Pengembangan Instrumen Efikasi Diri dalam Matematika: Studi Validasi dengan Analisis Faktor Eksploratori

Nurul Mukhlisah Abdal(1*), Dwi Rezky Anandari Sulaiman(2), Wirawan Setialaksana(3),

(1) Universitas Negeri Makassar
(2) Universitas Negeri Makassar
(3) Universitas Negeri Makassar
(*) Corresponding Author




DOI: https://doi.org/10.26858/jmtik.v6i2.47007

Abstract


Salah satu faktor afektif yang dapat mempengaruhi proses belajar siswa adalah efikasi diri. Efikasi diri berpengaruh ketika siswa melakukan proses investigasi yang tercermin dari tindakan, usaha, kegigihan, fleksibilitas dalam perbedaan, dan realisasi tujuan. Efikasi diri matematika mencakup interpretasi mahasiswa terhadap pencapaian mereka sebelumnya, penilaian diri terhadap kemampuan mereka, dan estimasi pribadi terhadap kinerja selanjutnya pada tugas matematika yang diberikan. Terlepas dari relevansi efikasi diri matematika dengan minat dan motivasi siswa untuk menyelesaikan studi secara profesional, sebagian besar penelitian yang menganalisis efikasi diri siswa dalam matematika menggunakan instrumen yang tidak dapat diandalkan atau kurang disesuaikan dengan konteks mahasiswa dan kebahasaan. Penelitian sebelumnya dalam literatur efikasi diri matematis kemudian menyesuaikannya dengan konteks dan bahasa yang baru, yaitu Bahasa Arab, Bahasa Spanyol, dan Bahasa Mexico. Penelitian ini yang akan menjadi pelopor instrumen efikasi diri dalam matematika dengan menggunakan Bahasa Indonesia Betz dan Hackett. Partisipan dalam penelitian ini adalah 620 oarng mahasiswa fakultas teknik tahun pertama hingga tahun ketiga. Survei dalam bentuk digital dikirimkan kepada mahasiswa melalui ketua tingkat masing-masing. Hasil penelitian ini menunjukkan bahwa kuesioner Source-of Self-Efficacy in Mathematics (SOSEM-24) menunjukkan sifat psikometri yang baik. Perbedaan mendasar dari hasil uji coba SOSEM-24 di mahasiswa Indonesia berdasarkan EFA yang dilakukan adalah faktor-faktor sumber efikasi diri dalam matematika menjadi lebih sedikit dalam konteks Indonesia.

Keywords


Instrumen, Efikasi Diri Matematika, Analisis Faktor Eksploratori

Full Text:

PDF

References


H. R. P. Negara, E. Nurlaelah, Wahyudin, T. Herman, and M. Tamur, “Mathematics self efficacy and mathematics performance in online learning,” J. Phys.: Conf. Ser., vol. 1882, no. 1, p. 012050, May 2021, doi: 10.1088/1742-6596/1882/1/012050.

A. Bandura, Social foundations of thought and action: a social cognitive theory. in Prentice-Hall series in social learning theory. Englewood Cliffs, N.J: Prentice-Hall, 1986.

G. Hackett and N. E. Betz, “An Exploration of the Mathematics Self-Efficacy/Mathematics Performance Correspondence,” JRME, vol. 20, no. 3, pp. 261–273, May 1989, doi: 10.5951/jresematheduc.20.3.0261.

H. T. Rowan-Kenyon, A. K. Swan, and M. F. Creager, “Social Cognitive Factors, Support, and Engagement: Early Adolescents’ Math Interests as Precursors to Choice of Career,” The Career Development Quarterly, vol. 60, no. 1, pp. 2–15, Mar. 2012, doi: 10.1002/j.2161-0045.2012.00001.x.

M. Pertiwi, S. Suhendra, and D. Juandi, “Mathematical Literacy Ability of Junior High School Students in Terms of Self-Efficacy,” supremum j of mathematics education, vol. 6, no. 2, pp. 171–180, Jul. 2022, doi: 10.35706/sjme.v6i2.6547.

N. S. Hamizah Amiruddin, N. Ahmad, and S

. S. Mamat, “Exploring Students’ Self-Efficacy and Anxiety Towards Mathematics Problem Solving During Open and Distance Learning (ODL),” MIJ, vol. 3, no. 1, pp. 39–55, May 2022, doi: 10.24191/mij.v3i1.18265.

V. Chytrý, J. Medová, J. Říčan, and J. Škoda, “Relation between Pupils’ Mathematical Self-Efficacy and Mathematical Problem Solving in the Context of the Teachers’ Preferred Pedagogies,” Sustainability, vol. 12, no. 23, p. 10215, Dec. 2020, doi: 10.3390/su122310215.

A. Bandura, Self-efficacy: the exercise of control. New York: W.H. Freeman, 1997.

D. Risalah and H. Hodiyanto, “Mathematics communication as an alternative to overcome the obstacles of undergraduate students in mathematical proof,” Int.J.Trends.Math.Edu.Research, vol. 5, no. 2, pp. 125–132, Jun. 2022, doi: 10.33122/ijtmer.v5i2.141.

Ma. M. L. Moreno, T. J. C. Gaspar, J. M. R. Torres, J. L. Agapito, and M. M. T. Rodrigo, “Factors Affecting Student Self-Efficacy during Emergency Remote Teaching,” in 2022 the 4th International Conference on Modern Educational Technology (ICMET), Macau China: ACM, May 2022, pp. 73–79. doi: 10.1145/3543407.3543420.

I. Çankaya and M. Dağ, “Comparison of Academic Achievement Levels of Students Beginning the Elementary School at Different Ages,” JEP, vol. 8, no. 3, pp. 140–143, 2017.

A. Garrote, “Academic Achievement and Social Interactions: A Longitudinal Analysis of Peer Selection Processes in Inclusive Elementary Classrooms,” Front. Educ., vol. 5, p. 4, Feb. 2020, doi: 10.3389/feduc.2020.00004.

R. Sheldrake, “Confidence as motivational expressions of interest, utility, and other influences: Exploring under-confidence and over-confidence in science students at secondary school,” International Journal of Educational Research, vol. 76, pp. 50–65, 2016, doi: 10.1016/j.ijer.2015.12.001.

F. Pfitzner-Eden, “Why Do I Feel More Confident? Bandura’s Sources Predict Preservice Teachers’ Latent Changes in Teacher Self-Efficacy,” Front. Psychol., vol. 7, Oct. 2016, doi: 10.3389/fpsyg.2016.01486.

Y. F. Zakariya, “Improving students’ mathematics self-efficacy: A systematic review of intervention studies,” Front. Psychol., vol. 13, p. 986622, Sep. 2022, doi: 10.3389/fpsyg.2022.986622.

S. A. Brown and J. Burnham, “Engineering Students Mathematics Self-Efficacy Development in a Freshmen Engineering Mathematics Course,” International Journal of Engineering Education, vol. 28, no. 1, pp. 113–129, 2012.

J. S. Briley, “The relationships among mathematics teaching efficacy, mathematics self-efficacy, and mathematical beliefs for elementary pre-service teachers,” IUMPST: The Journal, vol. 5, pp. 1–13, Aug. 2012.

D. K. May, “Mathematics Self-Efficacy and Anxiety Questionnaire,” Doctoral, The University of Georgia, USA, 2009.

A. Al Jabri, S. Gangadharan, and G. Bendanillo, “Mathematics Education among Higher Education Students: Analysis using Structural Equation Modelling and Confirmatory Factor Analysis,” Int. j. res. entrep. bus. stud., vol. 3, no. 4, pp. 19–32, Oct. 2022, doi: 10.47259/ijrebs.342.

F. de M. C. Torres, “La Autoeficacia Como Variable en la Motivación Intrínseca y Extrínseca en Matemáticas a Través de un Criterio Étnico,” Ph.D., Universidad Complutense De Madrid, Madrid, Spain, 2012.

M. F. Zalazar Jaime, Aparicio Martín Matías Daniel, C. M. Ramírez Flores, and S. J. Garrido, “Estudios preliminares de adaptación de la escala de fuentes de autoeficacia para matemáticas,” RACC, vol. 3, no. 2, pp. 1–6, 2011.

M. Auerswald and M. Moshagen, “How to determine the number of factors to retain in exploratory factor analysis: A comparison of extraction methods under realistic conditions.,” Psychological Methods, vol. 24, no. 4, pp. 468–491, Aug. 2019, doi: 10.1037/met0000200.

J. B. Schreiber, “Issues and recommendations for exploratory factor analysis and principal component analysis,” Research in Social and Administrative Pharmacy, vol. 17, no. 5, pp. 1004–1011, May 2021, doi: 10.1016/j.sapharm.2020.07.027.

A. F. Nunes, P. L. Monteiro, and A. S. Nunes, “Factor structure of the convergence insufficiency symptom survey questionnaire,” PLoS ONE, vol. 15, no. 2, p. e0229511, Feb. 2020, doi: 10.1371/journal.pone.0229511.

G. Shmueli et al., “Predictive model assessment in PLS-SEM: guidelines for using PLSpredict,” European journal of marketing, 2019.

N. Shrestha, “Factor analysis as a tool for survey analysis,” American Journal of Applied Mathematics and Statistics, vol. 9, no. 1, pp. 4–11, 2021, doi: 10.12691/ajams-9-1-2.

N. Shrestha, “Factor Analysis as a Tool for Survey Analysis,” AJAMS, vol. 9, no. 1, pp. 4–11, Jan. 2021, doi: 10.12691/ajams-9-1-2.

A. S. Beavers, J. W. Lounsbury, J. K. Richards, S. W. Huck, G. J. Skolits, and S. L. Esquivel, “Practical Considerations for Using Exploratory Factor Analysis in Educational Research”, doi: 10.7275/QV2Q-RK76.

A. Beauducel and N. Hilger, “On the Detection of the Correct Number of Factors in Two-Facet Models by Means of Parallel Analysis,” Educational and Psychological Measurement, vol. 81, no. 5, pp. 872–903, Oct. 2021, doi: 10.1177/0013164420982057.

D. Goretzko, C. Heumann, and M. Bühner, “Investigating Parallel Analysis in the Context of Missing Data: A Simulation Study Comparing Six Missing Data Methods,” Educational and Psychological Measurement, vol. 80, no. 4, pp. 756–774, Aug. 2020, doi: 10.1177/0013164419893413.

S. Lim and S. Jahng, “Determining the number of factors using parallel analysis and its recent variants.,” Psychological Methods, vol. 24, no. 4, pp. 452–467, Aug. 2019, doi: 10.1037/met0000230.

J. F. Hair, T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, and S. Ray, Partial least squares structural equation modeling (PLS-SEM) using R: a workbook. in Classroom Companion: Business. Cham: Springer, 2021.

J. Hulland, “Use of partial least squares (PLS) in strategic management research: a review of four recent studies,” Strat. Mgmt. J., vol. 20, no. 2, pp. 195–204, Feb. 1999, doi: 10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7.

J. F. Hair, G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, and S. Ray, Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R, vol. 46, no. 1–2. in Classroom Companion: Business, vol. 46. Cham: Springer International Publishing, 2021. doi: 10.1007/978-3-030-80519-7.

T. K. Dijkstra and J. Henseler, “Consistent partial least squares path modeling,” MIS quarterly, vol. 39, no. 2, pp. 297–316, 2015.

A. F. Hayes and J. J. Coutts, “Use Omega Rather than Cronbach’s Alpha for Estimating Reliability. But…,” Communication Methods and Measures, vol. 14, no. 1, pp. 1–24, Jan. 2020, doi: 10.1080/19312458.2020.1718629.

J. F. Hair Jr and L. P. Fávero, “Multilevel modeling for longitudinal data: concepts and applications,” RAUSP Management Journal, vol. 54, pp. 459–489, 2019.

M. Sarstedt, J. F. Hair, and C. M. Ringle, “‘PLS-SEM: indeed a silver bullet’ – retrospective observations and recent advances,” Journal of Marketing Theory and Practice, Apr. 2022, doi: 10.1080/10696679.2022.2056488.

E. Cho, “Neither Cronbach’s Alpha nor McDonald’s Omega: A Commentary on Sijtsma and Pfadt,” Psychometrika, vol. 86, no. 4, pp. 877–886, 2021, doi: 10.1007/s11336-021-09801-1.

C. Fornell and D. F. Larcker, “Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics,” Journal of Marketing Research, vol. 18, no. 3, p. 382, Aug. 1981, doi: 10.2307/3150980.

J. F. Hair, L. M. Matthews, R. L. Matthews, and M. Sarstedt, “PLS-SEM or CB-SEM: updated guidelines on which method to use,” International Journal of Multivariate Data Analysis, vol. 1, no. 2, 2017, doi: 10.1504/ijmda.2017.10008574.

E. L. Usher and F. Pajares, “Sources of self-efficacy in mathematics: A validation study,” Contemporary Educational Psychology, vol. 34, no. 1, pp. 89–101, 2009, doi: 10.1016/j.cedpsych.2008.09.002.


Article Metrics

Abstract view : 322 times | PDF view : 66 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Nurul Mukhlisah Abdal, S.Si., M.Si., Dwi Rezky Anandari Sulaiman, Wirawan Setialaksana

Terindeks:

        

 

 

Diterbitkan Oleh:

Program Studi Pendidikan Teknik Informatika dan Komputer,

Jurusan Teknik Informatika dan Komputer,

Fakultas Teknik Universitas Negeri Makassar,

Makassar, Telp. (0411) 889629

Email: jurnal.mediatik@unm.ac.id

 Creative Commons License
MediaTIK is licensed under a Creative Commons Attribution 4.0 International License.

 

Web Analytics View My Stats MediaTIK