Examining of Secondary School Students’ Integrated Science Process Skills

Year 2019, , 1137 - 1157, 15.12.2019

Oranit Chokchai Paitoon Pimdee

https://doi.org/10.17478/jegys.597449

Abstract

Integrated
science process skills (ISPS) are a
fundamental variable in scientific inquiry and scientific literacy. If students
are proficient in this skill, they will be ready for living and working in the 21^st-century, which requires the application of scientific
knowledge and scientific inquiry. For these reasons, the researchers undertook a study
to investigate the ISPS of Thai lower secondary school students. From the use of stratified random sampling, 350
Bangkok, Thailand Grade 8 secondary school students were selected. The SPSS Version 23 statistics software was used for data
analysis of the mean and standard deviation. A first-order confirmatory factor analysis (CFA) and
a two-way analysis of variance
(ANOVA) were also employed. Findings from the research determined that student
ISPS consisted of five indicators, including 1)
controlling variables 2) hypotheses formulation 3) defining variables operationally 4) experimentation, and 5) data
interpretation. Findings also revealed that overall, student ISPS were at a
level that needed improvement.

Keywords

gender, 21st-century, school size, confirmatory factor analysis, IPST, Thailand

References

• Abungu, H. E. O., Okere, M. I. O., & Wachanga, S. W. (2014). Effect of science process skills teaching strategy on boys and girls’ achievement in chemistry in Nyando District, Kenya. Journal of Education and Practice, 5(15), 42-48. Retrieved from https://tinyurl.com/ybeespfx
• Aktamis, H., & Ergin, Ö. (2008). The effect of scientific process skills education on students' scientific creativity, science attitudes, and academic achievements. Asia-Pacific Forum on Science Learning and Teaching, 9(1). Retrieved from https://www.eduhk.hk/apfslt/
• AAAS. (1967). Science–A process approach. Washington, DC: American Association for the Advancement of Science.
• AAAS. (1993). Benchmarks for science literacy. American Association for the Advancement of Science. New York, NY: Oxford University Press.
• Anderson, R. D. (2002). Reforming science teaching: What research says about inquiry. Journal of Science Teacher Education, 13(1), 1-12. DOI: https://doi.org/10.1023/ A:1015171124982
• Athuman, J. J. (2017). Comparing the effectiveness of an inquiry-based approach to that of conventional style of teaching in the development of students’ science process skills. International Journal of Environmental and Science Education, 12(8), 1797-1816. Retrieved from http://www.ijese.net/makale/1943
• Baird, W. E., & Borich, G. D. (1987). Validity considerations for research on integrated-science process skills and formal reasoning ability. Science Education, 71(2), 259 – 269. DOI: https://doi.org/10.1002/sce.3730710212
• Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42(5), 815-824. DOI: https://doi.org/10.1016/j.paid.2006.09.018
• Bartholomew, D. J., Steele, F., Moustaki, I., & Galbraith, J. I. (2008). Analysis of multivariate social science data (2nd ed.). Boca Raton, FL: CRC Press.
• Beaumont-Walters, Y., & Soyibo, K. (2001). An analysis of high school students' performance on five integrated science process skills. Research in Science & Technological Education, 19(2), 133-145. DOI: https://doi.org/10.1080/02635140120087687
• Bell, T., Urhahne, D., Schanze, S., & Ploetzner, R. (2010). Collaborative inquiry learning: models, tools, and challenges. International Journal of Science Education, 32(3), 349 – 377. DOI: https://doi.org/10.1080/09500690802582241
• Bilgin, I. (2006). The effect of hands-on activities incorporating a cooperative learning approach on eight students science process skills and attitudes towards science. Journal of Baltic Science Education, 9(1), 27-36. Retrieved from https://tinyurl.com/ycx54orj
• Bolat, M., Türk, C., Turna, Ö., & Altinbaş, A. (2014). Science and technology teacher candidates’ use of integrated process skills levels: A simple electrical circuit sample. Procedia-Social and Behavioral Sciences, 116, 2660-2663. DOI: https://doi.org/ 10.1016/j.sbspro.2014.01.631
• Brotherton, P. N., & Preece, P. F. W. (1995). Science process skills: Their nature and interrelationships. Research in Science & Technological Education, 13(1), 5-11. DOI: https://doi.org/10.1080/0263514950130101
• Chabalengula, V. M., Mumba, F., & Mbewe, S. (2012). How pre-service teachers’ understand and perform science process skills. EURASIA Journal of Mathematics, Science and Technology Education, 8(3), 167–176. DOI: https://doi.org/10.12973/eurasia.2012.832a
• Dillashaw, F. G., & Okey, J. R. (1980). Test of the integrated science process skills for secondary science students. Science Education, 64(5), 601–608. DOI: https://doi.org/ 10.1002/sce.3730640506
• Ekon, E. E., & Eni, E. I. (2015). Gender and acquisition of science process skills among junior secondary school students in Calabar Municipality: Implications for implementation of universal basic education objectives. Global Journal of Educational Research, 14(1), 93-99. DOI: https://doi.org/10.4314/gjedr.v14i1.3
• G*Power 3.1 manual. (2017). Retrieved from https://tinyurl.com/jt8a83u
• Gagne, P., & Hancock, G. R. (2006). Measurement model quality, sample size, and solution propriety in confirmatory factor models. Multivariate Behavioral Research, 41(1), 65-83. DOI: https://doi.org/10.1207/s15327906mbr4101_5
• Germann, P. J. (1991). Developing science process skills through directed inquiry. The American Biology Teacher, 53(4), 243–247. DOI: https://doi.org/10.2307/4449277
• Germann, P. J., Aram, R., & Burke, G. (1996). Identifying patterns and relationships among the responses of seventh-grade students to the science process skill of designing experiment. Journal of Research in Science Teaching, 33(1), 79-99. DOI: https://doi.org/ 10.1002/(sici)1098-2736(199601)33:1<79::aid-tea5>3.0.co;2-m
• Gultepe, N. (2016). High school science teachers’ views on science process skills. International Journal of Environmental & Science Education, 11(5), 779-800. DOI: https://doi.org/ 10.12973/ijese.2016.348a
• Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Harlow, UK: Pearson.
• Hambleton, R. K., & Rovinelli, R. J. (1986). Assessing the dimensionality of a set of test items. Applied Psychological Measurement, 10(3), 287-302. DOI: https://doi.org/10.1177/ 014662168601000307
• Harlen, W. (1999). Purposes and procedures for assessing science process skills. Assessment in Education: Principles, Policy & Practice, 6(1), 129-144. DOI: https://doi.org/ 10.1080/09695949993044
• Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modeling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60. Retrieved from http://tinyurl.com/zyd6od2
• Jack, G. U. (2013). The influence of identified student and school variables on students’ science process skills acquisition. Journal of Education and Practice, 4(5), 16-22. Retrieved from https://tinyurl.com/ychtrvk3
• Jack, G. U. (2018). Chemistry students’ science process skills acquisition: Influence of gender and class size. Global Research in Higher Education, 1(1), 80-97. DOI: https://doi.org/ 10.22158/grhe.v1n1p80
• Karsli, F., & Şahin, Ç. (2009). Developing worksheet based on science process skills: Factors affecting solubility. Asia-Pacific Forum on Science Learning and Teaching, 10(1). Retrieved from https://www.eduhk.hk/apfslt/
• Kasayanond, A., Umam, R., & Jermsittiparsert, K. (2019). Environmental sustainability and its growth in Malaysia by elaborating the green economy and environmental efficiency. International Journal of Energy Economics and Policy, 9(5), 465–473. DOI: https://doi.org/ 10.32479/ijeep.8310
• Kenny, D. A., & McCoach, D. B. (2003). Effect of the number of variables on measures of fit in structural equationmodeling. Structural Equation Modeling: A Multidisciplinary Journal, 10(3), 333-351. DOI: https://doi.org/10.1207/s15328007sem1003_1
• Keselman, A. (2003). Supporting inquiry learning by promoting normative understanding of multivariable causality. Journal of Research in Science Teaching, 40, 898–921. DOI: https://doi.org/10.1002/tea.10115
• Kruea-In, N., & Thongperm, O. (2014). Teaching of science process skills in Thai contexts: Status, supports and obstacles. Procedia - Social and Behavioral Sciences, 141, 1324 – 1329. DOI: https://doi.org/10.1016/j.sbspro.2014.05.228
• Lati, W., Supasorn, S., & Promarak, V. (2012). Enhancement of learning achievement and integrated science process skills using science inquiry learning activities of chemical reaction rates. Procedia-Social and Behavioral Sciences, 46, 4471-4475. DOI: https://doi.org/10.1016/j.sbspro.2012.06.279
• Lee, V. E., & Loeb, S. (2000). School size in Chicago elementary schools: Effects on teachers' attitudes and students' achievement. American Educational Research Journal, 37(1), 3-31. DOI: https://doi.org/10.2307/1163470
• Lestari, F., Saryantono, B., Syazali, M., Saregar, A., Madiyo, M., Jauhariyah, D., & Umam, R. (2019). Cooperative Learning Application with the Method of "Network Tree Concept Map": Based on Japanese Learning System Approach. Journal for the Education of Gifted Young Scientists, 7(1), 15 – 32. DOI: https://doi.org/10.17478/jegys.471466
• Lind, K. K. (1999). Science in early childhood: Developing and acquiring fundamental concepts and skills. Retrieved fromhttps://tinyurl.com/y9y8jaee
• Loehlin, J.C. (1992). Latentvariablemodels. Hillsdale, NJ: Lawrence Erlbaum Publishers.
• Mala, D. (2016, December 8). Pisa test rankings put Thai students near bottom of Asia. Bangkok Post. Retrieved from https://tinyurl.com/ya7wop9g
• Mala, D. (2018, June 22). Thailand threatens to quit Pisa test. Bangkok Post. Retrieved from https://tinyurl.com/yd28779d
• Martin, M. O., Mullis, I. V. S., Foy, P., & Hooper, M. (2016). As global study TIMSS turns 20, new results show East Asian students continue to outperform peers in mathematics. Retrieved from https://tinyurl.com/y7jc627m
• Minstrell, J., & van Zee, E. H. (2000). Inquiring into Inquiry Learning and Teaching in Science. AAAS Project 2061.
• Moeed, A. (2013). Science investigation that best supports student learning: Teachers' understanding of science investigation. International Journal of Environmental & Science Education, 8, 537-559. DOI: https://doi.org/10.12973/ijese.2013.218a
• Mulaik, S. A. (2009). Linear causal modeling with structural equations. Boca Raton, Fl: Chapman & Hall/CRC.
• Mullis, I. V. S., Martin, M. O., Goh, S., & Cotter, K. (2015). TIMSS 2015 Encyclopedia: Education Policy and Curriculum in Mathematics and Science. Retrieved fromhttps:// tinyurl.com/y9j46joq
• NRC. (1996). National science education standards. Washington, DC: The National Academy Press. DOI: https://doi.org/10.17226/4962
• NRC. (2000). Inquiry and the national science education standards: A guide for teaching and learning. Washington, DC: The National Academy Press. DOI: https://doi.org/10.17226/9596
• NRC. (2005). America’s lab report: Investigations in high school science. Washington, DC: The National Academies Press.
• NRC. (2007). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: The National Academies Press.
• Ning, W., & Kim, H. J. (2008). Residual pattern based test for interactions in two-way ANOVA. Biometrical Journal, 50(3), 431-445. DOI: https://doi.org/10.1002/ bimj.200710427
• OECD. (2013a). PISA 2012 results: Excellence through equity: Giving every student the chance to succeed (Volume II), PISA, OECD Publishing. Retrieved from https://tinyurl.com/y8g4hxjb
• OECD. (2013b). PISA in focus. Retrieved from https://tinyurl.com/yb9h4g4u
• OECD. (2014). PISA 2012 results: What students know and can do - student performance in mathematics, reading and science (Volume I, revised edition, February 2014), PISA, OECD Publishing. Retrieved fromhttps://tinyurl.com/y9s8g2xc
• OECD. (2016). PISA 2015 results (volume 1): Excellence and equity in education. Paris, France: OECD Publishing. DOI: https://doi.org/10.1787/9789264266490-en
• OECD. (2018). PISA 2015 results. Retrieved from https://tinyurl.com/ydcnuxl8
• Özgelen, S. (2012). Students’ science process skills within a cognitive domain framework. Eurasia Journal of Mathematics, Science & Technology Education, 8(4), 283-292. DOI: https://doi.org/10.12973/eurasia.2012.846a
• Özgelen, S. (2017). Primary school students’ views on science and scientists. In P. Katz P. (Ed.), Drawing for Science Education. Rotterdam, Netherlands: Sense Publishers. DOI: https://doi.org/10.1007/978-94-6300-875-4_17
• Padilla, M. J. (1990). The science process skills. Research Matters-to the Science Teacher. Retrieved from https://tinyurl.com/kod7p6w
• Roth, W.-M., & Roychoudhury, A. (1993).The development of science process skills in authentic contexts. Journal of Research in Science Teaching, 30(2), 127-152. DOI: https://doi.org/10.1002/tea.3660300203
• Ryan, R.M., & Deci, E. L. (2009). Promoting self-determined school engagement: Motivation, learning and well-being. In K. Wentzel, A. Wigfield and D. Miele (Eds.). Handbook of Motivation at School, (pp. 171-195), New York, NY: Routledge.
• Sagala, R., Umam, R., Thahir, A., Saregar , A., & Wardani, I. (2019). The Effectiveness of STEM-Based on Gender Differences: The Impact of physics concept understanding. European Journal of Educational Research, 8(3), DOI: https://doi.org/10.12973/eu-jer.8.3.753
• Sermsirikarnjana, P., Kiddee, K., & Pupat, P. (2017). An integrated science process skills needs assessment analysis for Thai vocational students and teachers. Asia-Pacific Forum on Science Learning and Teaching, 18(2). Retrieved from https://www.eduhk.hk/apfslt/
• Syazali, M., Putra, F. G., Rinaldi, A., Utami, K. F., Widayanti, R. U., & Jermsittiparsert, K. (2019). Partial correlation analysis using multiple linear regression: Impact on business environment of digital marketing interest in the era of industrial revolution 4.0. Management Science Letters, 1875 – 1886. DOI: https://doi.org/10.5267/j.msl.2019.6.005
• Shahali, E. H. M., & Halim, L. (2010). Development and validation of a test of integrated science process skills. Procedia-Social and Behavioral Sciences, 9, 142-146. DOI: https://doi.org/10.1016/j.sbspro.2010.12.127
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. New York, NY: Pearson Education.
• Turiman, P., Omar, J., Daud, A. M., & Osman, K. (2012). Fostering the 21st century skills through scientific literacy and science process skills. Procedia-Social and Behavioral Sciences, 59, 110-116.
• Turiman, P., Osman, K., Meriam, T. S., & Wook, T. (2017). Digital age literacy proficiency among science preparatory course students. 6th International Conference on Electrical Engineering and Informatics (ICEEI). Langkawi, Malaysia. DOI: https://doi.org/10.1109/iceei.2017.8312429
• Turner, R. C., & Carlson, L. (2003). Indexes of item-objective congruence for multidimensional items. International Journal of Testing, 3(2), 163-171. DOI: https://doi.org/10.1207/S15327574IJT0302_5
• UNESCO. (n/d). Introduction to inquiry: An online course for teachers to learn about the inquiry learning cycle. Retrieved from https://tinyurl.com/y8gsky99
• Wigfield, A., & Eccles, J. S. (2000). Expectancy–Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25(1), 68 – 81. DOI: https://doi.org/10.1006/ceps.1999.1015
• Yap, K. C., & Yeany, R. H. (1988). Validation of hierarchical relationships among Piagetian cognitive modes and integrated science process skills for different cognitive reasoning levels. Journal of Research in Science Teaching, 25(4), 247-281. DOI: https://doi.org/10.1002/tea.3660250402
• Yeany, R. H., Yap. K. C., & Padilla, M. J. (1986). Analyzing hierarchical relationships among modes of cognitive reasoning and integrated science process skills. Journal of Research in Science Teaching, 3(4), 277-291. DOI: https://doi.org/10.1002/tea.3660230403
• Yuenyong, C., Jones, A., & Yutakom, N. (2007). A comparison of Thailand and New Zealand students’ ideas about energy related to technological and societal issues. International Journal of Science and Mathematics Education, 6(2), 293–311. DOI: https://doi.org/10.1007/s10763-006-9060-9
• Zimmerman, D. W., & Burkheimer, G. J. (1968).Coefficient alpha, test reliability, and heterogeneity of score distributions. The Journal of Experimental Education, 37(2), 90-96. DOI: https://doi.org/10.1080/00220973.1968.11011118