environment. This study investigated how learner factors interactively affected higher-order thinking in the smart classroom environment. A total of 784 pupils in 16 classes randomly selected from 9 primary schools in central China participated in this study. Structural equation modeling (SEM) revealed that learners’ higher-order thinking was strongly and directly affected by learning style and internet attitude, but not by learning motivation. Learning style partly mediated the positive relationship between internet attitude and higher-order thinking. A deeper learning experience should be provided with them, which can change their internet attitude and improve their higher-order thinking. This meaningful, coherent learning experience should be implemented to make learners’ internet attitude, learning motivation and learning style consistent, so as to improve higher-order thinking more effectively.

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References

  • Aesaert, K., & Nijlen, D. V. (2014). Direct measures of digital information processing and communication skills in primary education: Using item response theory for the development and validation of an ICT competence scale. Computers & Education, 76(76), 168–181.

    Article  Google Scholar 

  • Aesaert, K., van Braak, J., van Nijlen, D., & Vanderlinde, R. (2015). Primary school pupils’ ICT competences: Extensive model and scale development. Computers & Education, 81, 326–344. https://doi.org/10.1016/j.compedu.2014.10.021.

    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 

  • Ames, P. C. (2003). Gender and learning style interactions in students’ computer attitudes. Journal of Educational Computing Research, 28(3), 231–244.

    Article  Google Scholar 

  • Bagarukayo, E., Weide, T., Mbarika, V., & Kim, M. (2012). The impact of learning driven constructs on the perceived higher order cognitive skills improvement: Multimedia vs. text. International Journal of Education & Development Using Information & Communication Technology, 8, 120–130.

    Google Scholar 

  • Bradley, R. V., Sankar, C. S., Clayton, H. R., Mbarika, V. W., & Raju, P. K. (2010). A Study on the impact of GPA on perceived improvement of higher-order cognitive skills. Decision Sciences Journal of Innovative Education, 5(1), 151–168.

    Article  Google Scholar 

  • Budsankom, P., Sawangboon, T., Damrongpanit, S., & Chuensirimongkol, J. (2015). Factors affecting higher order thinking skills of students: A meta-analytic structural equation modeling study. Educational Research & Reviews, 10(19), 2639–2652.

    Article  Google Scholar 

  • Chytry, V., Kroufek, R., Janovec, J., Zilcher, L. (2016). Stability of logical thinking among pupils at elementary schools. INTED2016: 10th international technology, education and development conference (pp. 3968–3975).

  • Cowan, N., Fristoe, N. M., Elliott, E. M., Brunner, R. P., & Saults, J. S. (2006). Scope of attention, control of attention, and intelligence in children and adults. Memory & Cognition, 34(8), 1754–1768.

    Article  Google Scholar 

  • Crawford, C. M., & Brown, E. (2002). Focusing upon higher order thinking skills: WebQuests and the learner-centered mathematical learning environment. Critical Thinking, 16.

  • Doleck, T., Bazelais, P., Lemay, D. J., Saxena, A., & Basnet, R. B. (2017). Algorithmic thinking, cooperativity, creativity, critical thinking, and problem solving: Exploring the relationship between computational thinking skills and academic performance. Journal of Computers in Education, 4(4), 355–369. https://doi.org/10.1007/s40692-017-0090-9.

    Article  Google Scholar 

  • Douglas, S. P., & Craig, C. S. (2007). Collaborative and iterative translation: An alternative approach to back translation. Journal of International Marketing, 15(1), 30–43. https://doi.org/10.1509/jimk.15.1.030.

    Article  Google Scholar 

  • Eishani, K. A., Saa’D, E. A., & Nami, Y. (2014). The relationship between learning styles and creativity. Procedia: Social and Behavioral Sciences, 114(1), 52–55.

    Google Scholar 

  • Engle, R. W., Kane, M. J., & Tuholski, S. W. (1999). Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. Models of working memory: Mechanisms of active maintenance and executive control.

    Book  Google Scholar 

  • Fensham, P. J., & Bellocchi, A. (2013). Higher order thinking in chemistry curriculum and its assessment. Thinking Skills and Creativity, 10, 250–264. https://doi.org/10.1016/j.tsc.2013.06.003.

    Article  Google Scholar 

  • González-Marcos, A., Alba-Elías, F., Navaridas-Nalda, F., & Ordieres-Meré, J. (2016). Student evaluation of a virtual experience for project management learning: An empirical study for learning improvement. Computers & Education, 102(C), 172–187.

    Article  Google Scholar 

  • Hanizar, A., Zain, M. Z. M., & Atan, H. (2005). The taxonomical analysis of science educational software in malaysian smart schools. Nosotchu, 22, 487–494.

    Google Scholar 

  • Hopson, M. H., Simms, R. L., & Knezek, G. A. (2001). Using a technology-enriched environment to improve higher-order thinking skills. Journal of Research on Technology in Education, 34(2), 109–119.

    Article  Google Scholar 

  • Huang, R., Hu, Y., Yang, J., & Xiao, G. (2012). The functions of smart classroom in smart learning age. Open Education Research, 18(2), 22–27.

    Google Scholar 

  • Hwang, G.-J., Lai, C.-L., Liang, J.-C., Chu, H.-C., & Tsai, C.-C. (2017). A long-term experiment to investigate the relationships between high school students’ perceptions of mobile learning and peer interaction and higher-order thinking tendencies. Educational Technology Research and Development, 66(1), 75–93. https://doi.org/10.1007/s11423-017-9540-3.

    Article  Google Scholar 

  • Jay, E. (1993). Teaching thinking dispositions: From transmission to enculturation. Theory Into Practice, 32(3), 147–153.

    Article  Google Scholar 

  • Lather, A. S., Jain, S., Anju, M., & Shukla, D. (2014). Student’s creativity in relation to locus of control: A study of Mysore University, India. The International Journal of Indian Psychology, 2(1), 146–165.

    Google Scholar 

  • Law, K. M. Y., Lee, V. C. S., & Yu, Y. T. (2010). Learning motivation in e-learning facilitated computer programming courses. Computers & Education, 55(1), 218–228. https://doi.org/10.1016/j.compedu.2010.01.007.

    Article  Google Scholar 

  • Lee, J., & Choi, H. (2017). What affects learner’s higher-order thinking in technology-enhanced learning environments? The effects of learner factors. Computers & Education, 115, 143–152. https://doi.org/10.1016/j.compedu.2017.06.015.

    Article  Google Scholar 

  • Lewis, A., & Smith, D. (1993). Defining higher order thinking. Theory into Practice, 32(3), 131–137.

    Article  Google Scholar 

  • Li, W., Kwok, L., Wu, S., & Ni, M. (2016). A study of scientific inquiry activities in smart classrooms of a primary school. Blended learning: Aligning theory with practices, ICBL, 2016(9757), 24–36. https://doi.org/10.1007/978-3-319-41165-1_3.

    Article  Google Scholar 

  • Lin, P. Y., Chang, Y. H., Lin, H. T., & Hong, H. Y. (2017). Fostering college students’ creative capacity through computer-supported knowledge building. Journal of Computers in Education, 4(1), 43–56. https://doi.org/10.1007/s40692-016-0063-4.

    Article  Google Scholar 

  • Liu, D., Huang, R., & Wosinski, M. (2017). Smart learning in classroom environment. Smart Learning in Smart Cities. https://doi.org/10.1007/978-981-10-4343-7_5.

    Article  Google Scholar 

  • MacLeod, J., Yang, H. H., Zhu, S., & Li, Y. (2018). Understanding students’ preferences toward the smart classroom learning environment: Development and validation of an instrument. Computers & Education, 122, 80–91. https://doi.org/10.1016/j.compedu.2018.03.015.

    Article  Google Scholar 

  • Manny-Ikan, E., Dagan, O., Tikochinski, T. B., & Zorman, R. (2011). Using the interactive white board in teaching and learning: An evaluation of the SMART CLASSROOM Pilot Project. Interdisciplinary Journal of e-Skills and Lifelong Learning, 7, 191–198.

    Article  Google Scholar 

  • McNaughton, J., Crick, T., Joyce-Gibbons, A., Beauchamp, G., Young, N., & Tan, E. (2017). Facilitating collaborative learning between two primary schools using large multi-touch devices. Journal of Computers in Education, 4(3), 307–320. https://doi.org/10.1007/s40692-017-0081-x.

    Article  Google Scholar 

  • Miltiadou, M., & Savenye, W. C. (2003). Applying social cognitive constructs of motivation to enhance student success in online distance education. Educational Technology Review, 11(1), 78–95.

    Google Scholar 

  • Ministry of Education, China. (2001). Notice of the Ministry of Education on printing and distributing the outline of the basic education curriculum reform (trial). Retrieved from http://old.moe.gov.cn/publicfiles/business/htmlfiles/moe/moe_309/200412/4672.html.

  • Moneta, G. B., & Siu, C. M. Y. (2002). Trait intrinsic and extrinsic motivations, academic performance, and creativity in Hong Kong college students. Journal of College Student Development, 43(5), 664–683.

    Google Scholar 

  • Oca, A. M. M. D., Nistor, N., Dascalu, M., & Trauşan-Matu, S. (2014). Designing smart knowledge building communities. Interaction Design and Architecture(s) Journal, 22, 9–21.

    Google Scholar 

  • Pamuk, S., & Peker, D. (2009). Turkish pre-service science and mathematics teachers’ computer related self-efficacies, attitudes, and the relationship between these variables. Computers & Education, 53(2), 454–461.

    Article  Google Scholar 

  • Pashler, H., Mcdaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest A Journal of the American Psychological Society, 9(3), 105–119.

    Article  Google Scholar 

  • Pelgrum, W. J. (1993). Attitudes of school principals and teachers towards computers: Does it matter what they think? Studies in Educational Evaluation, 19(2), 199–212.

    Article  Google Scholar 

  • Peng, H., Tsai, C. C., & Wu, Y. T. (2006). University students’ self-efficacy and their attitudes toward the Internet: The role of students’ perceptions of the Internet. Educational Studies, 1, 73–86.

    Article  Google Scholar 

  • Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31(6), 459–470.

    Article  Google Scholar 

  • Ramos, J. L. S. D., Villamor, Bretel B., & Brenda, B. (2013). Higher order thinking skills and academic performance in physics of college students: A regression analysis. International Journal of Innovative Interdisciplinary Research, 1(4), 13–20.

    Google Scholar 

  • Ramseier, E. (2001). Motivation to learn as an outcome and determining factor of learning at school. European Journal of Psychology of Education, 16(3), 421–439.

    Article  Google Scholar 

  • Rassool, G. H., & Rawaf, S. (2007). Learning style preferences of undergraduate nursing students. Nursing Standard Official Newspaper of the Royal College of Nursing, 21(21), 35–41.

    Google Scholar 

  • Rivero, B., & Victor. (2010). 21st-century learning in 2010: A global imperative. Multimedia & Internet.

  • Rogers, K. M. A. (2009). A preliminary investigation and analysis of student learning style preferences in further and higher education. Journal of Further & Higher Education, 33(1), 13–21.

    Article  Google Scholar 

  • Ruhnau, K. (2006). An analysis of learning outcomes of adult students: Learning styles versus teaching styles (pp. 1–56). Cambridge: Cambridge University Press.

    Google Scholar 

  • Schoenfeld, A. H. (1999). Looking toward the 21st century: Challenges of educational theory and practice. Educational Researcher, 28(7), 4–14.

    Article  Google Scholar 

  • Schultz, B. (2011). 21st century skills: Rethinking how students learn. School Administrator., 68(5), 44.

    Google Scholar 

  • Shen, C. W., Wu, Y. C. J., & Lee, T. C. (2014). Developing a NFC-equipped smart classroom: Effects on attitudes toward computer science. Computers in Human Behavior, 30(1), 731–738.

    Article  Google Scholar 

  • Silvia, & Paul, (2008). Creativity and intelligence revisited: A reanalysis of Wallach and Kogan (1965). Creativity Research Journal, 20(1), 34–39.

    Article  Google Scholar 

  • Srivastava, J. (2013). Media multitasking performance: Role of message relevance and formatting cues in online environments. Computers in Human Behavior, 29(3), 888–895.

    Article  Google Scholar 

  • Taleb, Z., & Hassanzadeh, F. (2015). Toward smart school: A comparison between smart school and traditional school for mathematics learning. Procedia: Social and Behavioral Sciences, 171, 90–95. https://doi.org/10.1016/j.sbspro.2015.01.093.

    Article  Google Scholar 

  • Tsingos, C., Bosnic-Anticevich, S., & Smith, L. (2015). Learning styles and approaches: Can reflective strategies encourage deep learning? Currents in Pharmacy Teaching & Learning, 7(4), 492–504.

    Article  Google Scholar 

  • Vidergor, H. E. (2018). Effectiveness of the multidimensional curriculum model in developing higher-order thinking skills in elementary and secondary students. Curriculum Journal, 4, 1–21.

    Google Scholar 

  • Voogt, J., & Roblin, N. P. (2012). A comparative analysis of international frameworks for 21st century competences: Implications for national curriculum policies. Journal of Curriculum Studies, 44(3), 299–321.

    Article  Google Scholar 

  • Wu, Y. T., & Tsai, C. C. (2006). University students’ Internet attitudes and Internet self-efficacy: A study at three universities in Taiwan. CyberPsychology & Behavior, 9(4), 441.

    Article  Google Scholar 

  • Grasha, A. F. (1996). Teaching with style a practical guide to enhancing learning by understanding teaching and learning styles. Sci Stke, 2005. Pittsburgh: Alliance.

    Google Scholar 

  • Yücel, A. S., & Koçak, C. (2009). Determination of attitudes of students teachers towards the utilization of technology: Creating a technology tree. Procedia: Social and Behavioral Sciences, 1(1), 2032–2037.

    Google Scholar 

  • Yau, S. S., Gupta, S. K. S., Karim, F., Ahamed, S. I., Wang, Y., & Wang, B. (2003). Smart classroom: Enhancing collaborative learning using pervasive computing technology. In: Proceedings on ASEE annual conference and exposition.

  • Zohar, A., & Dori, Y. J. (2003). Higher order thinking skills and low-achieving students: Are they mutually exclusive? Journal of the Learning Sciences, 12(2), 145–181.

    Article  Google Scholar 

  • Zulfa, A. (2006). Studi Tentang Metode Mengajar Matematika Dalam Kaitannya Dengan Gaya Belajar Siswa.

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Acknowledgements

This work was supported by the general topic of the Education Science Program of the National Social Science Foundation, “Research on the Construction and Application of the Informatization Development Index of Basic Education in China” under grant No. BCA180091.

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Di, W., Danxia, X. & Chun, L. The effects of learner factors on higher-order thinking in the smart classroom environment. J. Comput. Educ. 6, 483–498 (2019). https://doi.org/10.1007/s40692-019-00146-4

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