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Report on Smart Education in Hungary

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Smart Education in China and Central & Eastern European Countries

Part of the book series: Lecture Notes in Educational Technology ((LNET))

Abstract

In the past decade, technology advanced in a far faster pace than the general culture of education, and specifically the everyday classroom practice. Therefore, the potential of smart devices has not been fully exploited for the benefit of students. Technology can contribute to personalizing education by providing recommendations for customized learning paths and experiences (combining learning activities and content) that would be most beneficial for different students based on their learning profile. As such, technologies and technological advances have the potential to make education smarter, provided that they are used to support appropriate educational design. The simple usage of smart devices to access digital resources is not equal with smart education. Technology may have a sustainable impact in education only when technology applications are based on a substantial analysis of the needs of the existing educational practice towards their improvement. In this chapter, we discuss how education can be made smarter by the adequate application of technology-based assessment. As for the implementation of technology-based assessment, we deal with three critical periods of education (1) the kindergarten and the kindergarten-school transition, (2) the first years of the primary school when basic skills determining the success during the entire schooling are founded, and (3) the high school–university transition that determines the quality of studies in higher education. We introduce best practices regarding the smart implementation of technology-based assessment by making learning visible in Hungary.

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  1. 1.

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References

  • Adesope, O. O., & Rud, A. G. (2019). Maximizing the affordances of contemporary technologies in education: Promises and possibilities. In Contemporary technologies in education, pp. 1–15. Palgrave Macmillan, Cham.

    Google Scholar 

  • Ahtola, A., Silinskas, G., Poikonen, P. L., Kontoniemi, M., Niemi, P., & Nurmi, J. E. (2011). Transition to formal schooling: Do transition practices matter for academic performance? Early Childhood Research Quarterly, 26(3), 295–302.

    Article  Google Scholar 

  • Asseburg, R., & Frey, A. (2013). Too hard, too easy, or just right? The relationship between effort or boredom and ability-difficulty fit. Psychological Test and Assessment Modeling, 55(1), 92.

    Google Scholar 

  • Ausubel, D. P. (1968). Educational psychology: A cognitive view. Holt, Rinehart and Winston.

    Google Scholar 

  • Bandura, A. (2002). Social cognitive theory in cultural context. Applied Psychology, 51(2), 269–290.

    Article  Google Scholar 

  • Bond, T. G., & Fox, C. M. (2015). Applying the Rasch model. Fundamental measurement in the human sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.

    Book  Google Scholar 

  • Bowles, T. V., & Brindle, K. A. (2017). Identifying facilitating factors and barriers to improving student retention rates in tertiary teaching courses: A systematic review. Higher Education Research & Development, 36(5), 903–919.

    Article  Google Scholar 

  • Carlton, M. P., & Winsler, A. (1999). School readiness: The need for a paradigm shift. School Psychology Review, 28(3), 338–352.

    Article  Google Scholar 

  • Cook, D. A., & Artino, A. R., Jr. (2016). Motivation to learn: An overview of contemporary theories. Medical Education, 50(10), 997–1014. https://doi.org/10.1111/medu.13074

    Article  Google Scholar 

  • Cowan, N. (2010). The magical mystery four: How is working memory capacity limited, and why? Current Directions in Psychological Science, 19(1), 51–57. https://doi.org/10.1177/0963721409359277

    Article  Google Scholar 

  • Csapó, B., & Szendrei, M. (Eds.). (2011). Framework for diagnostic assessment of mathematics. Budapest: Nemzeti Tankönyvkiadó.

    Google Scholar 

  • Csapó, B., & Csépe, V. (Eds.). (2012). Framework for diagnostic assessment of reading. Budapest: Nemzeti Tankönyvkiadó.

    Google Scholar 

  • Csapó, B., & Szabó, G. (Eds.). (2012). Framework for diagnostic assessment of science. Budapest: Nemzeti Tankönyvkiadó.

    Google Scholar 

  • Csapó, B., & Funke, J. (2017). The development and assessment of problem solving in 21st-century schools. In B. Csapó & J. Funke (Eds.), The nature of problem solving: Using research to inspire 21st century learning (pp. 19–32). OECD.

    Google Scholar 

  • Csapó, B., & Molnár, G. (2017). Potential for assessing dynamic problem-solving at the beginning of higher education studies. Frontiers in Psychology, 8, 2022. https://doi.org/10.3389/fpsyg.2017.02022

    Article  Google Scholar 

  • Csapó, B., & Molnár, G. (2019). Online diagnostic assessment in support of personalized teaching and learning: The eDia System. Frontiers in Psychology, 10, 1522.

    Article  Google Scholar 

  • Csapó, B., Molnár, G., & Nagy, J. (2014). Computer-based assessment of school readiness and early reasoning. Journal of Educational Psychology, 106(3), 639.

    Article  Google Scholar 

  • Csapó, B. (2010). Goals of learning and the organization of knowledge. Zeitschrift für Pädagogik, 56, pp. 12–27.

    Google Scholar 

  • Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience (Vol. 1990). Harper & Row.

    Google Scholar 

  • Csikszentmihalyi, M. (2000). Beyond boredom and anxiety. Jossey-Bass.

    Google Scholar 

  • Daniels, L. M., Goegan, L. D., & Parker, P. C. (2021). The impact of COVID-19 triggered changes to instruction and assessment on university students’ self-reported motivation, engagement and perceptions. Social Psychology of Education, 24, 299–318. https://doi.org/10.1007/s11218-021-09612-3

    Article  Google Scholar 

  • Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for educational practice of the science of learning and development. Applied Developmental Science, 24(2), 97–140. https://doi.org/10.1080/10888691.2018.1537791

    Article  Google Scholar 

  • De Laet, T., Broos, T., Van Staalduinen, J. P., Leitner, P., & Ebner, M. (2016). Successful transition from secondary to higher education using learning analytics. In SEFI Conference, 12–15 September 2016, Tampere, Finland.

    Google Scholar 

  • Dede, C. (2013). Opportunities and challenges in embedding diagnostic assessments into immersive interfaces. Educational Designer, 2(6), 1–22.

    Google Scholar 

  • DJP. (2016). Digital success programme. In Digital education strategy of Hungary. Retrieved from https://digitalisjoletprogram.hu/files/d4/6b/d46bf17fdef3c9b5c1d38bd6db64c2a7.pdf

  • Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., Pagani, L. S., Feinstein, L., Engel, M., Brooks-Gunn, J., Sexton, H., Duckworth, K., & Japel, C. (2007). School readiness and later achievement. Developmental Psychology, 43(6), 428–446.

    Article  Google Scholar 

  • Ferrão, M. E., Costa, P. M., & Oliveira, P. N. (2015). Generalized partial credit item response model: Linking scales in the assessment of learning. Journal of Interdisciplinary Mathematics, 18(4), 339–354. https://doi.org/10.1080/09720502.2014.932119

    Article  Google Scholar 

  • Francis, J. (2017). The effects of technology on student motivation and engagement in classroom-based learning. Retrieved from https://dune.une.edu/theses/121/

  • Frey, A., & Seitz, N. N. (2009). Multidimensional adaptive testing in educational and psychological measurement: Current state and future challenges. Studies in Educational Evaluation, 35(2–3), 89–94.

    Article  Google Scholar 

  • Harackiewicz, J. M., & Knogler, M. (2017). Interest: Theory and application. In A. J. Elliot, C. S. Dweck, & D. S. Yeager (Eds.), Handbook of competence and motivation: Theory and application (pp. 334–352). The Guilford Press.

    Google Scholar 

  • Hattie, J. (2009). Visible learning: A synthesis of 800 meta-analyses relating to achievement. Routledge.

    Google Scholar 

  • Hattie, J. (2012). Visible learning for teachers: Maximizing impact on learning. Routledge.

    Book  Google Scholar 

  • Hattie, J., & Anderman, E. M. (2013). Introduction. In J. Hattie, & E. M. Anderman (Eds.), International guide to student achievement, pp. xix–xxii. New York: Routledge.

    Google Scholar 

  • Jiang, H., Justice, L., Purtell, K. M., Lin, T. J., & Logan, J. (2021). Prevalence and prediction of kindergarten-transition difficulties. Early Childhood Research Quarterly, 55, 15–23.

    Article  Google Scholar 

  • Kärner, T., Warwas, J., & Schumann, S. (2021a). A learning analytics approach to address heterogeneity in the classroom: The teachers’ diagnostic support system. Technology, Knowledge and Learning, 26, 31–52.

    Article  Google Scholar 

  • Kärner, T., Warwas, J., Krannich, M., & Weichsler, N. (2021b). How does information consistency influence prospective teachers’ decisions about task difficulty assignments? A within-subject experiment to explain data-based decision-making in heterogeneous classes. Learning and Instruction, 74, 101440.

    Article  Google Scholar 

  • Kárpáti, A. (2019). ICT policy development: A comparative analyses through the education transformation policy guide. Retrieved from http://publikacio.uni-eszterhazy.hu/6070/1/90_113_K%C3%A1rp%C3%A1ti.pdf

  • King, J. & South, J. (2017). Reimagining the role of technology in higher education: A supplement to the national education technology plan. US Department of Education, Office of Educational Technology.

    Google Scholar 

  • Kramer, S., & Benson, S. (2013). Changing faculty use of technology–one cohort at a time. Journal of Applied Research in Higher Education.

    Google Scholar 

  • Mayer, R. E. (2014). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 43–71). Cambridge University Press.

    Chapter  Google Scholar 

  • Mayer, R. E. (2019). Foreword: Maximizing the effectivensess of learning with media. In O. O. Adesope, & A. G. Rud (Eds.), Contemporary Technologies in education: Maximizing student engagement, motivation, and learning, pp. IX–X. Springer.

    Google Scholar 

  • McKenzie, K., & Schweitzer, R. (2001). Who succeeded at university? Factors predicting academic performance in first year Australian university students. Higher Education Research and Development, 20(1), 21–33.

    Article  Google Scholar 

  • Merrell, C., & Tymms, P. (2010). Changes in children’s cognitive development at the start of school in England 2001–2008. Oxford Review of Education, 37(3), 333–345. https://doi.org/10.1080/03054985.2010.527731

    Article  Google Scholar 

  • Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97.

    Article  Google Scholar 

  • Molnár G. (2011). Az információs-kommunikációs technológiák hatása a tanulásra és oktatásra. [The effect of ICT on learning and teaching]. Magyar Tudomány, 172(9), 1038–1047.

    Google Scholar 

  • Molnár, G. & Csapó, B. (2003). A képességek fejlődésének logisztikus modellje. [Logistic model of development of competencies]. Iskolakultúra, No. 2, pp. 57–69.

    Google Scholar 

  • Molnár, G. (2013). A Rasch modell alkalmazási lehetőségei az empirikus kutatások gyakorlatában. Budapest: Gondolat Kiadó.

    Google Scholar 

  • Molnár, G., & Csapó, B. (2019a). Making the psychological dimension of learning visible: Using technology-based assessment to monitor students’ cognitive development. Frontiers in Psychology, 10, 1368.

    Article  Google Scholar 

  • Molnár, G. & Csapó, B. (2019b). Technology-based diagnostic assessments for identifying early learning difficulties in mathematics. In A. Fritz-Stratmann, P. Räsänen and V. Haase (Eds.), International handbook of mathematical learning difficulties (pp. 683–707). Heidelberg: Springer.

    Google Scholar 

  • Molnár G., & Csapó B. (2019c). A felsőoktatási tanulmányi alkalmasság értékelésére kidolgozott rendszer a Szegedi Tudományegyetemen: elméleti keretek és mérési eredmények. [The system developed for the assessment of preparedness for higher educational studies at the University of Szeged: theoretical frameworks and measurement results]. Educatio, 28(4), 705–717.

    Google Scholar 

  • Molnár, G., & Csapó, B. (2020). Separating the disciplinary, application and reasoning dimensions of learning: The power of technology-based assessment. In H. Lane, S. Zvacek, & J. Uhomoibhi (Eds.), Computer Supported Education (pp. 174–190). Springer.

    Chapter  Google Scholar 

  • Molnár, G., Molnár, E. K., Dancs, K., & Csapó, B. (2020). Report on the development of educational informationalization and basic education-Hungary. In D. Liu, R. Huang, B. Lalic, H. Zeng, & N. Zivlak (Eds.), Comparative analysis of ICT in education between China and Central and Eastern European countries. Lecture Notes in Educational Technology, pp. 173–187. Singapore: Springer.

    Google Scholar 

  • Molnar, G. (2021). Challenges and developments in technology-based assessment: Possibilities in science education. Europhysics, 52(2), 16–19.

    Article  Google Scholar 

  • Molnár, G. (2021). Az IKT szerepe a felsőoktatás megújításában. [Role of ICT in Renewing Higher Education]. Magyar Tudomány, 182(11).

    Google Scholar 

  • Molnár, G., Hódi, Á., Molnár, E. D., Nagy, Z., & Csapó, B. (2021a). Assessment of first-year university students: Facilitation an effective transition into higher education. In Á. Engler & V. Bocsi (Eds.), Új kutatások a neveléstudományokban 2020 (pp. 11–26). Debrecen.

    Google Scholar 

  • Molnár, G., Hódi, Á., Ökördi, R., & Mokri, D. (2021b). A koronavírus-járvány okozta rendkívüli oktatási helyzet hatása 2–8. évfolyamos diákok tudás-és képességszintjére az olvasás-szövegértés, a matematika és a természettudományok területén. [The impact of digital education introduced due to corona virus on 2–8 graders’ reading, mathematics and science knowledge and skills]. Iskolakultúra, 31(2), 3–22.

    Google Scholar 

  • Molnár, G., Pásztor, A., Kiss, R., & Csapó, B. (2021c). Az eDia online diagnosztikus értékelő rendszer: A személyre szóló fejlesztés alapvető eszköze [The eDia online diagnostic assessment system: An essential tool for personal development]. Új Pedagógiai Szemle, 71(09–10), 42–53.

    Google Scholar 

  • Molnár, G., Alrababah, S. A., & Greiff, S. (2022). How we explore, interpret, and solve complex problems: A cross-national study of problem-solving processes. Heliyon, p. e08775.

    Google Scholar 

  • Mullis, I. V., & Martin, M. O. (2017). TIMSS 2019 assessment frameworks. International Association for the Evaluation of Educational Achievement.

    Google Scholar 

  • Nagy, J. (1980). 5–6 éves gyermekeink iskolakészültsége [School readiness among 5-to 6-year-old children]. Budapest, Hungary: Akadémiai Kiadó.

    Google Scholar 

  • OECD. (2014). PISA 2012 results: Creative problem solving: Students’ skills in tackling real-life problems (Vol. V). Paris: OECD Publishing.

    Google Scholar 

  • OECD. (2019). PISA 2018 Results (Volume I): What students know and can do. Paris: OECD Publishing. https://doi.org/10.1787/5f07c754-en

    Book  Google Scholar 

  • Pellegrino, J. W., Chudowsky, N., & Glaser, R. (2001). Knowing what students know: The science and design of educational assessment. National Academy Press, 2102 Constitutions Avenue, NW, Lockbox 285, Washington, DC 20055.

    Google Scholar 

  • Public Education Strategy of Hungary. (2020). Public education strategy of Hungary for 2021–2030. Retrieved from https://2015-2019.kormany.hu/download/d/2e/d1000/K%C3%B6znevel%C3%A9si%20strat%C3%A9gia.pdf; outline in English: https://eacea.ec.europa.eu/national-policies/eurydice/content/ongoing-reforms-and-policy-developments-29_en

  • Puspitarini, Y. D., & Hanif, M. (2019). Using learning media to increase learning motivation in elementary school. Anatolian Journal of Education, 4(2), 53–60.

    Article  Google Scholar 

  • Rasch, G. (1966). An individualistic approach to item analysis. In P. F. Lazarsfeld & N. W. Henry (Eds.), Readings in mathematical social science (pp. 89–108). Science Research Associates.

    Google Scholar 

  • Ricciardi, C., Manfra, L., Hartman, S., Bleiker, C., Dineheart, L., & Winsler, A. (2021). School readiness skills at age four predict academic achievement through 5th grade. Early Childhood Research Quarterly, 57, 110–120.

    Article  Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2000a). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67.

    Article  Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2000b). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68.

    Article  Google Scholar 

  • Schraw, G., Mayrath, M. C., Clarke Midura, J., & Robinson, D. H. (Eds.). (2012). Technology based assessments for 21st Century skills: Theoretical and practical implications from modern research. IAP.

    Google Scholar 

  • Schunk, D. H. (2012). Learning theories, an educational perspective (6th ed.). Pearson Education Inc.

    Google Scholar 

  • Schunk, D. H., & DiBenedetto, M. K. (2021). Self-efficacy and human motivation. Advances in Motivation Science, 8, 153–179.

    Article  Google Scholar 

  • Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312.

    Article  Google Scholar 

  • Telyani, A. E., Farmanesh, P., & Zargar, P. (2021). The impact of COVID-19 instigated changes on loneliness of teachers and motivation–engagement of students: A psychological analysis of education sector. Frontiers in Psychology, p. 4353.

    Google Scholar 

  • Tymms, P., Jones, P., Albone, S., & Henderson, B. (2009). The first seven years at school. Educational Assessment, Evaluation and Accountability, 21(1), 67–80. https://doi.org/10.1007/s11092-008-9066-7

    Article  Google Scholar 

  • Van Rooij, E. C., Jansen, E. P., & Van De Grift, W. J. (2018). First-year university students’ academic success: The importance of academic adjustment. European Journal of Psychology of Education, 33(4), 749–767.

    Article  Google Scholar 

  • Vygotsky, L. S. (1987). The collected works of LS Vygotsky: Thinking and speaking (Vol. 1). Springer Science & Business Media.

    Google Scholar 

  • Wang, W., Song, S., Chen, X., & Yuan, W. (2021). When learning goal orientation leads to learning from failure: The roles of negative emotion coping orientation and positive grieving. Frontiers in Psychology, 12, 1458.

    Google Scholar 

  • Weeks, J. P. (2018). An application of multidimensional vertical scaling. Measurement: Interdisciplinary Research and Perspectives, 16(3), 139–154. https://doi.org/10.1080/15366367.2018.1502005

    Article  Google Scholar 

  • Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. https://doi.org/10.1006/ceps.1999.1015

    Article  Google Scholar 

  • Wilby, K. J., & Paravattil, B. (2021). Cognitive load theory: Implications for assessment in pharmacy education. Research in Social and Administrative Pharmacy, 17(9), 1645–1649.

    Article  Google Scholar 

  • Wu, H., & Molnár, G. (2021). Logfile analyses of successful and unsuccessful strategy use in complex problem-solving: A cross-national comparison study. European Journal of Psychology of Education, 36(4), 1009–1032.

    Article  Google Scholar 

  • Yousef, A. M. F. (2021). Augmented reality assisted learning achievement, motivation, and creativity for children of low-grade in primary school. Journal of Computer Assisted Learning.

    Google Scholar 

  • Zhu, Z. T., Yu, M. H., & Riezebos, P. (2016). A research framework of smart education. Smart Learning Environment, 3(4). https://doi.org/10.1186/s40561-016-0026-2

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Acknowledgements

This research was supported by grants from the National Research, Development and Innovation Fund of Hungary (under the OTKA K135727 funding scheme) and the Hungarian Academy of Sciences (Research Programme for Public Education Development of the Hungarian Academy of Sciences grant KOZOKT2021-16).

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Authors and Affiliations

  1. Institute of Education, University of Szeged, MTA–SZTE Digital Learning Technologies Research Group, Szeged, Hungary

    Gyöngyvér Molnár & Benő Csapó

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  1. Gyöngyvér Molnár
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Correspondence to Gyöngyvér Molnár .

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Editors and Affiliations

  1. Smart Learning Institute, Beijing Normal University, Beijing, China

    Rongxia Zhuang

  2. Beijing Normal University, Beijing, China

    Dejian Liu

  3. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    Demetrios Sampson

  4. Teacher Education Faculty, University of Belgrade, Belgrade, Serbia

    Danimir Mandic

  5. Global Public Sector, Enterprise Business Group, Huawei, China

    Siyi Zou

  6. Global Public Sector, Enterprise Business Group, Huawei, China

    Yu Huang

  7. Beijing Normal University, Beijing, China

    Ronghuai Huang

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Molnár, G., Csapó, B. (2023). Report on Smart Education in Hungary. In: Zhuang, R., et al. Smart Education in China and Central & Eastern European Countries. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-7319-2_7

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