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Journal of Science Education and Technology

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29-11-2022

The Go-Lab Platform, an Inquiry-learning Space: Investigation into Students’ Technology Acceptance, Knowledge Integration, and Learning Outcomes

Authors: Chi-Jung Sui, Hsin-Chueh Chen, Ping-Han Cheng, Chun-Yen Chang

Published in: Journal of Science Education and Technology | Issue 1/2023

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Abstract

Utilizing an inquiry-learning space (ILS) via the Go-Lab platform, we investigated students’ technology acceptance, knowledge integration [KI] process, and learning outcomes of both the high-achiever KI student and low-achiever KI student. This study aimed to understand how students engage in knowledge integration tasks using an inquiry-learning space to learn Mendelian genetics and realize the relationship between student KI and domain knowledge. Using a quasi-experimental design, we examined 41 seventh-grade students in Taiwan completing Mendelian genetics KI tasks in ILS, pre/post-testing of domain knowledge tests, and a technology acceptance questionnaire. The analysis of students’ interaction in the ILS and domain knowledge tests was conducted through descriptive statistics, t-tests, Pearson’s correlation, and content analysis to indicate the direction and relationship between students’ KI processes and learning outcomes. A technology acceptance questionnaire was analyzed through descriptive statistics and Pearson’s correlation to reveal whether students accepted learning on the Go-Lab platform. The study showed that (i) students responded positively to the perceived usefulness of the Go-Lab platform, (ii) the high-achiever KI students could gradually construct links from simple to complex and diverse among genetic ideas, and the low-achiever almost built simple links, but (iii) the high-achiever and low-achiever KI students had similar learning outcomes. These findings have implications that the instruction design of knowledge integration tasks promotes students’ Mendelian genetics conceptual understanding and KI progress.

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Abd-El-Khalick, F., & Lederman, N. G. (2000). Improving science teachers’ conceptions of nature of science: A critical review of the literature. International Journal of Science Education, 22(7), 665–701. https://doi.org/10.1080/09500690050044044CrossRef

Ageitos, N., Puig, B., & Colucci-Gray, L. (2019). Examining reasoning practices and epistemic actions to explore students’ understanding of genetics and evolution. Science & Education, 28(9), 1209–1233. https://doi.org/10.1007/s11191-019-00086-6CrossRef

Ali, F., Nair, P. K., & Hussain, K. (2016). An assessment of students’ acceptance and usage of computer supported collaborative classrooms in hospitality and tourism schools. Journal of Hospitality, Leisure, Sport & Tourism Education, 18, 51–60. https://doi.org/10.1016/j.jhlste.2016.03.002CrossRef

Allen, R. D. (1987). Student difficulties with Mendelian genetics problems. The American Biology Teacher, 49(4), 229–233. https://doi.org/10.2307/4448497CrossRef

Alqahtani, M., & Mohammad, H. (2015). Mobile applications’ impact on student performance and satisfaction. Turkish Online Journal of Educational Technology-TOJET, 14(4), 102–112.

American Association for the Advancement of Science. (1993). Benchmarks for science literacy. In. New York: Oxford University Press.

Bell, P., & Linn, M. C. (2000). Scientific arguments as learning artifacts: Designing for learning from the web with KIE. International Journal of Science Education, 22(8), 797–817. https://doi.org/10.1080/095006900412284CrossRef

Brinson, J. R. (2015). Learning outcome achievement in non-traditional (virtual and remote) versus traditional (hands-on) laboratories: A review of the empirical research. Computers & Education, 87, 218–237. https://doi.org/10.1016/j.compedu.2015.07.003CrossRef

Browning, M. E., & Lehman, J. D. (1988). Identification of student misconceptions in genetics problem solving via computer program. Journal of Research in Science Teaching, 25(9), 747–761. https://doi.org/10.1002/tea.3660280410CrossRef

Buckley, B. C., Gobert, J. D., Kindfield, A. C. H., Horwitz, P., Tinker, R. F., Gerlits, B., & Willett, J. (2004). Model-based teaching and learning with BioLogica™: What do they learn? How do they learn? How do we know? Journal of Science Education and Technology, 13(1), 23–41. https://doi.org/10.1023/B:JOST.0000019636.06814.e3CrossRef

Cairns, D., Dickson, M., & McMinn, M. (2021). “Feeling like a Scientist”: Factors affecting students’ selections of technology tools in the science classroom. Journal of Science Education and Technology, 30(6), 766–776. https://doi.org/10.1007/s10956-021-09917-0CrossRef

Campbell, T., Oh, P. S., Maughn, M., Kiriazis, N., & Zuwallack, R. (2015). A review of modeling pedagogies: Pedagogical functions, discursive acts, and technology in modeling instruction. Eurasia Journal of Mathematics, Science and Technology Education, 11(1), 159–176. https://doi.org/10.12973/eurasia.2015.1314aCrossRef

Cavallo, A. M. L. (1996). Meaningful learning, reasoning ability, and students’ understanding and problem solving of topics in genetics. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 33(6), 625–656. https://doi.org/10.1002/(SICI)1098-2736(199608)33:6<625::AID-TEA3>3.0.CO;2-Q

Chinn, C. A., & Malhotra, B. A. (2002). Epistemologically authentic inquiry in schools: A theoretical framework for evaluating inquiry tasks. Science Education, 86(2), 175–218. https://doi.org/10.1002/sce.10001CrossRef

Cho, H. H., Kahle, J. B., & Nordland, F. H. (1985). An investigation of high school biology textbooks as sources of misconceptions and difficulties in genetics and some suggestions for teaching genetics. Science Education, 69(5), 707–719. https://doi.org/10.1002/sce.3730690512CrossRef

Clark, D., & Linn, M. C. (2003). Designing for knowledge integration: The impact of instructional time. Journal of the Learning Sciences, 12(4), 451–493. https://doi.org/10.1177/0022057409189001-210CrossRef

Corbett, A., Kauffman, L., Maclaren, B., Wagner, A., & Jones, E. (2010). A cognitive tutor for genetics problem solving: Learning gains and student modeling. Journal of Educational Computing Research, 42(2), 219–239. https://doi.org/10.2190/EC.42.2.eCrossRef

Dasgupta, S., Granger, M., & McGarry, N. (2002). User acceptance of e-collaboration technology: An extension of the technology acceptance model. Group Decision and Negotiation, 11(2), 87–100. https://doi.org/10.1016/j.compedu.2012.10.001CrossRef

Davis, E. A. (2000). Scaffolding students’ knowledge integration: Prompts for reflection in KIE. International Journal of Science Education, 22(8), 819–837. https://doi.org/10.1080/095006900412293CrossRef

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340. https://doi.org/10.2307/249008CrossRef

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982CrossRef

de Jong, T., Gillet, D., Rodríguez-Triana, M. J., Hovardas, T., Dikke, D., Doran, R., & Law, E. (2021). Understanding teacher design practices for digital inquiry–based science learning: The case of Go-Lab. Educational Technology Research and Development, 69(2), 417–444. https://doi.org/10.1007/s11423-020-09904-zCrossRef

de Jong, T., Linn, M. C., & Zacharia, Z. C. (2013). Physical and virtual laboratories in science and engineering education. Science, 340(6130), 305–308. https://doi.org/10.1126/science.1230579CrossRef

de Jong, T., Sotiriou, S., & Gillet, D. (2014). Innovations in STEM education: The Go-Lab federation of online labs. Smart Learning Environments, 1(1), 1–16. https://doi.org/10.1186/s40561-014-0003-6CrossRef

de Jong, T., Joolingen, V., & R, W. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179–201. https://doi.org/10.3102/00346543068002179CrossRef

Develaki, M. (2019). Methodology and epistemology of computer simulations and implications for science education. Journal of Science Education and Technology, 28(4), 353–370. https://doi.org/10.1007/s10956-019-09772-0CrossRef

Duncan, R. G., Rogat, A. D., & Yarden, A. (2009). A learning progression for deepening students’ understandings of modern genetics across the 5th–10th grades. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 46(6), 655–674. https://doi.org/10.1002/tea.20312CrossRef

Fang, S.-C., Hsu, Y.-S., Chang, H.-Y., Chang, W.-H., Wu, H.-K., & Chen, C.-M. (2016). Investigating the effects of structured and guided inquiry on students’ development of conceptual knowledge and inquiry abilities: A case study in Taiwan. International Journal of Science Education, 38(12), 1945–1971. https://doi.org/10.1080/09500693.2016.1220688CrossRef

Glasson, G. E. (1989). The effects of hands-on and teacher demonstration laboratory methods on science achievement in relation to reasoning ability and prior knowledge. Journal of Research in Science Teaching, 26(2), 121–131. https://doi.org/10.1002/tea.3660260204CrossRef

Gnesdilow, D., & Puntambekar, S. (2021). Comparing middle school students’ science explanations during physical and virtual laboratories. Journal of Science Education and Technology, 1–12. https://doi.org/10.1007/s10956-021-09941-0CrossRef

Gnidovec, T., Žemlja, M., Dolenec, A., & Torkar, G. (2020). Using augmented reality and the structure behavior function model to teach lower secondary school students about the human circulatory system. Journal of Science Education Technology, 29(774), 784. https://doi.org/10.1007/s10956-020-09850-8CrossRef

Heradio, R., de la Torre, L., & Dormido, S. (2016). Virtual and remote labs in control education: A survey. Annual Reviews in Control, 42, 1–10. https://doi.org/10.1016/j.arcontrol.2016.08.001CrossRef

Hickey, D. T., Kindfteld, A. C. H., Horwitz, P., & Christie, M. A. (1999). Advancing educational theory by enhancing practice in a technology-supported genetics learning environment. Journal of Education, 25–55.

Hovardas, T., Pedaste, M., Zacharia, Z., & de Jong, T. (2018). Model-based inquiry in computer-supported learning environments: The case of go-lab. In Cyber-physical laboratories in engineering and science education (pp. 241–268). Springer.

Johnson, M. A., & Lawson, A. E. (1998). What are the relative effects of reasoning ability and prior knowledge on biology achievement in expository and inquiry classes? Journal of Research in Science Teaching, 35(1), 89–103. https://doi.org/10.1002/(SICI)1098-2736(199801)35:1<89::AID-TEA6>3.0.CO;2-J

Keselman, A. (2003). Supporting inquiry learning by promoting normative understanding of multivariable causality. Journal of Research in Science Teaching, 40(9), 898–921. https://doi.org/10.1002/tea.10115CrossRef

Leijen, Ä., Valtna, K., Leijen, D. A. J., & Pedaste, M. (2012). How to determine the quality of students’ reflections? Studies in Higher Education, 37(2), 203–217. https://doi.org/10.1080/03075079.2010.504814CrossRef

Lewis, J., & Wood-Robinson, C. (2000). Genes, chromosomes, cell division and inheritance-do students see any relationship? International Journal of Science Education, 22(2), 177–195. https://doi.org/10.1080/095006900289949CrossRef

Linn, M. C. (1995). Designing computer learning environments for engineering and computer science: The scaffolded knowledge integration framework. Journal of Science Education and Technology, 4(2), 103–126. https://doi.org/10.1007/BF02214052CrossRef

Linn, M. C. (2000). Designing the knowledge integration environment. International Journal of Science Education, 22(8), 781–796. https://doi.org/10.1080/095006900412275CrossRef

Linn, M. C., Lee, H.-S., Tinker, R., Husic, F., & Chiu, J. L. (2006). Teaching and assessing knowledge integration in science. Science. https://doi.org/10.1126/science.1131408CrossRef

Liu, O. L., Lee, H.-S., Hofstetter, C., & Linn, M. C. (2008). Assessing knowledge integration in science: Construct, measures, and evidence. Educational Assessment, 13(1), 33–55. https://doi.org/10.1080/10627190801968224CrossRef

Ministry of Education. (2014). Curriculum guidelines of 12-year basic education: General guidelines. In Taipei: Ministry of Education.

Ministry of Education. (2018). Curriculum guidelines of 12-year basic education for elementary, junior high schools and general senior high schools – Natural sciences. In Taipei: Ministry of Education.

Minner, D. D., Levy, A. J., & Century, J. (2010). Inquiry-based science instruction—What is it and does it matter? Results from a research synthesis years 1984 to 2002. Journal of Research in Science Teaching, 47(4), 474–496. https://doi.org/10.1002/tea.20347CrossRef

National Research Council. (1996). National science education standards. National Academies Press.

NGSS Lead States. (2013). Next generation science standards: For states, by states. In. Washington, DC: The National Academies Press.

Pedaste, M., Mäeots, M., Siiman, L. A., de Jong, T., Van Riesen, S. A., Kamp, E. T., & Tsourlidaki, E. (2015). Phases of inquiry-based learning: Definitions and the inquiry cycle. Educational Research Review, 14, 47–61. https://doi.org/10.1016/j.edurev.2015.02.003CrossRef

Potkonjak, V., Gardner, M., Callaghan, V., Mattila, P., Guetl, C., Petrović, V. M., & Jovanović, K. (2016). Virtual laboratories for education in science, technology, and engineering: A review. Computers & Education, 95, 309–327. https://doi.org/10.1016/j.compedu.2016.02.002CrossRef

Schwartz, R., Lederman, N., & Crawford, B. (2004). Developing views of nature of science in an authentic context: An explicit approach to bridging the gap between nature of science and scientific inquiry. Science Education, 88, 610–645. https://doi.org/10.1002/sce.10128CrossRef

Slack, S. J., & Stewart, J. (1990). High school students’ problem-solving performance on realistic genetics problems. Journal of Research in Science Teaching, 27(1), 55–67. https://doi.org/10.1002/tea.3660270106CrossRef

Smith, M., & Gericke, N. (2013). Mendel in the modern classroom. Science & Education. https://doi.org/10.1007/s11191-013-9629-yCrossRef

Stewart, J. (1983). Student problem solving in high school genetics. Science Education, 67(4), 523–540. https://doi.org/10.1002/sce.3730670408CrossRef

Stewart, J. (1988). Potential learning outcomes from solving genetics problems: A typology of problems. Science Education, 72(2), 237–254. https://doi.org/10.1002/sce.3730720211CrossRef

Stewart, J. H. (1982). Difficulties experienced by high school students when learning basic Mendelian genetics. The American Biology Teacher, 44(2), 80–89. https://doi.org/10.2307/4447413CrossRef

Sullivan, S., Gnesdilow, D., Puntambekar, S., & Kim, J.-S. (2017). Middle school students’ learning of mechanics concepts through engagement in different sequences of physical and virtual experiments. International Journal of Science Education, 39(12), 1573–1600. https://doi.org/10.1080/09500693.2017.1341668CrossRef

Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312. https://doi.org/10.1016/j.compedu.2008.08.006CrossRef

Tsui, C.-Y., & Treagust, D. F. (2003). Genetics reasoning with multiple external representations. Research in Science Education, 33(1), 111–135. https://doi.org/10.4324/9780429443961-7CrossRef

Tsui, C. Y., & Treagust, D. (2010). Evaluating secondary students’ scientific reasoning in genetics using a two-tier diagnostic instrument. International Journal of Science Education, 32(8), 1073–1098. https://doi.org/10.1080/09500690902951429CrossRef

Tsui, C. Y., & Treagust, D. F. (2007). Understanding genetics: Analysis of secondary students’ conceptual status. Journal of Research in Science Teaching: THe Official Journal of the National Association for Research in Science Teaching, 44(2), 205–235. https://doi.org/10.1002/tea.20116CrossRef

Ulus, B., & Oner, D. (2020). Fostering middle school students’ knowledge integration using the Web-based inquiry science environment (WISE). Journal of Science Education and Technology, 29(2), 242–256. https://doi.org/10.1007/s10956-019-09809-4CrossRef

VanVoorhis, C. R. W., & Morgan, B. L. (2007). Understanding power and rules of thumb for determining sample sizes. Tutorials in Quantitative Methods for Psychology, 3(2), 43–50.CrossRef

Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision sciences, 27(3), 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb01822.xCrossRef

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926CrossRef

White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3–118. https://doi.org/10.1207/s1532690xci1601_2CrossRef

Yenilmez, A., Sungur, S., & Tekkaya, C. (2006). Students’ achievement in relation to reasoning ability, prior knowledge and gender. Research in Science & Technological Education, 24(1), 129–138. https://doi.org/10.1080/02635140500485498CrossRef

Zhai, X., & Shi, L. (2020). Understanding how the perceived usefulness of mobile technology impacts physics learning achievement: A pedagogical perspective. Journal of Science Education and Technology, 29(6), 743–757. https://doi.org/10.1007/s10956-020-09852-6CrossRef

Zimmerman, C. (2007). The development of scientific thinking skills in elementary and middle school. Developmental Review, 27(2), 172–223. https://doi.org/10.1016/j.dr.2006.12.001CrossRef

Title: The Go-Lab Platform, an Inquiry-learning Space: Investigation into Students’ Technology Acceptance, Knowledge Integration, and Learning Outcomes
Authors: Chi-Jung Sui
Hsin-Chueh Chen
Ping-Han Cheng
Chun-Yen Chang
Publication date: 29-11-2022
Publisher: Springer Netherlands
Published in: Journal of Science Education and Technology / Issue 1/2023
Print ISSN: 1059-0145
Electronic ISSN: 1573-1839
DOI: https://doi.org/10.1007/s10956-022-10008-x

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