Digital Competence, Self-Efficacy, Academic Stress and Perceived Mathematical Problem-Solving Ability Among Madrasah Aliyah Students

Yola Puspita; Erisa Kurniati; Rohati Rohati

doi:10.22437/edumatica.v16i2.56121

Authors

Yola Puspita Universitas Jambi
Erisa Kurniati Universitas Jambi
Rohati Rohati Universitas Jambi

DOI:

https://doi.org/10.22437/edumatica.v16i2.56121

Keywords:

academic burnout, digital competence, mathematical problem-solving, PLS-SEM, self-efficacy

Abstract

Mathematics performance among Indonesian students remained a pressing concern, yet the joint contribution of digital competence, academic stress, self-efficacy, and academic burnout to students' perceived mathematical problem-solving ability had rarely been examined together. This study examined these relationships among 515 Madrasah Aliyah students in Jambi Province, selected through stratified random sampling and analysed using Partial Least Squares Structural Equation Modelling via SmartPLS. Self-efficacy was the strongest predictor of perceived problem-solving ability (β = 0.397, p < 0.001) but, contrary to expectations, was also positively associated with academic stress (β = 0.369, p < 0.001) and academic burnout (β = 0.133, p = 0.002), suggesting a dark side of high self-belief under competitive conditions. Academic burnout showed an unexpected positive association with perceived ability (β = 0.184, p < 0.001), interpreted as a possible suppression effect. Digital competence was modestly associated with perceived ability (β = 0.116, p < 0.01) but not with stress or burnout, while academic stress was the strongest predictor of burnout (β = 0.446, p < 0.001). The model explained 35.5% of the variance in perceived ability. Psychological resilience mattered more for perceived mathematical competence than digital skills alone, supporting policies pairing digital literacy with stress-regulation support for high-efficacy students.

References

Al Umairi, K. S. (2024). Role of mathematics motivation in the relationship between mathematics self-efficacy and achievement. Journal of Pedagogical Research, 8(4), 125–146. https://doi.org/10.33902/JPR.202428560

Ali, N. B., Suyurno, S. S., Norddin, M. F., & Ramli, A. (2025). Academic fatigue in Asian higher education: A thematic literature review. Environment-Behaviour Proceedings Journal, 10(34), 65–71. https://doi.org/10.21834/e-bpj.v10i34.7255

Almarzouki, A. F. (2024). Stress, working memory, and academic performance: A neuroscience perspective. Stress, 27(1), Article 2364333. https://doi.org/10.1080/10253890.2024.2364333

Asanre, A. A., Sondlo, A., Chinaka, T., & Oluwadoyo, T. A. (2024). Mathematics self-efficacy and study habit as predictors of achievement of senior secondary school students. Mathematics Education Journal, 8(2), 137–146. https://doi.org/10.22219/mej.v8i2.34183

Asare, B., Dissou Arthur, Y., & Adu Obeng, B. (2025). Mathematics self-belief and mathematical creativity of university students: The role of problem-solving skills. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2025.2456438

Aulia, R., Rohati, R., & Marlina, M. (2021). Students' self-confidence and their mathematical communication skills in solving problems. Edumatika: Jurnal Riset Pendidikan Matematika, 4(2), 90–103. https://doi.org/10.32939/ejrpm.v4i2.770

Badan Pusat Statistik Provinsi Jambi. (2025). Jumlah satuan pendidikan, kepala sekolah dan guru, serta peserta didik Madrasah Aliyah (MA) di bawah Kementerian Agama menurut kabupaten/kota di Provinsi Jambi, 2025/2026. https://jambi.bps.go.id/id/statistics-table/3/VUUxWVltazBUblI1VG5veWNIbFliek5uYmtGSVp6MDkjMw==/jumlah-sekolah--guru--dan-murid-madrasah-aliyah--ma--di-bawah-kementerian-agama-menurut-kabupaten-kota-di-provinsi-jambi--2017.html?year=2025

Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.

Cabero-Almenara, J., Gutiérrez-Castillo, J. J., Guillén-Gámez, F. D., & Gaete-Bravo, A. F. (2022). Digital competence of higher education students as a predictor of academic success. Technology, Knowledge and Learning, 28(2), 683–702. https://doi.org/10.1007/s10758-022-09624-8

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

Cuder, A., Živković, M., Doz, E., Pellizzoni, S., & Passolunghi, M. C. (2023). The relationship between math anxiety and math performance: The moderating role of visuospatial working memory. Journal of Experimental Child Psychology, 233, Article 105688. https://doi.org/10.1016/j.jecp.2023.105688

Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7(2), 336–353. https://doi.org/10.1037/1528-3542.7.2.336

Fauziyah, U. T., Solihat, A. N., & Kurniawan, U. S. (2024). Pengaruh self regulated learning dan self efficacy terhadap academic burnout mahasiswa Pendidikan Ekonomi Universitas Siliwangi. Jurnal Ilmiah Nusantara, 1(5), 519–532. https://doi.org/10.61722/JINU.V1I5.2613

Gao, X. (2023). Academic stress and academic burnout in adolescents: A moderated mediating model. Frontiers in Psychology, 14, Article 1133706. https://doi.org/10.3389/fpsyg.2023.1133706

González-Gómez, B., Colomé, À., & Núñez-Peña, M. I. (2023). Math anxiety and attention: Biased orienting to math symbols or less efficient attentional control? Current Psychology, 43(7), 6533–6548. https://doi.org/10.1007/s12144-023-04828-2

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications.

Hendra, R., Habibi, A., Ridwan, A., Sembiring, D. A. E. P., Wijaya, T. T., Denmar, D., & Widana, I. W. (2025). The impact of perfectionism, self-efficacy, academic stress, and workload on academic fatigue and learning achievement: Indonesian perspectives. Open Education Studies, 7(1). https://doi.org/10.1515/edu-2025-0071

Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382

Herianto, H., Sofroniou, A., Fitrah, M., Rosana, D., Setiawan, C., Rosnawati, R., Widihastuti, W., Jusmiana, A., & Marinding, Y. (2024). Quantifying the relationship between self-efficacy and mathematical creativity: A meta-analysis. Education Sciences, 14(11), Article 1251. https://doi.org/10.3390/educsci14111251

Ilhami, M., Amanah, S., Sophia, R., Priyanto, Kumalasari, A., Kurniati, E., Hayati, S., & Nusantara, D. S. (2025). The influence of scientific attitude, active learning, and friendly character on science learning outcomes in junior high school students. Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 9(1), 1–14. https://doi.org/10.22437/jiituj.v9i1.41809

Jebb, A. T., Ng, V., & Tay, L. (2021). A review of key Likert scale development advances: 1995–2019. Frontiers in Psychology, 12, Article 637547. https://doi.org/10.3389/fpsyg.2021.637547

Joshi, D. R., Sharma Chapai, K. P., Upadhayaya, P. R., Adhikari, K. P., & Belbase, S. (2025). Effect of using digital resources on mathematics achievement: Results from PISA 2022. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2025.2488161

Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101

Kuokkanen, J., Saarinen, M., Phipps, D. J., Korhonen, J., & Romar, J. E. (2025). Unveiling the longitudinal reciprocal relationship between burnout and engagement among adolescent athletes in sport schools. Journal of Adolescence, 97(2), 383–394. https://doi.org/10.1002/jad.12426

Liljedahl, P. (2021). Building thinking classrooms in mathematics, grades K–12. Corwin Press.

Lukitasari, M., Murtafiah, W., Ramdiah, S., Hasan, R., & Sukri, A. (2022). Constructing digital literacy instrument and its effect on college students' learning outcomes. International Journal of Instruction, 15(2), 171–188. https://doi.org/10.29333/iji.2022.15210a

Madigan, D. J., & Curran, T. (2021). Does burnout affect academic achievement? A meta-analysis of over 100,000 students. Educational Psychology Review, 33(2), 387–405. https://doi.org/10.1007/s10648-020-09533-1

Madrilejos, K. (2025). Structural equation model of students' interest, motivation, self-efficacy, persistence, and perceived teaching quality in mathematics. Journal of Interdisciplinary Perspectives, 3(3), 260–272. https://doi.org/10.69569/jip.2024.0698

Moustaka, E., Bacopoulou, F., Manousou, K., Kanaka-Gantenbein, C., Chrousos, G. P., & Darviri, C. (2023). Reliability and validity of the Educational Stress Scale for Adolescents (ESSA) in a sample of Greek students. Children, 10(2), Article 292. https://doi.org/10.3390/children10020292

NCTM. (2000). Principles and standards for school mathematics: An overview. National Council of Teachers of Mathematics.

OECD. (2023). PISA 2022 results (Factsheets, Vol. I). https://www.oecd-ilibrary.org/education/pisa-2022-results-volume-i_53f23881-en

Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what's being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489–497. https://doi.org/10.1002/nur.20147

Polya, G. (1957). How to solve it: A new aspect of mathematical method (2nd ed.). Princeton University Press.

Ramadhanti, A., Fathiyah, K. N., & Putra, P. (2025). Academic self-efficacy and teacher social support as predictors of academic flow mathematics among senior high school students in Jambi City. Journal of Indonesian Psychological Science, 5(1), 59–78.

Ran, H., Kim, N. J., & Secada, W. G. (2022). A meta-analysis on the effects of technology's functions and roles on students' mathematics achievement in K-12 classrooms. Journal of Computer Assisted Learning, 38(1), 258–284. https://doi.org/10.1111/jcal.12611

Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4 [Computer software]. SmartPLS GmbH. https://www.smartpls.com

Salmela-Aro, K., Kiuru, N., Leskinen, E., & Nurmi, J. E. (2009). School Burnout Inventory (SBI): Reliability and validity. European Journal of Psychological Assessment, 25(1), 48–57. https://doi.org/10.1027/1015-5759.25.1.48

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research (pp. 587–632). Springer. https://doi.org/10.1007/978-3-319-57413-4_15

Schoenfeld, A. H. (2022). Why are learning and teaching mathematics so difficult? In Handbook of cognitive mathematics (pp. 763–797). Springer. https://doi.org/10.1007/978-3-031-03945-4_10

Simsek, M. (2022). Predicting mathematics performance by ICT variables in PISA 2018 through decision tree algorithm. International Journal of Technology in Education, 5(2), 269–279. https://doi.org/10.46328/ijte.296

Skulmowski, A., & Xu, K. M. (2022). Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 34(1), 171–196. https://doi.org/10.1007/s10648-021-09624-7

Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x

Sun, J., Dunne, M. P., Hou, X., & Xu, A. (2011). Educational Stress Scale for Adolescents: Development, validity, and reliability with Chinese students. Journal of Psychoeducational Assessment, 29(6), 534–546. https://doi.org/10.1177/0734282910394976

Szczygieł, M., & Pieronkiewicz, B. (2022). Exploring the nature of math anxiety in young children: Intensity, prevalence, and reasons. Mathematical Thinking and Learning. https://doi.org/10.1080/10986065.2021.1882363

Ulum, H., & Küçükdanaci, T. (2022). The relationship between mathematics anxiety and mathematics achievement: Meta analysis study. Research on Education and Psychology, 6(2), 193–206. https://doi.org/10.54535/rep.1206987

Usher, E. L., & Pajares, F. (2008). Sources of self-efficacy in school: Critical review of the literature and future directions. Review of Educational Research, 78(4), 751–796. https://doi.org/10.3102/0034654308321456

Valverde-Berrocoso, J., Acevedo-Borrega, J., & Cerezo-Pizarro, M. (2022). Educational technology and student performance: A systematic review. Frontiers in Education, 7, Article 916502. https://doi.org/10.3389/feduc.2022.916502

Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The Digital Competence Framework for citizens — With new examples of knowledge, skills and attitudes. Publications Office of the European Union. https://doi.org/10.2760/115376

Wagiran, W., Suharjana, S., Nurtanto, M., & Mutohhari, F. (2022). Determining the e-learning readiness of higher education students: A study during the COVID-19 pandemic. Heliyon, 8(10), Article e11160. https://doi.org/10.1016/j.heliyon.2022.e11160

Wawan, & Retnawati, H. (2022). Empirical study of factors affecting the students' mathematics learning achievement. International Journal of Instruction, 15(2), 417–434. https://doi.org/10.29333/iji.2022.15223a

Widlund, A., Tuominen, H., & Korhonen, J. (2023). Reciprocal effects of mathematics performance, school engagement and burnout during adolescence. British Journal of Educational Psychology, 93(1), 183–197. https://doi.org/10.1111/bjep.12548

Zakariya, Y. F. (2022). Improving students' mathematics self-efficacy: A systematic review of intervention studies. Frontiers in Psychology, 13, Article 986622. https://doi.org/10.3389/fpsyg.2022.986622

Zhang, J., Meng, J., & Wen, X. (2025). The relationship between stress and academic burnout in college students: Evidence from longitudinal data on indirect effects. Frontiers in Psychology, 16, Article 1517920. https://doi.org/10.3389/fpsyg.2025.1517920

Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257