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Interval: Indonesian Journal of Mathematical Education

an Open Access Journal

Unveiling College Student Preferences: Integrating Numerical and Factor Analysis in Understanding Choices for Mathematics Majors

Fitri Rahmayani
Universitas Islam Negeri Alauddin Makassar ROR
Indonesia
fifielfifira29@gmail.com
Sulaiman Muritala Hambali
Kwara State University ROR
Nigeria
Amin Abrishami Moghadam
Ferdowsi University of Mashhad ROR
Iran, Islamic Republic of
Dumitru Ripeanu
Romanian Academy ROR
Romania
Thulisile Nkambule
University of Eswatini ROR
Botswana
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  • Purpose of the study: This study aims to understand the factors that influence students in choosing a mathematics major using the factor analysis method.

    Methodology: Data were collected through structured interviews from 150 students at two different universities using stratified random sampling techniques. Analysis was performed using Principal Component Analysis (PCA) and Varimax rotation to identify the main dimensions that influence student preferences. Numerical analysis helped to group the variables into relevant factors based on the loading values

    Main Findings: Factors that influence students in choosing Mathematics Major consist of 19 variables which are grouped into 5 factors, namely: the first factor is privileges and facilities with an eigenvalue of 4.088%, the second factor is the lecture building and social factors with an eigenvalue of 2.431%, the third factor is the promotion factor with an eigenvalue of 1.743%, the fourth factor is the job factor with an eigenvalue of 1.351%, the fifth factor is the comfort factor with an eigenvalue of 1.148%.

    Novelty/Originality of this study: These findings provide new insights for educational institutions in designing effective promotional strategies and developing relevant curricula to increase the attractiveness of mathematics majors. The novelty of this study lies in the application of factor analysis to map students' specific reasons, which has rarely been done before in the context of higher education.

  • How to cite

    Unveiling College Student Preferences: Integrating Numerical and Factor Analysis in Understanding Choices for Mathematics Majors. (2023). Interval: Indonesian Journal of Mathematical Education, 1(2), 83-98. https://doi.org/10.37251/ijome.v1i2.1346

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    1. V. G. Goulart, L. B. Liboni, and L. O. Cezarino, “Balancing skills in the digital transformation era: The future of jobs and the role of higher education,” Ind. High. Educ., vol. 36, no. 2, pp. 118–127, 2022, doi: 10.1177/09504222211029796. DOI: https://doi.org/10.1177/09504222211029796
    2. M. Akour and M. Alenezi, “Higher Education Future in the Era of Digital Transformation Mohammad Akour and Mamdouh Alenezi * Software,” Educ. Sci., vol. 12, no. 784, pp. 1–13, 2022, doi: 10.3390/educsci12110784. DOI: https://doi.org/10.3390/educsci12110784
    3. S. Sudrajat, A. Tri Wijayanti, and G. Kumar Jha, “Inculcating Honesty Values in Boarding School: Study in Muhammadiyah Boarding School Yogyakarta,” AL-ISHLAH J. Pendidik., vol. 16, no. 1, pp. 317–327, 2024, doi: 10.35445/alishlah.v16i1.4501. DOI: https://doi.org/10.35445/alishlah.v16i1.4501
    4. D. Müller, M. Chiodo, and J. Franklin, “A Hippocratic Oath for Mathematicians? Mapping the Landscape of Ethics in Mathematics,” Sci. Eng. Ethics, vol. 28, no. 5, pp. 1–30, 2022, doi: 10.1007/s11948-022-00389-y. DOI: https://doi.org/10.1007/s11948-022-00389-y
    5. G. Stillman, J. Brown, and J. Czocher, “Yes, mathematicians do X so students should do X, but it’s not the X you think!,” ZDM - Math. Educ., vol. 52, no. 6, pp. 1211–1222, 2020, doi: 10.1007/s11858-020-01183-5. DOI: https://doi.org/10.1007/s11858-020-01183-5
    6. R. Q. Berry, “2021 Founders Lecture: Examining Mathematics Education Reforms’ Impact on Historically Excluded Learners,” Investig. Math. Learn., vol. 13, no. 3, pp. 153–166, 2021, doi: 10.1080/19477503.2021.1938870. DOI: https://doi.org/10.1080/19477503.2021.1938870
    7. B. Mainali, “Representation in teaching and learning mathematics,” Int. J. Educ. Math. Sci. Technol., vol. 9, no. 1, pp. 1–21, 2021, doi: 10.46328/ijemst.1111. DOI: https://doi.org/10.46328/ijemst.1111
    8. A. Bicer, “A systematic literature review: Discipline-specific and general instructional practices fostering the mathematical creativity of students,” Int. J. Educ. Math. Sci. Technol., vol. 9, no. 2, pp. 252–281, 2021, doi: 10.46328/IJEMST.1254. DOI: https://doi.org/10.46328/ijemst.1254
    9. A. Bakker, J. Cai, and L. Zenger, “Future themes of mathematics education research: an international survey before and during the pandemic,” Educ. Mat., vol. 35, no. 2, pp. 9–46, 2023, doi: 10.24844/EM3502.01. DOI: https://doi.org/10.24844/EM3502.01
    10. P. Juric, M. B. Bakaric, and M. Matetic, “Cognitive predispositions of students for STEM success and differences in solving problems in the computer game for learning mathematics,” Int. J. Eng. Pedagog., vol. 11, no. 44, pp. 81–94, 2021, doi: 10.3991/IJEP.V11I4.20587. DOI: https://doi.org/10.3991/ijep.v11i4.20587
    11. C. Cresswell and C. P. Speelman, “Does mathematics training lead to better logical thinking and reasoning? A cross-sectional assessment from students to professors,” PLoS One, vol. 15, no. 7, p. e0236153, 2020. DOI: https://doi.org/10.1371/journal.pone.0236153
    12. H. C. Çelik and F. Özdemir, “Mathematical Thinking as a Predictor of Critical Thinking Dispositions of Pre-service Mathematics Teachers,” Int. J. Progress. Educ., vol. 16, no. 4, pp. 81–98, 2020, doi: 10.29329/ijpe.2020.268.6. DOI: https://doi.org/10.29329/ijpe.2020.268.6
    13. A. K. Lampinen et al., “Language models, like humans, show content effects on reasoning tasks,” PNAS Nexus, vol. 3, no. 7, pp. 1–13, 2024, doi: 10.1093/pnasnexus/pgae233. DOI: https://doi.org/10.1093/pnasnexus/pgae233
    14. Y. Makarem and J. Wang, “Career experiences of women in science, technology, engineering, and mathematics fields: A systematic literature review,” Hum. Resour. Dev. Q., vol. 31, no. 1, pp. 91–111, 2020, doi: 10.1002/hrdq.21380. DOI: https://doi.org/10.1002/hrdq.21380
    15. B. Gajdzik and R. Wolniak, “Smart Production Workers in Terms of Creativity and Innovation: The Implication for Open Innovation,” J. Open Innov. Technol. Mark. Complex., vol. 8, no. 2, 2022, doi: 10.3390/joitmc8020068. DOI: https://doi.org/10.3390/joitmc8020068
    16. S. Bianchini, M. Müller, and P. Pelletier, “Artificial intelligence in science: An emerging general method of invention,” Res. Policy, vol. 51, no. 10, p. 104604, 2022, doi: 10.1016/j.respol.2022.104604. DOI: https://doi.org/10.1016/j.respol.2022.104604
    17. N. T. Hoang, D. Tran, N. Huy, and N. T. Hoang, “Determining Factors for Educating Students for Choosing to Work for Foreign Units: Absence of Self-Efficacy,” J. Educ. Teach. Trainers, vol. 12, no. 2, pp. 11–19, 2021, doi: 10.47750/jett.2021.12.02.002. DOI: https://doi.org/10.47750/jett.2021.12.02.002
    18. X. Zhang and F. Dong, “Why do consumers make green purchase decisions? Insights from a systematic review,” Int. J. Environ. Res. Public Health, vol. 17, no. 18, pp. 1–25, 2020, doi: 10.3390/ijerph17186607. DOI: https://doi.org/10.3390/ijerph17186607
    19. G. W. Scurati, M. Bertoni, S. Graziosi, and F. Ferrise, “Exploring the use of virtual reality to support environmentally sustainable behavior: A framework to design experiences,” Sustain., vol. 13, no. 2, pp. 1–20, 2021, doi: 10.3390/su13020943. DOI: https://doi.org/10.3390/su13020943
    20. C. Camden and M. Silva, “Pediatric Teleheath: Opportunities Created by the COVID-19 and Suggestions to Sustain Its Use to Support Families of Children with Disabilities,” Phys. Occup. Ther. Pediatr., vol. 41, no. 1, pp. 1–17, 2021, doi: 10.1080/01942638.2020.1825032. DOI: https://doi.org/10.1080/01942638.2020.1825032
    21. J. Manning, J. Billian, J. Matson, C. Allen, and N. Soares, “Perceptions of Families of Individuals with Autism Spectrum Disorder during the COVID-19 Crisis,” J. Autism Dev. Disord., vol. 51, no. 8, pp. 2920–2928, 2021, doi: 10.1007/s10803-020-04760-5. DOI: https://doi.org/10.1007/s10803-020-04760-5
    22. M. B. Chafi, A. Hultberg, and N. B. Yams, “Post-pandemic office work: Perceived challenges and opportunities for a sustainable work environment,” Sustain., vol. 14, no. 1, pp. 1–20, 2022, doi: 10.3390/su14010294. DOI: https://doi.org/10.3390/su14010294
    23. F. Ferri, P. Grifoni, and T. Guzzo, “Online learning and emergency remote teaching: Opportunities and challenges in emergency situations,” Societies, vol. 10, no. 4, pp. 1–18, 2020, doi: 10.3390/soc10040086. DOI: https://doi.org/10.3390/soc10040086
    24. A. Aithal and P. S. Aithal, “Development and Validation of Survey Questionnaire and experimental data,” Int. J. Manag. Technol. Soc. Sci., vol. 5, no. 2, pp. 233–251, 2020. DOI: https://doi.org/10.47992/IJMTS.2581.6012.0116
    25. A. I. Hamzani, T. V. Widyastuti, N. Khasanah, and M. H. M. Rusli, “Legal Research Method: Theoretical and Implementative Review,” Int. J. Membr. Sci. Technol., vol. 10, no. 2, pp. 3610–3619, 2023, doi: 10.15379/ijmst.v10i2.3191. DOI: https://doi.org/10.15379/ijmst.v10i2.3191
    26. W. Wu, M. Zhang, and Y. Ding, “Exploring the effect of economic and environment factors on PM2.5 concentration: A case study of the Beijing-Tianjin-Hebei region,” J. Environ. Manage., vol. 268, no. April, p. 110703, 2020, doi: 10.1016/j.jenvman.2020.110703. DOI: https://doi.org/10.1016/j.jenvman.2020.110703
    27. C. de la Porte and M. D. Jensen, “The next generation EU: An analysis of the dimensions of conflict behind the deal,” Soc. Policy Adm., vol. 55, no. 2, pp. 388–402, 2021, doi: 10.1111/spol.12709. DOI: https://doi.org/10.1111/spol.12709
    28. Y. M. Tang et al., “Comparative analysis of Student’s live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector,” Comput. Educ., vol. 168, no. April, 2021, doi: 10.1016/j.compedu.2021.104211. DOI: https://doi.org/10.1016/j.compedu.2021.104211
    29. A. Robitzsch, “Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods,” Front. Educ., vol. 5, no. October, pp. 1–7, 2020, doi: 10.3389/feduc.2020.589965. DOI: https://doi.org/10.3389/feduc.2020.589965
    30. M. Shoaib et al., “A stochastic numerical analysis based on hybrid NAR-RBFs networks nonlinear SITR model for novel COVID-19 dynamics,” Comput. Methods Programs Biomed., vol. 202, no. January, pp. 1–18, 2021. DOI: https://doi.org/10.1016/j.cmpb.2021.105973
    31. A. Afzal, A. D. M. Samee, R. D. Jilte, M. T. Islam, A. M. Manokar, and K. Abdul Razak, “Battery thermal management: An optimization study of parallelized conjugate numerical analysis using Cuckoo search and Artificial bee colony algorithm,” Int. J. Heat Mass Transf., vol. 166, no. February, p. 120798, 2021, doi: 10.1016/j.ijheatmasstransfer.2020.120798. DOI: https://doi.org/10.1016/j.ijheatmasstransfer.2020.120798
    32. C. K. Y. Chan, “A comprehensive AI policy education framework for university teaching and learning,” Int. J. Educ. Technol. High. Educ., vol. 20, no. 1, pp. 1–25, 2023, doi: 10.1186/s41239-023-00408-3. DOI: https://doi.org/10.1186/s41239-023-00408-3
    33. N. T. Hoa, T. N. H. Dinh, and V. T. Tran, “Implementation of students scientific research policy at universal education institutions in vietnam in today situation and solutions,” Rev. Int. Geogr. Educ., vol. 11:, pp. 11–19, 2021, doi: 10.48047/rigeo.11.08.XXXX. DOI: https://doi.org/10.47059/revistageintec.v11i3.1913
    34. S. Anggarista and A. Y. Wahyudin, “A Correlational Study of Language Learning Strategies and English Proficiency of University Students At Efl Context,” J. Arts Educ., vol. 2, no. 1, 2022, doi: 10.33365/jae.v2i1.68. DOI: https://doi.org/10.33365/jae.v2i1.68
    35. Bulkani, M. Fatchurahman, H. Adella, and M. Andi Setiawan, “Development of animation learning media based on local wisdom to improve student learning outcomes in elementary schools,” Int. J. Instr., vol. 15, no. 1, pp. 55–72, 2022, doi: 10.29333/iji.2022.1514a. DOI: https://doi.org/10.29333/iji.2022.1514a
    36. V. Kuleto et al., “Extended reality in higher education, a responsible innovation approach for generation y and generation z,” Sustain., vol. 13, no. 21, pp. 1–19, 2021, doi: 10.3390/su132111814. DOI: https://doi.org/10.3390/su132111814
    37. C. C. Thelma, “International Journal of Research Publication and Reviews The Impact of Remote Learning on Educational Outcomes : A Case of Selected Higher Learning Institutions in Lusaka District , Zambia,” Int. J. Res. Publ. Rev., vol. 5, no. 7, pp. 587–596, 2024. DOI: https://doi.org/10.55248/gengpi.5.0124.0230
    38. A. Shavazipour, H. Jan, K. Multi-scenario, B. Shavazipour, J. H. Kwakkel, and K. Miettinen, “This is a self-archived version of an original article . This version may differ from the original in pagination and typographic details . approach Copyright : Rights : Rights url : Please cite the original version : Multi-scenario multi-objective robust ,” Environ. Model. Softw., vol. 144, no. 2, p. 105134, 2021, doi: 10.1564/tme. DOI: https://doi.org/10.1016/j.envsoft.2021.105134
    39. R. Kaushik, M. Parmar, and S. Jhamb, “Roles and Research Trends of Artificial Intelligence in Mathematics Education,” Proc. - 2021 2nd Int. Conf. Comput. Methods Sci. Technol. ICCMST 2021, pp. 202–205, 2021, doi: 10.1109/ICCMST54943.2021.00050. DOI: https://doi.org/10.1109/ICCMST54943.2021.00050
    40. Y. Wardat, M. A. Tashtoush, R. AlAli, and A. M. Jarrah, “ChatGPT: A revolutionary tool for teaching and learning mathematics,” Eurasia J. Math. Sci. Technol. Educ., vol. 19, no. 7, 2023, doi: 10.29333/ejmste/13272. DOI: https://doi.org/10.29333/ejmste/13272
    41. P. Vankúš, “Influence of game-based learning in mathematics education on students’ affective domain: A systematic review,” Mathematics, vol. 9, no. 9, 2021, doi: 10.3390/math9090986. DOI: https://doi.org/10.3390/math9090986
    42. B. N. Yusuf and J. Ahmad, “Are We Prepared Enough? a Case Study of Challenges in Online Learning in a Private Higher Learning Institution During the Covid-19 Outbreaks,” Adv. Soc. Sci. Res. J., vol. 7, no. 5, pp. 205–212, 2020, doi: 10.14738/assrj.75.8211. DOI: https://doi.org/10.14738/assrj.75.8211
    43. W. Ali, “Online and Remote Learning in Higher Education Institutes: A Necessity in light of COVID-19 Pandemic,” High. Educ. Stud., vol. 10, no. 3, p. 16, 2020, doi: 10.5539/hes.v10n3p16. DOI: https://doi.org/10.5539/hes.v10n3p16
    44. A. M. Maatuk, E. K. Elberkawi, S. Aljawarneh, H. Rashaideh, and H. Alharbi, “The COVID-19 pandemic and E-learning: challenges and opportunities from the perspective of students and instructors,” J. Comput. High. Educ., vol. 34, no. 1, pp. 21–38, 2022, doi: 10.1007/s12528-021-09274-2. DOI: https://doi.org/10.1007/s12528-021-09274-2
    45. N. Shrestha, “Factor Analysis as a Tool for Survey Analysis,” Am. J. Appl. Math. Stat., vol. 9, no. 1, pp. 4–11, 2021, doi: 10.12691/ajams-9-1-2. DOI: https://doi.org/10.12691/ajams-9-1-2
    46. V. E. Messerle, A. B. Ustimenko, and M. K. Bodykbaeva, “Plasma-chemical waste processing: numerical analysis and experiment. Part 1: Medical-biological waste and fuel biomass,” Thermophys. Aeromechanics, vol. 30, no. 1, pp. 183–192, 2023, doi: 10.1134/S0869864323010201. DOI: https://doi.org/10.1134/S0869864323010201
    47. R. S. Afif and D. Amelia, “Humanistic Hierarchy of Needs on Auggie’S Personality in Wonder Movie,” Linguist. Lit. J., vol. 2, no. 2, pp. 69–74, 2021, doi: 10.33365/llj.v2i2.1351. DOI: https://doi.org/10.33365/llj.v2i2.1351
    48. J. Liu, S. Li, and Q. Ji, “Regional differences and driving factors analysis of carbon emission intensity from transport sector in China,” Energy, vol. 224, p. 120178, 2021, doi: 10.1016/j.energy.2021.120178. DOI: https://doi.org/10.1016/j.energy.2021.120178
    49. B. M. S. Hasan and A. M. Abdulazeez, “A Review of Principal Component Analysis Algorithm for Dimensionality Reduction,” J. Soft Comput. Data Min., vol. 2, no. 1, pp. 20–30, 2021, doi: 10.30880/jscdm.2021.02.01.003. DOI: https://doi.org/10.30880/jscdm.2021.02.01.003
    50. J. R. Beattie and F. W. L. Esmonde-White, “Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra,” Appl. Spectrosc., vol. 75, no. 4, pp. 361–375, 2021, doi: 10.1177/0003702820987847. DOI: https://doi.org/10.1177/0003702820987847
    51. B. Tellman et al., “Satellite imaging reveals increased proportion of population exposed to floods,” Nature, vol. 596, no. 7870, pp. 80–86, 2021, doi: 10.1038/s41586-021-03695-w. DOI: https://doi.org/10.1038/s41586-021-03695-w
    52. S. Anand et al., “Prevalence of SARS-CoV-2 antibodies in a large nationwide sample of patients on dialysis in the USA: a cross-sectional study,” Lancet, vol. 396, no. 10259, pp. 1335–1344, 2020, doi: 10.1016/S0140-6736(20)32009-2. DOI: https://doi.org/10.1016/S0140-6736(20)32009-2
    53. R. Iliyasu and I. Etikan, “Comparison of quota sampling and stratified random sampling,” Biometrics Biostat. Int. J., vol. 10, no. 1, pp. 24–27, 2021, doi: 10.15406/bbij.2021.10.00326. DOI: https://doi.org/10.15406/bbij.2021.10.00326
    54. S. Baltes and P. Ralph, “Sampling in software engineering research: a critical review and guidelines,” Empir. Softw. Eng., vol. 27, no. 4, pp. 1–38, 2022, doi: 10.1007/s10664-021-10072-8. DOI: https://doi.org/10.1007/s10664-021-10072-8
    55. M. M. Rahman, M. I. Tabash, A. Salamzadeh, S. Abduli, and M. S. Rahaman, “Sampling Techniques (Probability) for Quantitative Social Science Researchers: A Conceptual Guidelines with Examples,” SEEU Rev., vol. 17, no. 1, pp. 42–51, 2022, doi: 10.2478/seeur-2022-0023. DOI: https://doi.org/10.2478/seeur-2022-0023
    56. A. C. Tabuena, Y. M. Hilario, and M. Buenaflor, “Overview and Exemplar Components of the Research Methodology on the Research Writing Process for Senior High School Students,” SSRN Electron. J., 2021, doi: 10.2139/ssrn.3802722. DOI: https://doi.org/10.2139/ssrn.3802722
    57. A. R. Virgiawan, S. Riyanto, and E. Endri, “Organizational culture as a mediator motivation and transformational leadership on employee performance,” Acad. J. Interdiscip. Stud., vol. 10, no. 3, pp. 67–79, 2021, doi: 10.36941/AJIS-2021-0065. DOI: https://doi.org/10.36941/ajis-2021-0065
    58. H. Baharudin, M. I. Hamzah, and N. bin Rani, “Validation of Existing Arabic Language Reading Knowledge Instrument (SPSAMBA): Exploratory Factor Analysis (EFA),” Psychol. Educ. J., vol. 58, no. 1, pp. 2464–2476, 2021, doi: 10.17762/pae.v58i1.1123. DOI: https://doi.org/10.17762/pae.v58i1.1123
    59. R. F. Setiawan, “Analysis of Factors Affecting The Interest of People to Use Dana Application Using Principal Component Analysis Method (PCA),” Int. Res. J. Adv. Eng. Sci., vol. 5, no. 1, pp. 226–232, 2020.
    60. M. K. Islam, M. S. Ali, M. S. Miah, M. M. Rahman, M. S. Alam, and M. A. Hossain, “Brain tumor detection in MR image using superpixels, principal component analysis and template based K-means clustering algorithm,” Mach. Learn. with Appl., vol. 5, no. January, p. 100044, 2021, doi: 10.1016/j.mlwa.2021.100044. DOI: https://doi.org/10.1016/j.mlwa.2021.100044
    61. L. Sürücü, İ. Yıkılmaz, and A. Maşlakçı, “Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations,” Gümüşhane Üniversitesi Sağlık Bilim. Derg., vol. 13, no. 2, pp. 947–965, 2024, doi: 10.37989/gumussagbil.1183271. DOI: https://doi.org/10.37989/gumussagbil.1183271
    62. X. Wang, Z. Yan, Y. Zeng, X. Liu, X. Peng, and H. Yuan, “Research on correlation factor analysis and prediction method of overhead transmission line defect state based on association rule mining and RBF-SVM,” Energy Reports, vol. 7, pp. 359–368, 2021, doi: 10.1016/j.egyr.2021.01.058. DOI: https://doi.org/10.1016/j.egyr.2021.01.058
    63. G. Tzounas, I. Dassios, M. Liu, and F. Milano, “Comparison of numerical methods and open-source libraries for eigenvalue analysis of large-scale power systems,” Appl. Sci., vol. 10, no. 21, pp. 1–27, 2020, doi: 10.3390/app10217592. DOI: https://doi.org/10.3390/app10217592
    64. M. Moreira, T. Silva, J. Dias-de-Oliveira, F. Neto, and C. Amaral, “Numerical modelling of radiant systems and phase change materials in building applications - a review,” Appl. Therm. Eng., vol. 234, no. August, p. 121342, 2023, doi: 10.1016/j.applthermaleng.2023.121342. DOI: https://doi.org/10.1016/j.applthermaleng.2023.121342
    65. A. Dawrea, R. G. Zytner, and J. Donald, “Enhanced GPR data interpretation to estimate in situ water saturation in a landfill,” Waste Manag., vol. 120, no. February, pp. 175–182, 2021, doi: 10.1016/j.wasman.2020.11.033. DOI: https://doi.org/10.1016/j.wasman.2020.11.033
    66. Z. Kohen and D. Orenstein, “Mathematical modeling of tech-related real-world problems for secondary school-level mathematics,” Educ. Stud. Math., vol. 107, no. 1, pp. 71–91, 2021, doi: 10.1007/s10649-020-10020-1. DOI: https://doi.org/10.1007/s10649-020-10020-1
    67. T. Valtonen et al., “Learning environments preferred by university students: a shift toward informal and flexible learning environments,” Learn. Environ. Res., vol. 24, no. 3, pp. 371–388, 2021, doi: 10.1007/s10984-020-09339-6. DOI: https://doi.org/10.1007/s10984-020-09339-6
    68. T. R. F. Kizi and O. B. B. Ugli, “Directions For Improving Investment Attraction Mechanisms,” EURASIAN J. Acad. Res., vol. 4, no. 1, pp. 144–151, 2024, doi: 10.5281/zenodo.10612578.
    69. D. Lusianti, Widodo, and Mulyana, “Mas’uliyah Society Brand Resonance: Enhancing Sustainable Marketing Performance of the National Health Insurance Program,” Qubahan Acad. J., vol. 4, no. 3, pp. 619–637, 2024, doi: 10.48161/qaj.v4n3a976. DOI: https://doi.org/10.48161/qaj.v4n3a976
    70. S. I. Dubbelink, C. H. Soria, and E. Constantinides, “Social media marketing as a branding strategy in extraordinary times: Lessons from the covid‐19 pandemic,” Sustain., vol. 13, no. 18, 2021, doi: 10.3390/su131810310. DOI: https://doi.org/10.3390/su131810310
    71. M. A. Ulaya, D. P. Ainapur, and P. S. Gudi, “Blended Approaches to Improve the Efficiency of Admissions to University affiliated MBA Program in the Selected Districts of North Karnataka,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 10, no. 5, pp. 4548–4559, 2022, doi: 10.22214/ijraset.2022.43341. DOI: https://doi.org/10.22214/ijraset.2022.43341
    72. U. C. Okolie, C. A. Nwajiuba, M. O. Binuomote, C. Ehiobuche, N. C. N. Igu, and O. S. Ajoke, “Career training with mentoring programs in higher education: Facilitating career development and employability of graduates,” Educ. Train., vol. 62, no. 3, pp. 214–234, 2020, doi: 10.1108/ET-04-2019-0071. DOI: https://doi.org/10.1108/ET-04-2019-0071
    73. E. Urquía-Grande and R. Pérez Estébanez, “Bridging the gaps between higher education and the business world: internships in a faculty of economics and business,” Educ. Train., vol. 63, no. 3, pp. 490–509, 2020, doi: 10.1108/ET-01-2018-0017. DOI: https://doi.org/10.1108/ET-01-2018-0017
    74. F. Smaldone, A. Ippolito, J. Lagger, and M. Pellicano, “Employability skills: Profiling data scientists in the digital labour market,” Eur. Manag. J., vol. 40, no. 5, pp. 671–684, 2022, doi: 10.1016/j.emj.2022.05.005. DOI: https://doi.org/10.1016/j.emj.2022.05.005