Anhalt, C. O., & Cortez, R. (2016). Developing understanding of mathematical modeling in secondary teacher preparation. Journal of Mathematics Teacher Education, 19(6), 523-545. https://doi.org/10.1007/s10857-015-9309-8

Anhalt, C. O., Cortez, R., & Bennett, A. B. (2018). The emergence of mathematical modeling competencies: An investigation of prospective secondary mathematics teachers. Mathematical Thinking and Learning, 20(3), 202-221. https://doi.org/10.1080/10986065.2018.1474532

Anoling, O. Jr., Dagdag, J., Pascual, J., & Salviejo, R. (2018). Factor structure of problem-solving efficacy among college Algebra students. Journal of Research, Policy & Practice of Teachers and Teacher Education, 8(2), 19-28. https://doi.org/10.37134/jrpptte.vol8.no2.3.2018

Awang, Z. (2012). A Handbook on structural equation modeling (SEM) using AMOS. MPWS Publication Sdn Bhd.

Awang, Z., Hui, L. S., & Zainudin, N. F. S. (2018). Pendekatan mudah SEM-structural equation modelling [A simple approach to SEM-structural equation modeling]. MPWS Rich Resources.

Biccard, P., & Wessels, D. C. J. (2011). Documenting the development of modelling competencies of grade 7 mathematics students. In G. Kaiser, R. B. Ferri, W. Blum, & G. Stillman (Eds.), Trends in teaching and learning of mathematical modelling, international perspectives on the teaching and learning of mathematical modelling (pp. 375–383). Springer. https://doi.org/10.1007/978-94-007-0910-2_37

Blomhoej, M., & Jensen, T. (2003). Developing mathematical modelling competence: Conceptual clarification and educational planning. Teaching Mathematics and Its Applications, 22(3), 123–139. https://doi.org/10.1093/teamat/22.3.123

Blomhøj, M. (2009). Different perspectives on mathematical modelling in educational research - categorising the TSG21 papers. In M. Blomhøj & S. Carreira (Eds.), Mathematical applications and modelling in the teaching and learning of mathematics (pp. 1–13). Roskilde University.

Blum, W. (2011). Can modelling be taught and learnt? Some answers from empirical research. In G. Kaiser, W. Blum, R. Borromeo, & G. Stillman (Eds.), Trends in teaching and learning of mathematical modelling (pp. 15–29). Springer. https://doi.org/10.1007/978-94-007-0910-2_3

Blum, W., Galbraith, P. L., Henn, H. W., & Niss, M. (2007). Modelling and applications in mathematics education: The 14 ICMI study (Vol. 10). Springer. https://doi.org/10.1007/978-0-387-29822-1

Blummer, B., & Kenton, J. M. (2014). Improving student information search. Chandos Publishing. https://doi.org/10.1016/B978-1-84334-781-1.50020-7

Brand-Gruwel, S., Wopereis, I., & Vermetten, Y. (2005). Information problem solving by experts and novices: Analysis of a complex cognitive skill. Computers in Human Behavior, 21(3), 487–508. https://doi.org/10.1016/j.chb.2004.10.005

Brown, A. L. (1978). Knowing when, where, and how to remember: A problem of metacognition. In R. Glaser (Ed.), Advances in instructional psychology (pp. 77–165). Erlbaum.

Che Md Ghazali, N. H., Abdullah, N., Hamzah, M., & Mohamed Arif, N. R. (2019). Flat-btot: A method to improve mathematic communication skills in solving mathematic word problems. Evaluation Studies in Social Sciences, 6(1), 9-20.

Corum, K., & Garofalo, J. (2019). Engaging preservice secondary mathematics teachers in authentic mathematical modeling: Deriving Ampere's Law. Mathematics Teacher Educator, 8(1), 76-91. https://doi.org/10.5951/mathteaceduc.8.1.0076

Coskun, Y. (2018). A study on metacognitive thinking skills of university        students. Journal of Education and Training Studies, 6(3), 38-46. https://doi.org/10.11114/jets.v6i3.2931

De Lange, J. (1987). Mathematics insight and meaning. OW & OC

De Lange, J. (1996). Using and applying mathematics in education. In International handbook of mathematics education (pp. 49–97). Kluwer Academic Publishers. https://doi.org/10.1007/978-94-009-1465-0_3

De Lange, J. (2006). Mathematical literacy for Living from OECD-PISA perspective. Tsukuba Journal of Educational Study in Mathematics, 25, 13–35. https://doi.org/10.1.1.500.5073

Desoete, A., Baten, E., Vercaemst, V., De Busschere, A., Baudonck, M., & Vanhaeke, J. (2019). Metacognition and motivation as predictors for mathematics performance of Belgian elementary school children. International Reviews on Mathematical Education/ Zentralblatt für Didaktik der Mathematik, 51(4), 667-677. https://doi.org/10.1007/s11858-018-01020-w

Desoete, A., & De Craene, B. (2019). Metacognition and mathematics education: An overview. International Reviews on Mathematical Education/ Zentralblatt für Didaktik der Mathematik, 51(4), 565-575. https://doi.org/10.1007/s11858-019-01060-w

English, L. (2003). Mathematical modeling with young learners. In S. J. Lamon, W. A. Parker, & S. K. Houston (Eds.), Mathematical modelling: A way of life. ICTMA11 (pp. 3–7). Horwood.

Fathurohman, I., & Cahyaningsih, R. D. (2021). Development of authentic assessment instrument for improving extensive capacity of metacognitive perspective. Asian Journal of Assessment in Teaching and Learning, 11(1), 13-21. https://doi.org/10.37134/ajatel.vol11.1.2.2021

Flavell, J. H. (1979). Metacognition and cognitive monitoring. A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906

France, B. (2018). Modeling in technology education: A route to technological literacy. In M. J. de Vries (Eds.), Handbook of technology education. Springer. https://doi.org/10.1007/978-3-319-44687-5_45

Frejd, P., & Ärlebäck, J. B. (2011). First results from a study investigating Swedish upper secondary students’ mathematical modelling competencies. In G. Kaiser, W. Blum, R. Borromeo, & G. Stillman (Eds.), Trends in teaching and learning of mathematical modelling. International perspectives on the teaching and learning of mathematical modelling (Vol. 1, pp. 407–416). Springer. https://doi.org/10.1007/978-94-007-0910-2_40

Freudenthal, H. (1991). Revisiting mathematics education, China lectures. Kluwer Academic Publishers.

Fu, J., & Xie, J. (2013). Comparison of mathematical modelling skills of secondary and tertiary students. In Teaching mathematical modelling: Connecting to research and practice (pp. 165-173). Springer. https://doi.org/10.1007/978-94-007-6540-5_14

Galbraith, P. (2006). Real world problems: Developing principles of design. In P. Grootenboer, R. Zevenbergen, & M. Chinnappan (Eds.), Identities, cultures and learning spaces (Proceedings of the 29th annual conference of the mathematics education research group of Australasia, Canberra (Vol. 1, pp. 228–236). MERGA.

Galbraith, P. (2017). Forty years on: Mathematical modelling in and for education. In A. Downton, S. Livy, & J. Hall (Eds.), 40 Years on: We are still learning! Proceedings of the 40th annual conference of the mathematics education research group of Australasia (pp. 47–50). MERGA.

Garofalo, J., & Lester, F. K. (1985). Metacognition, cognitive monitoring, and mathematical performance. Journal for Research in Mathematics Education, 16(3), 163–176. https://doi.org/10.2307/748391

Germain-Williams, T. L. (2014). Mathematical modeling in algebra textbooks at the onset of the Common Core State Standards (Publication No. 3620395) [Doctoral dissertation, Columbia University]. ProQuest Dissertations and Theses Global.

Gravemeijer, K., & Doorman, M. (1999). Context problems in realistic mathematics education: A calculus course as an example. Educational Studies in Mathematics, 39(1), 111–129. https://doi.org/10.1023/A:1003749919816

Gravemeijer, K., & Terwel, J. (2000). Hans Freudenthal: A mathematician on didactics and curriculum theory. Journal of Curriculum Studies, 32(6), 777–796. https://doi.org/10.1080/00220270050167170

Haines, C., & Crouch, R. (2001). Recognizing constructs within mathematical modelling. Teaching Mathematics and Its Applications, 20(3), 129–138. https://doi.org/10.1093/teamat/20.3.129

Haines, C. R., & Crouch, R. (2010). Remarks on a modeling cycle and interpreting behaviours. In R. Lesh, P. L. Galbraith, C. R. Haines, & A. Hurford (Eds.), Modeling students’ mathematical modeling competencies: ICTMA 13 (pp. 145–154). Springer. https://doi.org/10.1007/978-1-4419-0561-1

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed). Prentice Hall.

Hallström, J., & Schönborn, K. J. (2019). Models and modelling for authentic STEM education: Reinforcing the argument. International Journal of STEM Education, 6(1), 1-10. https://doi.org/10.1186/s40594-019-0178-z

Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408-420. https://doi.org/10.1080/03637750903310360

Hidayat, R., & Iksan, Z. H. (2018). Mathematical modelling competency for Indonesian students in mathematics education programmes. Creative Education, 9(15), 2483-2490. https://doi.org/10.4236/ce.2018.915187

Hidayat, R., Qudratuddarsi, H., Mazlan, N. H., & Mohd Zeki, M. Z. (2021). Evaluation of a test measuring mathematical modelling competency for Indonesian college students. Journal of Nusantara Studies, 6(2), 133-155. https://doi.org/10.24200/jonus.vol6iss2pp133-155

Hidayat, R., Zulnaidi, H., & Syed Zamri, S. N. A. (2018). Roles of metacognition and achievement goals in mathematical modeling competency: A structural equation modeling analysis. PloS one, 13(11), 1-25. https://doi.org/10.1371/journal.pone.0206211

Hidayat, R., Zamri, S. N. A. S., Zulnaidi, H., & Yuanita, P. (2020). Meta-cognitive behaviour and mathematical modelling competency: Mediating effect of performance goals. Heliyon, 6(4), 1-10. https://doi.org/10.1016/j.heliyon.2020.e03800

Hong, W. H., Vadivelu, J., Daniel, E. G. S., & Sim, J. H. (2015). Thinking about thinking: Changes in first-year medical students’ metacognition and its relation to performance. Medical education online, 20(1), 1-6 https://doi.org/10.3402/meo.v20.27561

Huang, C. H. (2018). Investigating engineering students’ mathematical modeling competency. American Journal of Educational Research, 6(12), 1668-1672. https://doi.org/10.12691/education-6-12-12

Kaiser, G. (2007). Modelling and modelling competencies in school. In C. Haines, P. Galbraith, W. Blum, & S. Khan (Eds.), Mathematical modelling ICTMA 12: Education, engineering and economics (pp. 110–119). Horwood.

Kaiser, G., & Schwarz, B. (2006). Mathematical modelling as bridge between school and university. International Reviews on Mathematical Education/ Zentralblatt für Didaktik der Mathematik, 38(2), 196–208. https://doi.org/10.1007/BF02655889

Kaiser, G., & Stender, P. (2013). Complex modelling problems in co-operative, self-directed learning environments. In G. A. Stillman, G. Kaiser, W. Blum, & J. P. Brown (Eds.), International perspectives on the teaching and learning of mathematical modelling (pp. 277–293). Springer. https://doi.org/10.1007/978-94-007-6540-5

Kramarski, B., Mevarech, Z., & Arami, M. (2002). The effects of metacognitive instruction on solving mathematical authentic tasks. Educational Studies in Mathematics, 49, 225–250. https://doi.org/10.1023/A:1016282811724

Kwok, O. M., Cheung, M. W., Jak, S., Ryu, E., & Wu, J. Y. (2018). Editorial: Recent advancements in structural equation modeling (SEM): From both methodological and application perspectives. Frontiers in Psychology, 9, 1-3. https://doi.org/10.3389/fpsyg.2018.01936

Lesh, R., & Doerr, H. M. (2003). Foundation of a models and modeling perspective on mathematics teaching, learning, and problem solving. In R. Lesh & H. M. Doerr (Eds.), Beyond constructivism. Models and modeling perspectives on mathematics problem solving, learning, and teaching (pp. 3–33). Lawrence Erlbaum Associates Publishers.

Lesh, R., & Zawojewski, J. (2007). Problem solving and modeling. In J. F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 763–804). Information Age Publishing.

Lingefjärd, T. (2011). Modelling from primary to upper secondary school: Findings of empirical research–Overview. In G. Kaiser, W. Blum, R. Borromeo, & G. Stillman (Eds.), International perspectives on the teaching and learning of mathematical modelling (pp. 9–14). Springer.             https://doi.org/10.1007/978-94-007-0910-2_2   

Livingston, J. A. (2003). Metacognition: An overview. ERIC. https://files.eric.ed.gov/fulltext/ED474273.pdf

Maaß, K. (2006). What are modelling competencies? International Reviews on Mathematical Education/ Zentralblatt für Didaktik der Mathematik, 38(2), 113–142. https://doi.org/10.1007/BF02655885

Mohamad, M., Mohammad, M., Mat Ali, N. A., & Awang, Z. (2018). The impact of life satisfaction on substance abuse: Delinquency as a mediator. International Journal of Adolescence and Youth, 23(1); 25-35. https://doi.org/10.1080/02673843.2016.1267021

Mohamed, Z., & Rosli, R. (2014). Development of a structural model with multicollinearity and outliers’ problems. EDUCATUM Journal of Science, Mathematics and Technology, 1(1), 38-52.

O’Neil, H. F., & Abedi, J. (1996). Reliability and validity of a state metacognitive inventory: Potential for alternative assessment. The Journal of Educational Research, 89(4), 234–245. https://doi.org/10.1080/00220671.1996.9941208

Papaleontiou-Louca, E. (2008). Metacognition and theory of mind. Cambridge Scholars Publishing.

Qin, Y., Zhang, R., Yuan, B., Xu, T., Chen, H., Yang, Y., & Hu, T. (2019). Structural equation modelling for associated factors with dental caries among 3–5-year-old children: A cross-sectional study. BMC oral health, 19(1), 1-12. https://doi.org/10.1186/s12903-019-0787-4

Rasmussen, C., Zandieh, M., King, K., & Teppo, A. (2005). Advancing mathematical activity: A practice-oriented view of advanced mathematical thinking. Mathematical             Thinking and Learning, 7(1), 51–73. https://doi.org/10.1207/s15327833mtl0701

Rellensmann, J., Schukajlow, S., & Leopold, C. (2020). Measuring and investigating strategic knowledge about drawing to solve geometry modelling problems. International Reviews on Mathematical Education/ Zentralblatt für Didaktik der Mathematik, 52(1), 97-110. https://doi.org/10.1007/s11858-019-01085-1

Sawuwu, B. Y., Partana, C. F., & Hairida. (2018). Exploration of procedural knowledge of bilingual students in chemical equilibrium problem solving. EDUCATUM Journal of Science, Mathematics and Technology, 5(1), 14-23. https://doi.org/10.37134/ejsmt.vol5.1.3.2018

Schaap, S., Vos, P., & Goedhart, M. (2011). Trends in teaching and learning of mathematical modelling. In G. Kaiser, W. Blum, R. Borromeo, & G. Stillman (Eds.), International perspectives on the teaching and learning of mathematical modelling (pp. 137–146). Springer. https://doi.org/10.1007/978-94-007-0910-2

Schoenfeld, A. H. (1983). Beyond the purely cognitive: Belief systems, social cognitions, and metacognitions as driving forces in intellectual performance. Cognitive Science, 7(4), 329–363. https://doi.org/10.1016/S0364-0213(83)80003-2

Schraw, G., & Moshman, D. (1995). Metacognitive theories. Educational Psychology Review, 7(4), 351–371. https://doi.org/10.1007/BF02212307

Schukajlow, S., Kaiser, G., & Stillman, G. (2018). Empirical research on teaching and learning of mathematical modelling: A survey on the current state-of-the-art. International Reviews on Mathematical Education/ Zentralblatt für Didaktik der Mathematik, 50(1), 5-18. https://doi.org/10.1007/s11858-018-0933-5

Sekerak, J. (2010). Competences of mathematical modelling of high school students. Mathematics Teaching, (220), 8–13.

Sharma, A. (2013). Associations between self-efficacy beliefs, self-regulated learning strategies, and students’ performance on model-eliciting tasks: An examination of direct and indirect effects (Publication No.1520794387) [Doctoral dissertation, University of Florida]. ProQuest Dissertations and Theses Global.

Stillman, G. (2011). Applying metacognitive knowledge and strategies in applications and modelling tasks at secondary school. In G. Kaiser, R. B. Ferri, W. Blum, & G. Stillman (Eds.), International perspectives on the teaching and learning of mathematical modelling (pp. 172–187). Springer. https://doi.org/10.1007/978-94-007-0910-2_18

Stillman, G. A., & Galbraith, P. L. (1998). Applying mathematics with real world            connections: Metacognitive characteristics of secondary students. Educational Studies in Mathematics, 36(2), 157–194. https://doi.org/10.1023/A:1003246329257

Stillman, G., Galbraith, P., Brown, J., & Edwards, I. (2007). A Framework for success in implementing mathematical modelling in the secondary classroom. In J. Watson & K. Beswick (Eds.), Proceedings of the 30th annual conference of the mathematics education research group of Australasia (pp. 688–697). MERGA Inc

Tavakol, M., & Dennick, R. 2011. Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd

Tian, Y., Fang, Y., & Li, J. (2018). The effect of metacognitive knowledge on mathematics performance in self-regulated learning framework-multiple mediation of self-efficacy and motivation. Frontiers in psychology, 9, 1-11. https://doi.org/10.3389/fpsyg.2018.02518

Tjalla, A., & Putriyani, M. F. (2018). Mathematics metacognitive skills of Papua’s students in solving mathematics problems. Asian Social Science, 14(7), 14-25. https://doi.org/10.5539/ass.v14n7p14

Tran, D., & Dougherty, B. J. (2014). Authenticity of mathematical modeling. The Mathematics Teacher, 107(9), 672-678. https://doi.org/10.5951/mathteacher.107.9.0672

Treffers, A. (1978). Three Dimensions. A model of goal and theory description in mathematics instruction–the Wiskobas project. D. Reidel Publishing Company.

Treffers, A., & Goffree, F. (1985). Rational analysis of realistic mathematics education: The Wiskobas program. In L. Streefland (Ed.), Proceedings of the ninth annual conference of the international group for the psychology of mathematics education (pp. 97–121). OW&OC.

Van den Heuvel-Panhuizen, M., & Drijvers, P. (2014). Realistic mathematics education. In S. Lerman (Ed.), Encyclopedia of mathematics education (pp. 521–525). Springer. https://doi.org/10.1007/978-94-007-4978-8_170

Veenman, M. V. J., Van Hout-Wolters, B. H. A. M., & Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations. Metacognition and Learning, 1(1), 3–14. https://doi.org/10.1007/s11409-006-6893-0

Verschaffel, L. (2002). Taking the modeling perspective seriously at the elementary school level: Promises and pitfalls. In A. D. Cockbum & E. Nardi (Eds.), Proceedings of the 26th annual meeting of the international group for the psychology of mathematics education (pp. 64–80). University of East Anglia.

Vorhölter, K. (2018). Conceptualization and measuring of metacognitive modelling competencies: Empirical verification of theoretical assumptions. International Reviews on Mathematical Education/ Zentralblatt für Didaktik der Mathematik, 50(1), 343-354. https://doi.org/10.1007/s11858-017-0909-x

Vorhölter, K. (2019). Enhancing metacognitive group strategies for modelling International Reviews on Mathematical Education/ Zentralblatt für Didaktik der Mathematik, 51(4), 703-716. https://doi.org/10.1007/s11858-019-01055-7

Vos, P. (2018). “How real people really need mathematics in the real world”-Authenticity in mathematics education. Education Sciences, 8(4), 1-14. https://doi.org/10.3390/educsci8040195

Wendt, L., Vorhölter, K., & Kaiser, G. (2020). Teachers’ perspectives on students’ metacognitive strategies during mathematical modelling processes–A case study. In Mathematical Modelling Education and Sense-making (pp. 335-346). Springer. https://doi.org/10.1007/978-3-030-37673-4_29

Widjaja, W. (2013). Building awareness of mathematical modelling in teacher education:         A case study in Indonesia. In G. A. Stillman, W. Blum, G. Kaiser, & J. P. Brown (Eds.), Teaching mathematical modelling: Connecting to research and practice (pp. 583–593). Springer. https://doi.org/10.1007/978-94-007-6540-5

Wilson, J., & Clarke, D. (2002). Monitoring mathematical metacognition Jeni Wilson department of learning and educational development and David Clarke Department of science and mathematics education faculty of education, University of Melbourne, Australia. Paper Presented at the American Education Research Association Conference. New Orleans.

Yildirim, T. P. (2010). Understanding the modeling skill shift in engineering: The impact of self-efficacy, epistemology, and metacognition (Publication No.858073953) [Doctoral dissertation, University of Pittsburgh]. ProQuest Dissertations and Theses Global.

Zimmerman, B. J., & Campillo, M. (2003). Motivating self-regulated problem solvers. In J. E. Davidson & R. J. Sternberg (Eds.), The psychology of problem solving (pp. 233–262). Cambridge University Press. https://doi.org/10.1017/CBO9780511615771.009