Implementation of Mechanical Technology Competence Learning Model with Maximum Likelihood Estimation

A Muhammad Idkhan, - Djuanda, Iswahyudi Indra Putra

Abstract


Developed countries in the world need evaluation in the world of education. This evaluation is used to formulate policies that support the creation of competitive human resources against the industrial era 4.0. This research conducted to analyze and look at the mechanical, technological competencies of students who are influenced by factors of learning psychology, namely the scientific approach to learning, the level of student independence and the reasoning abilities of students. This study will use a non-probability sampling technique, namely accidental sampling technique. This method is a sampling procedure that selects samples from people or units that are most easily found or accessed as respondents. It is undoubtedly by the size of the sample in the Structural Equation Model with the estimation model using a minimum Maximum Likelihood (ML). The population of this research is the tenth-grade students in the Mechanical Engineering expertise program which consists of the Field of Mechanical Engineering and Welding Techniques in the Vocational High School in Makassar City, amounting to 248 students. The number of samples used was 120 respondents considering the outlier numbers at the period of the examination. The exogenous variable in this study is the implementation of the scientific approach, learning independence. The endogenous variable in this study is the achievement of mechanical technology competencies while the intervening variable or connecting variable is reasoning ability. From the consequences of the analysis, there is a significant influence between the variable ability to mechanical technology competence and the variable self-regulated learning to mechanical technology competence. It shows that students mandate learning, either directly or indirectly, will improve student competence but must be through understanding students' ethical reasoning. From the results of the study, there is a significant influence between the variable ability to mechanical technology competence and the variable self-regulated learning to mechanical technology competence. It shows that students mandate learning, either directly or indirectly, will improve student competence but must be through understanding students' ethical reasoning.

Keywords


evaluation education; scientific approach; reasoning; self-regulated learning; structural equation model analysis.

Full Text:

PDF

References


A. Green, “Education, globalization and the nation state,†in Education, Globalization and the Nation State, Springer, 1997, pp. 130–186.

M. Friedman, The role of government in education, vol. 13. Rutgers University Press New Brunswick, NJ, 1955.

W. E. Deming, The new economics for industry, government, education. MIT press, 2018.

United Nations Educational, Scientific and Cultural Organization (UNESCO)., “Education for All Global Monitoring Report 2010: Reaching the Marginalized,†2010.

E. Unterhalter, Measuring gender inequality in education in South Asia. Citeseer, 2006.

United Nations Educational, Scientific and Cultural Organization (UNESCO)., Education for All Global Monitoring Report 2008: Education for All by 2015. Will We Make It? Oxford University Press, 2007.

J. Hattie, J. Biggs, and N. Purdie, “Effects of learning skills interventions on student learning: A meta-analysis,†Rev. Educ. Res., vol. 66, no. 2, pp. 99–136, 1996.

E. J. Simpson, “The Classification of Educational Objectives, Psychomotor Domain.,†1966.

T. C. Reeves, “How do you know they are learning? The importance of alignment in higher education,†Int. J. Learn. Technol., vol. 2, no. 4, pp. 294–309, 2006.

R. W. Bybee, Achieving scientific literacy: From purposes to practices. ERIC, 1997.

F. Reif, “Teaching problem solvingâ€A scientific approach,†Phys. Teach., vol. 19, no. 5, pp. 310–316, 1981.

N. R. Council, Advancing scientific research in education. National Academies Press, 2004.

L. Ivanitskaya, D. Clark, G. Montgomery, and R. Primeau, “Interdisciplinary learning: Process and outcomes,†Innov. High. Educ., vol. 27, no. 2, pp. 95–111, 2002.

National Center for Education Statistics (NCES), “Trends in International Maths and Science Survey (TIMSS) 2007,†U.S. Department of Education (https://www.ed.gov/), 2007. [Online]. Available: https://nces.ed.gov/timss/results07_science07.asp. [Accessed: 05-Apr-2019].

P. C. Blumenfeld, E. Soloway, R. W. Marx, J. S. Krajcik, M. Guzdial, and A. Palincsar, “Motivating project-based learning: Sustaining the doing, supporting the learning,†Educ. Psychol., vol. 26, no. 3–4, pp. 369–398, 1991.

F. N. Kerlinger and H. B. Lee, Foundations of Behavioral Research, 4th ed. New York: Holt, Rinehart and Winston, 2000.

S. Schumacher and J. H. McMillan, Research in Education Evidence–Based Inquiry. Boston: Pearson Education, 2006.

G. D. Garson, Partial Least Squares: Regression & structural equation modeling. Asheboro, USA: Statistical Publishing Associates, 2016.

G. D. Garson, Structural Equation Modeling, Blue Book. Asheboro, North Corolina: Statistical Associates Publishing, 2012.

J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 7th ed. Harlow, England: Pearson New International Edition, 2014.

R. H. Hoyle, Structural Equation Modeling : Concepts, Issues, and Applications. Thousand Oaks, California: SAGE Publications, Inc., 1995.

A. H. Hasim, L. B. Said, and S. M. Hafram, “Have a Personal Vehicle : Is it really need or simply want ?,†vol. 1, no. 1, pp. 7–16, 2019.

B. M. Byrne, Structural equation modeling with AMOS: Basic concepts, applications, and programming, 2nd ed. New York: Routledge, 2016.

D. Gefen, E. E. Rigdon, and D. Straub, “Editor’s comments: an update and extension to SEM guidelines for administrative and social science research,†Mis Q., pp. iii–xiv, 2011.

R. E. Schumacher and R. G. Lomax, A Beginner’s Guide to Structural Equation Modeling: Third Edition, 3rd ed. Mahwah, NJ: Lawrence Erlbaum Associates, 2010.

R. B. Kline, “Promise and pitfalls of structural equation modeling in gifted research.,†2010.

A. Boomsma, “Reporting analyses of covariance structures,†Struct. Equ. Model., vol. 7, no. 3, pp. 461–483, 2000.

K. G. Jöreskog and D. Sörbom, LISREL 8: Structural equation modeling with the SIMPLIS command language. Scientific Software International, 1993.

J. J. Hox and T. M. Bechger, “An introduction to structural equation modeling,†2007.

B. Wheaton, B. Muthen, D. F. Alwin, and G. F. Summers, “Assessing reliability and stability in panel models,†Sociol. Methodol., vol. 8, pp. 84–136, 1977.

E. G. Carmines, “Analyzing models with unobserved variables,†Soc. Meas. Curr. issues, vol. 80, 1981.

J. S. Tanaka and G. J. Huba, “A general coefficient of determination for covariance structure models under arbitrary GLS estimation,†Br. J. Math. Stat. Psychol., vol. 42, no. 2, pp. 233–239, 1989.

J. H. Steiger and J. C. Lind, “Statistically based tests for the number of common factors,†in Paper presented at the annual meeting of the Psychometric Society, May 1980, 1980.

M. W. Browne and R. Cudeck, “Alternative ways of assessing model fit,†Sage Focus Ed., vol. 154, p. 136, 1993.

L. J. Williams and E. O’Boyle Jr, “The myth of global fit indices and alternatives for assessing latent variable relations,†Organ. Res. Methods, vol. 14, no. 2, pp. 350–369, 2011.

F. Chen, P. J. Curran, K. A. Bollen, J. Kirby, and P. Paxton, “An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models,†Sociol. Methods Res., vol. 36, no. 4, pp. 462–494, 2008.

L. Hu and P. M. Bentler, “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives,†Struct. Equ. Model. a Multidiscip. J., vol. 6, no. 1, pp. 1–55, 1999.

L. R. Tucker and C. Lewis, “A reliability coefficient for maximum likelihood factor analysis,†Psychometrika, vol. 38, no. 1, pp. 1–10, 1973.

P. M. Bentler and L. T. Hu, “Evaluating model fit,†in Structural equation modeling: Concepts, issues, and applications, Thousand Oaks, CA: SAGE Publications, 1995, pp. 76–99.

P. M. Bentler, “SEM with simplicity and accuracy,†J. Consum. Psychol., vol. 20, no. 2, pp. 215–220, 2010.

T. A. Brown, Confirmatory factor analysis for applied research. New York: Guilford Publications, 2014.

K. A. Bollen, “A new incremental fit index for general structural equation models,†Sociol. Methods Res., vol. 17, no. 3, pp. 303–316, 1989.

L. James, S. Mulaik, and J. M. Brett, Causal analysis: Assumptions, models, and data. Beverly Hills: Sage publications, 1982.

C. C. DiClemente and J. O. Prochaska, “Self-change and therapy change of smoking behavior: A comparison of processes of change in cessation and maintenance,†Addict. Behav., vol. 7, no. 2, pp. 133–142, 1982.

K. A. Bollen and J. S. Long, Testing structural equation models, vol. 154. Sage, 1993.

M. S. Khine, L. C. Ping, and D. Cunningham, Application of Structural Equation Modeling in Educational Research and Practice : Contemporary Approaches to Research, 7th ed. Rotterdam, Netherlands: Sense Publishers, 2013.

R. P. Bagozzi and Y. Yi, “On the evaluation of structural equation models,†J. Acad. Mark. Sci., vol. 16, no. 1, pp. 74–94, 1988.

D. George and P. Mallery, IBM SPSS Statistics 23 Step by Step: A Simple Guide and Reference, 14th ed. New York, United States: Routledge, 2016.

W. B. Arthur, “Inductive reasoning and bounded rationality,†Am. Econ. Rev., vol. 84, no. 2, pp. 406–411, 1994.

R. B. Sund and A. A. Carin, “Teaching science through discovery,†Ohio, O., Merrill, Co, 1975.

J. S. Bruner, “The act of discovery,†Harv. Educ. Rev., vol. 31, pp. 21–32, 1961.

J. Piaget, “Piaget’s theory,†in Piaget and his school, Springer, 1976, pp. 11–23.

R. Gersten et al., “Assisting Students Struggling with Mathematics: Response to Intervention (RtI) for Elementary and Middle Schools. NCEE 2009-4060.,†What Work. Clear., 2009.

B. Kramarski and N. Mizrachi, “Online discussion and self-regulated learning: Effects of instructional methods on mathematical literacy,†J. Educ. Res., vol. 99, no. 4, pp. 218–231, 2006.

I. M. Copi and K. Burgess-Jackson, Informal logic. Macmillan Publishing Company, 1991.

A. L. Glass, K. J. Holyoak, and J. L. Santa, “Cognition Reading,†MA Addison-Wesley, 1979.

K. M. Galotti, “Approaches to studying formal and everyday reasoning.,†Psychol. Bull., vol. 105, no. 3, p. 331, 1989.

R. L. Shurter and J. R. Pierce, Critical Thinking. New York, United States: McGraw Hill Inc, 1966.

B. Charlin, H. P. A. Boshuizen, E. J. Custers, and P. J. Feltovich, “Scripts and clinical reasoning,†Med. Educ., vol. 41, no. 12, pp. 1178–1184, 2007.

A. J. Baroody and R. T. Coslick, Problem solving, reasoning, and communicating, K-8: Helping children think mathematically. Prentice Hall, 1993.

L. R. Novick and K. M. Catley, “Reasoning about evolution’s grand patterns: College students’ understanding of the tree of life,†Am. Educ. Res. J., vol. 50, no. 1, pp. 138–177, 2013.

B. Kaur and T. L. Toh, Reasoning, Communication and Connections in Mathematics: Yearbook 2012, Association of Mathematics Educators, vol. 4. World Scientific, 2012.

J. Hiebert and P. Lefevre, “Conceptual and procedural knowledge in mathematics: An introductory analysis,†Concept. Proced. Knowl. case Math., vol. 2, pp. 1–27, 1986.

J. Lithner, “Students’ mathematical reasoning in university textbook exercises,†Educ. Stud. Math., vol. 52, no. 1, pp. 29–55, 2003.

J. Lithner, “Mathematical reasoning in calculus textbook exercises,†J. Math. Behav., vol. 23, no. 4, pp. 405–427, 2004.

J. Lithner, “Mathematical reasoning in task solving,†Educ. Stud. Math., pp. 165–190, 2000.

T. Palm, J. Boesen, and J. Lithner, “Mathematical reasoning requirements in Swedish upper secondary level assessments,†Math. Think. Learn., vol. 13, no. 3, pp. 221–246, 2011.

J. Boesen, J. Lithner, and T. Palm, “The relation between types of assessment tasks and the mathematical reasoning students use,†Educ. Stud. Math., vol. 75, no. 1, pp. 89–105, 2010.

K. Hatzikiriakou and P. Metallidou, “Teaching deductive reasoning to pre-service teachers: Promises and constraints,†Int. J. Sci. Math. Educ., vol. 7, no. 1, pp. 81–101, 2009.

M. Hung Cheng and K. Chee Pang, “Teacher socialization: Implications for the design and management of initial teacher education programmes,†Educ. Train., vol. 39, no. 5, pp. 195–204, 1997.

J. Souders and C. Prescott, “A Case for Contextual Learning.,†High Sch. Mag., vol. 7, no. 3, pp. 38–43, 1999.

L. S. Vygotsky, The collected works of LS Vygotsky: Problems of the theory and history of psychology, vol. 3. Springer Science & Business Media, 1997.

L. Vygotsky, “Interaction between learning and development,†Readings Dev. Child., vol. 23, no. 3, pp. 34–41, 1978.

D. Parnell, Contextual Teaching Works!: Helping Students Reach Higher Levels of Achievement. CCI Pub., 2001.

J. G. Brooks and M. G. Brooks, “In search of understanding,†case Constr. classrooms, pp. 101–118, 1993.

C. A. Wolters, “Regulation of motivation: Evaluating an underemphasized aspect of self-regulated learning,†Educ. Psychol., vol. 38, no. 4, pp. 189–205, 2003.

C. S. Carver and M. Scheier, Principles of self-regulation: Action and emotion. Guilford Press, 1990.

B. J. Zimmerman, “A social cognitive view of self-regulated academic learning.,†J. Educ. Psychol., vol. 81, no. 3, p. 329, 1989.

S. A. Karabenick and R. Sharma, “Perceived teacher support of student questioning in the college classroom: Its relation to student characteristics and role in the classroom questioning process.,†J. Educ. Psychol., vol. 86, no. 1, p. 90, 1994.

D. L. Butler, “Individualizing instruction in self-regulated learning,†Theory Pract., vol. 41, no. 2, pp. 81–92, 2002.

R. Fasse, J. Humbert, and R. Rappold, “Rochester Institute of Technology: Analyzing student success.,†J. Asynchronous Learn. Networks, vol. 13, no. 3, pp. 37–48, 2009.




DOI: http://dx.doi.org/10.18517/ijaseit.9.5.9157

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development