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How Behavioral and Psychological Factors Influence STEM Performance in K-12 Schools: A Mediation Model

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Abstract

Understanding factors that influence k-12 students’ Science, Technology, Engineering, and Mathematics (STEM) performance is essential to improving their problem-solving ability. Most studies have focused on the relationship between students’ psychological factors and STEM performance and have paid little attention to the relationship between behavioral factors and STEM performance. This study explored the impact of behavioral factors (i.e., information and communications technology (ICT) readiness and online interaction (OI)) and psychological factors (i.e., internet self-efficacy (ISE)) on k-12 students’ STEM performance. The sample included 851 fifth graders and 535 eighth graders from cities in central China. The results of structural equation modeling analysis showed that ISE and ICT readiness (IR) significantly impacted the STEM performance of eighth graders. More importantly, ISE, a psychological factor, had the greatest effect on STEM performance and played a mediating role in the relationship between IR, OI, and STEM performance. These findings have important implications for STEM teachers. To improve students’ STEM performance, teachers should intervene to improve ISE according to students’ grades and cognitive ability, guide students to use ICT correctly, and encourage them to actively engage in OI.

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Availability of Data and Materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

References

  • Aesaert, K., & van Braak, J. (2014). Exploring factors related to primary school pupils’ ICT self-efficacy: A multilevel approach. Computers in Human Behavior, 41, 327–341. https://doi.org/10.1016/j.chb.2014.10.006

    Article  Google Scholar 

  • Allen, P. J., Chang, R., Gorrall, B. K., Waggenspack, L., Fukuda, E., Little, T. D., & Noam, G. G. (2019). From quality to outcomes: A national study of after school STEM programming. International Journal of STEM Education, 6(1), 37. https://doi.org/10.1186/s40594-019-0191-2

    Article  Google Scholar 

  • Amriza, R., Saintika, Y., & Firmansyah, A. (2022). The Investigation of Student Engagement as Mediator in Ict Readiness and Experience on e- Learning Effectiveness in Post-Pandemic Recovery. https://doi.org/10.1109/ICISS55894.2022.9915172

    Article  Google Scholar 

  • Areepattamannil, S., & Khine, M. S. (2017). Early adolescents’ use of information and communication technologies (ICTs) for social communication in 20 countries: Examining the roles of ICT-related behavioral and motivational characteristics. Computers in Human Behavior, 73, 263–272. https://doi.org/10.1016/j.chb.2017.03.058

    Article  Google Scholar 

  • Bandura, A. (1978). The self system in reciprocal determinism. American Psychologist, 33, 344–358.

    Article  Google Scholar 

  • Bandura, A., Freeman, W. H., & Lightsey, R. (1997). Self-efficacy: The exercise of control. Journal of Cognitive Psychotherapy, 13(2), 158–166. https://doi.org/10.1891/0889-8391.13.2.158

    Article  Google Scholar 

  • Barlow, A., & Brown, S. (2020). Correlations between modes of student cognitive engagement and instructional practices in undergraduate STEM courses. International Journal of STEM Education, 7(1), 18. https://doi.org/10.1186/s40594-020-00214-7

    Article  Google Scholar 

  • Bati, K., Yetişir, M. I., Çalişkan, I., Güneş, G., & Gül Saçan, E. (2018). Teaching the concept of time: A steam-based program on computational thinking in science education. Cogent Education, 5(1), 1507306. https://doi.org/10.1080/2331186X.2018.1507306

    Article  Google Scholar 

  • Batoya, I. B., Wabwoba, F., & Kilwake, J. (2015). Influence of social technical factors on ICT readiness for primary schools in Bungoma County, Kenya. Unified Journal of Computer Science Research, 1(1), 1–7.

    Google Scholar 

  • Belbase, S., Mainali, B. R., Kasemsukpipat, W., Tairab, H., Gochoo, M., & Jarrah, A. (2021). At the dawn of science, technology, engineering, arts, and mathematics (STEAM) education: prospects, priorities, processes, and problems. International Journal of Mathematical Education in Science and Technology, 1–37. https://doi.org/10.1080/0020739x.2021.1922943

  • Byrne, B. M. (2011). Structural equation modeling with Mplus: Basic concepts, applications, and programming. Psicothema, 24(2), 343–344.

    Google Scholar 

  • Castano-Munoz, J., Sancho-Vinuesa, T., & Duart, J. M. (2013). Online interaction in higher education: Is there evidence of diminishing returns? International Review of Research in Open and Distributed Learning, 14(5), 240–257.

    Article  Google Scholar 

  • Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. https://doi.org/10.1207/S15328007SEM0902_5

    Article  Google Scholar 

  • Chiu, Y.-L., Liang, J.-C., Mao, P.C.-M., & Tsai, C.-C. (2016). Improving health care providers’ capacity for self-regulated learning in online continuing pharmacy education: The role of internet self-efficacy. Journal of Continuing Education in the Health Professions, 36(2), 89–95. https://doi.org/10.1097/ceh.0000000000000066

    Article  Google Scholar 

  • Chuang, S.-C., Lin, F.-M., & Tsai, C.-C. (2015). An exploration of the relationship between internet self-efficacy and sources of internet self-efficacy among Taiwanese university students. Computers in Human Behavior, 48, 147–155. https://doi.org/10.1016/j.chb.2015.01.044

    Article  Google Scholar 

  • Copriady, J. (2015). Self-motivation as a mediator for teachers’ readiness in applying ICT in teaching and learning. Procedia - Social and Behavioral Sciences, 176(4), 699–708.

    Article  Google Scholar 

  • DeTure, M. (2004). Cognitive style and self-Efficacy: Predicting student success in online distance education. American Journal of Distance Education, 18(1), 21–38. https://doi.org/10.1207/s15389286ajde1801_3

    Article  Google Scholar 

  • Eickelmann, B., Drossel, K., Wendt, H., & Bos, W. (2012). ICT-use in primary schools and childrens’ mathematics achievement; A multi-level approach to compare educational systems through an international lens with TIMSS data.

  • Fernández-Gutiérrez, M., Gimenez, G., & Calero, J. (2020). Is the use of ICT in education leading to higher student outcomes? Analysis from the Spanish Autonomous Communities. Computers & Education, 157, 103969–103984. https://doi.org/10.1016/j.compedu.2020.103969

    Article  Google Scholar 

  • Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059

    Article  Google Scholar 

  • Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410. https://doi.org/10.1073/pnas.1319030111

    Article  Google Scholar 

  • Gombachika, H. S. H., & Khangamwa, G. (2013). ICT readiness and acceptance among TEVT students in University of Malawi. Campus-Wide Information Systems, 30(1), 35–43. https://doi.org/10.1108/10650741311288805

    Article  Google Scholar 

  • Griaznova, E. D. (2014). The development of ICT-competencies of the students at foreign language lessons as the basis for successful learning and cognitive activity. Pacific Science Review, 1(2), 155–162.

    Google Scholar 

  • Gubbels, J., Swart, N. M., & Groen, M. A. (2020). Everything in moderation: ICT and reading performance of Dutch 15-year-olds. Large-Scale Assessments in Education, 8(1), 2–17. https://doi.org/10.1186/s40536-020-0079-0

    Article  Google Scholar 

  • Han, J., Kelley, T., & Knowles, J. G. (2021). Factors influencing student STEM learning: Self-efficacy and outcome expectancy, 21st century skills, and career awareness. Journal for STEM Education Research, 4(2), 117–137. https://doi.org/10.1007/s41979-021-00053-3

    Article  Google Scholar 

  • Honey, M., Pearson, G., & Schweingruber, H. (2014). Stem integration in k-12 education: Status, prospects, and an agenda for research. National Academies Press.

    Google Scholar 

  • Hu, X., Gong, Y., Lai, C., & Leung, F. K. S. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1–13. https://doi.org/10.1016/j.compedu.2018.05.021

    Article  Google Scholar 

  • Jeon, M., Draney, K., Wilson, M., & Sun, Y. (2020). Investigation of adolescents’ developmental stages in deductive reasoning: An application of a specialized confirmatory mixture IRT approach. Behavior Research Methods, 52(1), 224–235. https://doi.org/10.3758/s13428-019-01221-5

    Article  Google Scholar 

  • Jia, Y., Zhou, B., & Zheng, X. (2021). A curriculum integrating STEAM and maker education promotes pupils’ learning motivation, self-efficacy, and interdisciplinary knowledge acquisition. Frontiers in Psychology, 12, 1–10. https://doi.org/10.3389/fpsyg.2021.725525

    Article  Google Scholar 

  • Jiang, H., Tang, M., Peng, X., & Liu, X. (2018). Learning design and technology through social networks for high school students in China. International Journal of Technology and Design Education, 1, 189–206.

    Article  Google Scholar 

  • Kazi, M., & Samara, K. (2015). Student readiness for ICT learning: a case study investigation in a large multi-national ICT organization. In 21st Century Academic Forum Conference Proceedings IC21CE 2014, 45–51.

  • Koh, J. H. L., Chai, C. S., Benjamin, W., & Hong, H.-Y. (2015). Technological pedagogical content knowledge (TPACK) and design thinking: A framework to support ICT lesson design for 21st century learning. The Asia-Pacific Education Researcher, 24(3), 535–543. https://doi.org/10.1007/s40299-015-0237-2

    Article  Google Scholar 

  • Kuo, Y.-C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35–50. https://doi.org/10.1016/j.iheduc.2013.10.001

    Article  Google Scholar 

  • Lu, C., Yang, X., & Wu, D. (2018). ICT competency, network interaction, internet self-efficacy, and mathematical achievement: Direct and mediating effects. 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), 534–539. https://doi.org/10.1109/tale.2018.8615317

  • Luu, K., & Freeman, J. G. (2011). An analysis of the relationship between information and communication technology (ICT) and scientific literacy in Canada and Australia. Computers & Education, 56(4), 1072–1082. https://doi.org/10.1016/j.compedu.2010.11.008

    Article  Google Scholar 

  • Ma, Y., & Qin, X. (2021). Measurement invariance of information, communication and technology (ICT) engagement and its relationship with student academic literacy: Evidence from PISA 2018. Studies in Educational Evaluation, 68, 100982–100997. https://doi.org/10.1016/j.stueduc.2021.100982

    Article  Google Scholar 

  • Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling A Multidisciplinary Journal, 11(3), 320–341.

    Article  Google Scholar 

  • Marsh, H. W., Pekrun, R., Parker, P. D., Kou, M., & Lichtenfeld, S. (2017). Long-term positive effects of repeating a year in school: Six-year longitudinal study of self-beliefs, anxiety, social relations, school grades, and test scores. Journal of Educational Psychology, 109(3), 425–438.

    Article  Google Scholar 

  • Nadelson, L. S., & Seifert, A. L. (2017). Integrated STEM defined: Contexts, challenges, and the future. The Journal of Educational Research, 110(3), 221–223. https://doi.org/10.1080/00220671.2017.1289775

    Article  Google Scholar 

  • Ndiku, J. M., & Kaluyu, V. (2020). Learner perspective of pedagogy for improved performance in stem subjects: a literature review. IOSR Journal of Research & Method in Education, 10(4), 15–27. Retrieved March 6, 2023, from https://iosrjournals.org/iosr-jrme/papers/Vol-10%20Issue-4/Series-3/C1004031527.pdf

  • Papastergiou, M. (2010). The role of computer self-efficacy, self-esteem, and subjective well-being in predicting research self-efficacy among postgraduate students. The Asia-Pacific Education Researcher, 1(2), 399–406.

    Google Scholar 

  • Parasuraman, A. (2000). Technology readiness index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320.

    Article  Google Scholar 

  • Pelch, M. (2018). Gendered differences in academic emotions and their implications for student success in STEM. International Journal of STEM Education, 5(1), 33. https://doi.org/10.1186/s40594-018-0130-7

    Article  Google Scholar 

  • Piaget, J. (1980). The child’s conception of space. Acta Psychologica, 19(2), 164–165. https://doi.org/10.1016/S0001-6918(61)80057-7

    Article  Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903. https://doi.org/10.1037/0021-9010.88.5.879

    Article  Google Scholar 

  • Reddy, E., Sharma, B., Reddy, P., & Dakuidreketi, M. (2017). Mobile learning readiness and ICT competency: a case study of senior secondary school students in the Pacific Islands. 2017 4th Asia-Pacific World Congress on Computer Science and Engineering, 137–143.

  • Schina, D., Valls-Bautista, C., Borrull-Riera, A., Usart, M., & Esteve-Gonzalez, V. (2021). An associational study: Preschool teachers’ acceptance and self-efficacy towards educational robotics in a pre-service teacher training program. International Journal of Educational Technology in Higher Education, 18(1), 28. https://doi.org/10.1186/s41239-021-00264-z

    Article  Google Scholar 

  • Shiau, W., & Luo, M. M. (2012). Factors affecting online group buying intention and satisfaction: A social exchange theory perspective. Computers in Human Behavior, 28(6), 2431–2444.

    Article  Google Scholar 

  • Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The challenges of defining and measuring student engagement in science. Educational Psychologist, 50(1), 1–13. https://doi.org/10.1080/00461520.2014.1002924

    Article  Google Scholar 

  • Skryabin, M., Zhang, J., Liu, L., & Zhang, D. (2015). How the ICT development level and usage influence student achievement in reading, mathematics, and science. Computers & Education, 85, 49–58. https://doi.org/10.1016/j.compedu.2015.02.004

    Article  Google Scholar 

  • Stehle, S. M., & Peters-Burton, E. E. (2019). Developing student 21st century skills in selected exemplary inclusive STEM high schools. International Journal of STEM Education, 6(1), 39. https://doi.org/10.1186/s40594-019-0192-1

    Article  Google Scholar 

  • Su, Y.-S., Chang, C.-Y., Wang, C.-H., & Lai, C.-F. (2022). A study of students’ learning perceptions and behaviors in remote STEM programming education. Frontiers in Psychology, 13, 1–10. https://doi.org/10.3389/fpsyg.2022.962984

    Article  Google Scholar 

  • Sun, Z., Liu, R., Luo, L., Wu, M., & Shi, C. (2017). Exploring collaborative learning effect in blended learning environments. Journal of Computer Assisted Learning, 6, 575–587.

    Google Scholar 

  • Tam, H., Chan, A. Y. F., & Lai, O. L. H. (2020). Gender stereotyping and STEM education: girls’ empowerment through effective ICT training in Hong Kong. Children and Youth Services Review, 119, 105624. https://doi.org/10.1016/j.childyouth.2020.105624

    Article  Google Scholar 

  • Tawfik, A. A., Giabbanelli, P. J., Hogan, M., Msilu, F., Gill, A., & York, C. S. (2018). Effects of success v failure cases on learner-learner interaction. Computers & Education, 118, 120–132.

    Article  Google Scholar 

  • Thompson, L. F., Meriac, P. J., & Cope, G. J. (2002). Motivating online performance: The influences of goal setting and internet self-efficacy. Social Science Computer Review, 20(2), 149–160.

    Article  Google Scholar 

  • Tsai, C.-C., Chuang, S.-C., Liang, J.-C., & Tsai, M.-J. (2011). Self-efficacy in internet-based learning environments: A literature review. Educational Technology & Society, 14(4), 222–240.

    Google Scholar 

  • Wang, A. Y., & Newlin, M. H. (2002). Predictors of web-student performance: The role of self-efficacy and reasons for taking an on-line class. Computers in Human Behavior, 18(2), 151–163. https://doi.org/10.1016/S0747-5632(01)00042-5

    Article  Google Scholar 

  • Whittaker, T. A. (2011). A beginner’s guide to structural equation modeling. Structural Equation Modeling A Multidisciplinary Journal.

  • Xu, Z., & Jang, E. (2017). The role of math self-efficacy in the structural model of extracurricular technology-related activities and junior elementary school students’ mathematics ability. Computers in Human Behavior, 68, 547–555.

    Article  Google Scholar 

  • Zheng, B., & Warschauer, M. (2015). Participation, interaction, and academic achievement in an online discussion environment. Computers & Education, 84, 78–89. https://doi.org/10.1016/j.compedu.2015.01.008

    Article  Google Scholar 

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Acknowledgements

This study was supported by the National Social Science Fund of China project “Research on evaluation strategy of high-quality balanced ICT development of compulsory education in urban and rural areas”(Grant Number BCA210090).

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Authors and Affiliations

  1. Educational Informatization Strategy Research Base of Ministry of Education, Central China Normal University, Wuhan, Hubei, China

    Chun Lu

  2. National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, China

    Wei Yang & Xiao Yang

  3. Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China

    Longkai Wu

  4. Science Hall, Central China Normal University, 152 Luoyu Road, Wuhan, 430079, Hubei, China

    Chun Lu, Wei Yang, Longkai Wu & Xiao Yang

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  1. Chun Lu
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Contributions

All authors are major contributors to this study. CL conceived of the study and designed the survey. WY conducted manuscript writing. LKW has substantially revised and polished the manuscript. XY conducted data analysis, manuscript writing and reviewer comments revision. All authors read and approved the final manuscript.

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Correspondence to Xiao Yang.

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Lu, C., Yang, W., Wu, L. et al. How Behavioral and Psychological Factors Influence STEM Performance in K-12 Schools: A Mediation Model. J Sci Educ Technol 32, 379–389 (2023). https://doi.org/10.1007/s10956-023-10034-3

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