Journal of Engineering Education Transformations

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Configural Measurement Equivalence Testing of the Comprehensive Trait Based Model of Self-Regulated Learning in Engineering Undergraduates


Affiliations
1 School of Education, Lovely Professional University, Phagwara, Punjab, India
     

   Subscribe/Renew Journal


The present study extended the integrative trait model of self regulated learning by [30] by including the remaining behavioral and emotional components. Measurement invariance testing was conducted to check for the equivalence of the model with respect to gender, stream and batch among 488 (351 male and 137 female; 321 Computer Science and 167 Mechanical; 263 IInd Year and 225 IIIrd Year) engineering students selected through stratified random sampling from the three regions, Majha, Doaba and Malwa, of the Punjab state of India. [25] measurement invariance criterion of ∆CFI be less than or equal to 0.01 was used to test for equivalence of the model across the selected groups, since it is unaffected by model complexity, sample size and unrelated to overall fit measures. The revised integrative trait model of self regulated learning among engineering undergraduates was found to be Configural measurement invariant with respect to gender, stream and batch using SPSS Amos Ver. 23.0, meaning that the construct of self regulated learning is conceptualized by the participants across the groups of the study in same way. Reliability of the instrument's 14 variables was estimated using Pearson's correlation-based Cronbach's alpha (using SPSS Statistics Ver. 23.0) and Polychoric correlation based ordinal alpha (using R Package Psych), along with estimation of attenuation index to show the extent of underestimation of the vital psychometric property by assuming the data of Likert scale based questionnaires as continuous interval and on ignoring its categorical ordinal nature. The effect size of the validated model conducted as part of post-hoc power analysis using semPower R package was found to be satisfactorily high at 0.941. The academic implications of the study with respect to engineering education in the country are discussed.

Keywords

Attenuation Index, Configural Measurement Invariance Testing, Engineering Education, Polychoric Ordinal Reliability, Sophomore Slump, Revised Integrative Trait Model of Self Regulated Learning.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Alotaibi,K., Tohmaz,R. & Jabak.O. (2017). The Relationship Between Self-Regulated Learning and Academic Achievement for a Sample of Community College Students at King Saud University. Education Journal., 6(1), pp. 28-37. doi:10.11648/j.edu.20170601.14

  • All India Survey on Higher Education AISHE 2018-19, (2019). Ministry of Human Resource Development, Department of Higher Education, Government of India.

  • Buric,.I, Sonic,.I., & Penezic,.Z. (2016). Emotion regulation in academic domain: Development and validation of academic emotion regulation questionnaire (AERQ), Personality and Individual Differences, 96, pp: 138-147, dx.doi.org/10.1016/j.paid.2016.02.074.

  • Berry, D.A. & Lindgren, B.W. 1990. Statistics. Brooks/Cole Publishing Co., Pacific Grove, Calif.

  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

  • Brannick, M. T. (1995). Critical comments on applying covariance structure modeling. Journal of Organizational Behavior, 16, 201–213.3

  • Bembenutty, H., & Karabenick, S. A. (2004). Inherent association between academic delay of gratification, future time perspective, and self-regulated learning. Educational Psychology Review, 16 (1), 35-57. doi:10.1023/B:EDPR.0000012344.34008.5c.

  • Bembenutty, H., & Karabenick, S. A. (1998). Academic delay of gratification. Learning and Individual Differences, 10(4), 329-346. doi:10.1016/S1041-6080(99)80126-5

  • Boekaerts, M. (1999). Self-regulated learning: Where we are today. International Journal of Educational Research, 31(6), 445-457. doi:10.1016/S0883-0355(99)00014-2.

  • Boekaerts, M. (1999). Motivated learning: studying student situation transactional units. Eur. J. Psycholo. Educ. 14, 41–55. doi: 10.1007/bf03173110.

  • Ben-Eliyahu, A. & Linnenrinck-Garcia, .L. (2013). Extending self regulated learning to include self regulated emotional strategies, Motiv Emot, 37(1), pp: 558–573, DOI 10.1007/s11031-012-9332-3.

  • Babbie, E. (1990). Survey Research Methods, (2nd ed.) Belmont, CA: Wadsworth.

  • Biggs, J. (1993). What do inventories of students' learning processes really measure? A theoretical review and clarification. Br. J. Educ. Psychol. 63: 3–19.

  • Balu, C. (2019). "Engineering education in India: an overview" Library Philosophy and Practice (ejournal). 2791. https://digitalcommons.unl.edu/libphilprac/2791

  • Capote, G., Rizo, N., & Bravo, G. (2017). La autorregulación del aprendizaje en estudiantes de la carrera ingeniería industrial. Universidad y Sociedad, 9(2), 44-52.

  • Corno, L. (2001). Volitional aspects of self-regulated learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement. Theoretical perspectives (pp. 191-226). Mahwah, NJ: Erlbaum.

  • Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd Edition). Hillsdale, NJ: Lawrence Earlbaum Associates.

  • Chasmar, J.M., Melloy, B.J. & Benson, L.B. (2015). Use of Self- Regulated Learning Strategies by Second-Year Industrial Engineering Students, Paper presented at the 122nd ASEE Annual Conference and Exposition, Seattle, WA

  • Cazan, A. (2013). Teaching self regulated learning strategies for psychology students, Procedia – Social and Behavioral Sciences, 78, pp:743-747.

  • Cazan, A. (2012a). Self regulated learning strategies – predictors of academic adjustment, Procedia – Social and Behavioral Sciences, 33, pp:104-108.

  • Cazan, A. (2012b). Enhancing self regulated learning strategies by learning journals, Procedia – Social and Behavioral Sciences, 33, pp:413-417.

  • Cazan, A. & Anitei, M. (2010). Motivation, learning strategies and academic adjustment, Romanian Journal of Experimental Applied Psychology, 1 (1).

  • Carstensen, L. L., & Lang, F. R. (1996). Future Orientation Scale. Unpublished manuscript, Stanford University.

  • Chakraborty, R. & Chechi, V.K. (2021). Measurement Invariance Testing of the Revised Integrative Trait Model of Self- regulated Learning Among Engineering Undergraduates, Ph.D. Thesis retrieved from Shodhganga, https://shodhganga.inflibnet.ac.in/handle/10603/344499.

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

  • Dignath, C., Buettner, G., & Langfeldt, H. P. (2008). How can primary school students learn self-regulated learning strategies most effectively?: A meta-analysis on self- regulation training programmes. Educational Research Review, 3 (2), 101-129. doi:10.1016/j.edurev.2008.02.00

  • Duckworth, A. L., & Seligman, M. E. (2006). Self-discipline gives girls the edge: Gender in self-discipline, grades, and achievement test scores. Journal of Educational Psychology, 98(1), 198. doi:10.1037/0022-0663.98.1.198

  • Dyne, A., Taylor, P., & Boulton-Lewis, G. (1994). Information processing and the learning context: An analysis from recent perspectives in cognitive psychology. Br. J. Educ. Psychol. 64: 359–372.

  • Deci, E.L. & Ryan, R.M., 1985. Intrinsic Motivation and Self Determination in Human Behavior Security in Wireless Ad Hoc Networks, New York, Plenum Press.

  • Dorrenbacher,.L. & Perels, .F. (2015). Volition completes the puzzle: Development and evaluation of an integrative trait model of self regulated learning, Frontline Learning Research, 3(4), pp:14-36, DOI:http://dx.doi.org/10.14786/flr.v3i4.179

  • Entwistle, N., & Waterston, S. (1988). Approaches to studying and levels of processing in university students. Br. J. Educ. Psychol. 58: 258–265.

  • Fink, A. (2002). The Survey Kit (2nd Ed.) Thousand Oaks, CA: Sage.

  • Gross, J. J. (1999). Emotion and emotion regulation. In L. A. Pervin & O. P. John (Eds.), Handbook of personality: Theory and research (2nd ed., pp. 525–552). New York, NY, US: The Guilford Press.

  • Gardner, P. D. (2000) Visible solutions for invisible students: Helping sophomores succeed 67–77.

  • Graunke, S. S., & Woosley, S. A., (2005). "An exploration of the factors that affect the academic success of college sophomores." College Student Journal, vol. 39, no. 2.

  • Goukh, M.E. (2011). Engineering Economy (3rd ed.) Khartoum, Sudan: University of Khartoum Press.

  • Hong, E. (1995). A structural comparison between state and trait self/regulation models. Applied Cognitive Psychology, 9(4), 333-349.!doi:10.1002/acp.2350090406

  • Hong, E. & O'Neil Jr, H. F. (2001). Construct validation of a trait self-regulation model. International Journal of Psychology, 36(3), 186-194. doi:10.1080/00207590042000146.

  • Hinton, P.R., Brownlow, C., McMurray, I. & Cozens, B. (2004). SPSS Explained, Routledge, Taylor and Francis Group, London and New York.

  • India Skill Report (2019). Confederation of Indian Industry (CII), Association of Indian Universities (AIU), All India Council for Technical Education (AICTE).

  • Jackson, C. (2018). Validating and Adapting the Motivated Strategies for Learning Questionnaire (MSLQ) for STEM Courses at an HBCU, AERA Open, 4 (4), pp: 1-16, DOI: 10.1177/2332858418809346.

  • Kitsantas, A., Winsler, A., & Huie, F. (2008). Self-regulation and ability predictors of academic success during college: A predictive validity study. Journal of Advanced Academics, 20(1), 42-68.doi:10.4219/jaa-2008-867.

  • Kelloway, E. K. (1995). Structural equation mode lling in pe rspe c tive . Journa l of Organizational Behavior, 16, 215–224.

  • Kovacs, M., van Ravenzwaaij, D., Hoekstra, R., & Aczel, B. (2022). SampleSizePlanner: a tool to estimate and justify sample size for two-group studies. Adv. Meth. Pract. Psychol. Sci. 5, 25152459211054059.10.1177/25152459211054059

  • Levine, J. & Wyckoff, J. (1990). Identification of Student Characteristics that Predict Persistence and Success in an Engineering College.

  • Lakens D. (2022). Sample Size Justification. Collabra: Psychol. 8: 33267.10.1525/collabra.33267

  • LeMay, J.O. IV, (2017). "Academic Engagement, Motivation, Self-Regulation, and Achievement of Georgia Southern University Sophomore Students", Electronic Theses and Dissertations.1666.https://digitalcommons.georgiasouthern.edu/etd/1666.

  • Mischel, .W.,(1981). Metacognition and rules of delay of gratification, In J. H.Flabell & L. Ross (Eds.),Social cognitive development: Frontiers and possible futures, NY: Cambridge University Press.

  • McCord,.R. (2016). The impact of teaching self regulated learning skills to first year engineering students, Paper presented at ASEE's 123rd Annual Conference and Exposition, New Orleans, Los Angeles, June 26-29, Paper ID: 16283.

  • McBurnie, J. E., Campbell, M., & West, J. M. (2012).Avoiding the second year slump: A transition framework for students progressing through university. International Journal of Innovation in Science and Mathematics Education, 20(2), 14-24.

  • Moshagen, M., & Erdfelder, E. (2016). A new strategy for testing structural equation models. Structural Equation Modeling, 23, 54-60. doi: 10.1080/10705511.2014.950896

  • Mylliem, H. & Chechi, V.K., (2023). Relationship of Self - Regulated Learning on Engineering Competency : Mediating role of Self Efficacy, Unpublished Master of Education Dissertation, Lovely Professional University, Punjab, India

  • Maier M., Lakens D. (2022). Justify Your Alpha: A Primer on Two Practical Approaches. Adv. Meth. Pract. Psychol. Sci. 5, 25152459221080396.10.1177/25152459221080396

  • Nackerud, S. (2013). Collaborative Statistics Using Spreadsheets - snackeru. OpenStax CNX.

  • Nunnally, J.C. (1967). Psychometric Theory, New York: McGraw-Hill Book Company.

  • Nomura, O.; Soma, Y.; Kijima, H.; Matsuyama, Y. (2023). Adapting the Motivated Strategies for Learning Questionnaire to the Japanese Problem-Based Learning Context: A Validation Study. Children, 10, 154. https://doi.org/10.3390/children10010154.

  • Nesbary, D.K. (2000). Survey research and the world wide web, Boston: Allyn and Bacon.

  • Nelson, K. G., Shell, D.F., Husman, J., Fishman, E.J., & Soh, L. (2015). Motivational and self-regulated learning profiles of students taking a foundational engineering courses, Journal of Engineering Education, 104(1), pp:74-100, DOI 10.1002/jee.20066.

  • National Employability Report-Engineers (2016) Aspiring Minds.

  • Orosz, G., Dombi, E., Toth-Kiraly, I. & Roland-Levy, C. (2017). The Less is More: The 17-item Zimbardo Time Perspective Inventory, Current Psychology, 36 (1), pp: 39-47, doi: 10.1007/s12144-015-9382-2.

  • Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). Amanual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor: University of Michigan, National Center for Research to Improve Postsecondary Teaching and Learning.

  • Pintrich, P. R. (2000b). The role of goal orientation in self- regulated learning. In Boekaerts, M., Pintrich, P. R., and Zeidner, M. (eds.), Handbook of Self-Regulation, Academic Press, San Diego, CA, pp. 451–502.

  • Panadero,.E., (2017) AReview of Self-regulated Learning: Six Models and Four Directions for Research. Front. Psychol. 8:422. doi: 10.3389/fpsyg.2017.00422.

  • Schmitz, B., & Wiese, B. S. (2006). New perspectives for the evaluation of training sessions in self-regulated learning: Time-series analyses of diary data. Contemporary Educational Psychology,31, 64–96.

  • Sirois, F. M. (2014). Out of sight, out of time? A meta/analytic investigation of procrastination and time perspective. European Journal of Personality, 28 (5), 511-520. doi:10.1002/per.1947.

  • Sanchez-Leguelinel, C. (2008). Supporting “Slumping” Sophomores: Programmatic Peer Initiatives Designed to Enhance Retention in the Crucial Second Year of College. Coll. Stud. J., 42, 637–646.

  • Sue, V.M., & Ritter, L.A. (2007). Conducting Online Surveys.Thousand Oaks, CA: Sage.

  • Steel, P. D. G. & König, C. J. (2006). Integrating theories of motivation. Academy of Management Review, 31(4), 889-913.

  • Soper, D.S. (2018). A-priori Sample Size Calculator for Structural Equation Models [Software]. Available from http://www.danielsoper.com/statcalc.

  • Sahu, A.R., Shrivastava, R.R. & Shrivastava, R.L. (2013), “Critical success factors for sustainable improvement in technical education excellence: A literature review”, The TQM Journal, 25(1), pp. 62–74.

  • Saez, F., Mella, J., Loyer, S., Zambrano, C., & Zanartu, N. (2020). Self-regulated learning in engineering students: A systematic review, Espacious, 41(2), pp: 7-21, ISSN 0798 1015.

  • Sasaki K, Yamada Y. (2023). SPARKing: Sample-size planning after the results are known. Front Hum Neurosci. 22;17:912338. doi: 10.3389/fnhum.2023.912338. PMID: 36908711; PMCID: PMC9992160.

  • Tobolowsky, B. F. (2008).Sophomores in transition: The forgotten year. In B. Barefoot & J. L. Kinzie (Eds.), New directions for higher education (pp. 59-67). New York, NY: Wiley Online Library.

  • Turner, A.G., (2003). Sampling Frames and Master Samples, United Nations Secretariat, Statistics Devision.

  • Vallerand, R.J., Pelletier, L.G., Blais, M.R., Briere, N.M., Senécal, C., &Vallierès, E.F.. (1992). The Academic Motivation Scale: a measure of intrinsic, extrinsic, and amotivation in education. Educ Psychol Meas. 52: pp. 1003–1017.

  • Wolters, C. A., & Benzon, M. B. (2013). Assessing and predicting college students' use of strategies for the self- regulation of motivation. The Journal of Experimental Education, 81(2), 199-221. doi:10.1080/00220973.2012.699901.

  • Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Ed s.), Metacognition in educational theory and practice (pp. 277–304). Mahwah, NJ US: Lawrence Erlbaum Associates Publishers.

  • Westland, J.C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476-487.

  • Washington Accord (1989). International Engineering Alliance, https://www.Ieagreements.org/assets/Uploads/Docum ents/History/25Years WashingtonAccordA5booklet-FINAL.pdf

  • Wolf, E.J., Harrington, K.M., Clark, S.L. & Miller, M.W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias and solution propriety, Educ Psychol Meas., 76 (6): 913-934.doi:10.1177/0013164413495237.

  • Yockey, R.D., (2016). Validation of Short Form of Academic Procrastination Scale, Psychological Reports, 118(1), pp:171-179.

  • Zimmerman, B. J. (1986). Becoming a self-regulated learner: which are the key subprocesses? Contemp. Educ. Psychol. 11, 307–313. doi: 10.1016/0361- 476x(86)90027-5

  • Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70.

  • Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45 (1), 166–183. doi:10.3102/0002831207312909.

  • Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17.

  • Zimmerman, B. J. (2011). Motivational sources and outcomes of self-regulated learning and performance. In B. J. Zimmerman & D. H. Schunk. (Eds.), Handbook of self- regulation of learning and performance (pp. 49-64). New York, NY: Routledge.

  • Zimmerman, B. J. (2013). From cognitive modeling to self- regulation: A social cognitive career path. Educational Psychologist, 48(3), 135–147.

  • Zimmerman, B. J. (2000a). Attaining self-regulation: A social cognitive perspective. In Boekaerts, M., Pintrich, P. R., and Zeidner, M. (eds.), Handbook of Self-Regulation: Theory, Research, and Applications, Academic Press, San Diego, CA, pp. 13–39.


Abstract Views: 83

PDF Views: 0




  • Configural Measurement Equivalence Testing of the Comprehensive Trait Based Model of Self-Regulated Learning in Engineering Undergraduates

Abstract Views: 83  |  PDF Views: 0

Authors

R. Chakraborty
School of Education, Lovely Professional University, Phagwara, Punjab, India
V. K. Chechi
School of Education, Lovely Professional University, Phagwara, Punjab, India

Abstract


The present study extended the integrative trait model of self regulated learning by [30] by including the remaining behavioral and emotional components. Measurement invariance testing was conducted to check for the equivalence of the model with respect to gender, stream and batch among 488 (351 male and 137 female; 321 Computer Science and 167 Mechanical; 263 IInd Year and 225 IIIrd Year) engineering students selected through stratified random sampling from the three regions, Majha, Doaba and Malwa, of the Punjab state of India. [25] measurement invariance criterion of ∆CFI be less than or equal to 0.01 was used to test for equivalence of the model across the selected groups, since it is unaffected by model complexity, sample size and unrelated to overall fit measures. The revised integrative trait model of self regulated learning among engineering undergraduates was found to be Configural measurement invariant with respect to gender, stream and batch using SPSS Amos Ver. 23.0, meaning that the construct of self regulated learning is conceptualized by the participants across the groups of the study in same way. Reliability of the instrument's 14 variables was estimated using Pearson's correlation-based Cronbach's alpha (using SPSS Statistics Ver. 23.0) and Polychoric correlation based ordinal alpha (using R Package Psych), along with estimation of attenuation index to show the extent of underestimation of the vital psychometric property by assuming the data of Likert scale based questionnaires as continuous interval and on ignoring its categorical ordinal nature. The effect size of the validated model conducted as part of post-hoc power analysis using semPower R package was found to be satisfactorily high at 0.941. The academic implications of the study with respect to engineering education in the country are discussed.

Keywords


Attenuation Index, Configural Measurement Invariance Testing, Engineering Education, Polychoric Ordinal Reliability, Sophomore Slump, Revised Integrative Trait Model of Self Regulated Learning.

References