IDEAS home Printed from https://ideas.repec.org/a/bcp/journl/v8y2024i12p2025-2037.html
   My bibliography  Save this article

Teacher Support and Academic Stress: The Mediating Effects of Self-Regulated Learning in Physics among Senior High School STEM Students

Author

Listed:
  • Mohamad T. Simpal

    (Science, Technology, Engineering and Mathematics (STEM) Department, Senior High School, Esperanza National High School, Division of Sultan Kudarat, Philippines)

  • Cashmere Zinneth A. Montoya

    (Science, Technology, Engineering and Mathematics (STEM) Department, Senior High School, Esperanza National High School, Division of Sultan Kudarat, Philippines)

  • Althea Grace A. Pasawilan

    (Science, Technology, Engineering and Mathematics (STEM) Department, Senior High School, Esperanza National High School, Division of Sultan Kudarat, Philippines)

  • Re-An Kaye J. Bernal

    (Science, Technology, Engineering and Mathematics (STEM) Department, Senior High School, Esperanza National High School, Division of Sultan Kudarat, Philippines)

  • Trixy Devie Faith G. Nacionales

    (Science, Technology, Engineering and Mathematics (STEM) Department, Senior High School, Esperanza National High School, Division of Sultan Kudarat, Philippines)

Abstract

Academic stress among students ascends from multidimensional pressure points derived from workloads, competitions, and fear of failure. Despite the pursuit of investigating the influence of teacher support on academic stress, more clarity is still needed in uncovering the nuances of academic stress and how it is affected by other factors. In this study, the researchers determined the mediating effects of self-regulated learning in the relationship between teacher support and academic stress in physics among Senior High STEM students using a descriptive causal research design employed randomly on 210 Grade 12 STEM students in the Division of Sultan Kudarat. An adopted survey questionnaire underwent validity and reliability tests and was subjected to confirmatory factor analysis (CFA) distributed through Google Forms to gather necessary data. The mean and standard deviation, Pearson’s r correlation, simple linear regression analysis, and mediation analysis utilizing the Preacher and Hayes (2008) approach were used. The results revealed that the extent of teacher’s support, academic stress, and self-regulated learning were high. Moreover, the high extent of teacher’s support significantly positively predicted self-regulated learning and academic stress. More so, teacher support has a direct effect on self-regulated learning. Further, a partial mediation exists between teacher support and academic stress through self-regulated learning. Thus, teachers must provide adequate support to their students in maximizing their learning experiences so they can contribute to the development of students’ self-regulated learning skills. With sufficient support, students can enhance their self-regulated learning strategies, which helps them minimize academic stress.

Suggested Citation

  • Mohamad T. Simpal & Cashmere Zinneth A. Montoya & Althea Grace A. Pasawilan & Re-An Kaye J. Bernal & Trixy Devie Faith G. Nacionales, 2024. "Teacher Support and Academic Stress: The Mediating Effects of Self-Regulated Learning in Physics among Senior High School STEM Students," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(12), pages 2025-2037, December.
    • Handle: RePEc:bcp:journl:v:8:y:2024:i:12:p:2025-2037
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijriss/Digital-Library/volume-8-issue-12/2025-2037.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijriss/articles/teacher-support-and-academic-stress-the-mediating-effects-of-self-regulated-learning-in-physics-among-senior-high-school-stem-students/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gurdip Bakshi & Xiaohui Gao & Alberto G. Rossi, 2019. "Understanding the Sources of Risk Underlying the Cross Section of Commodity Returns," Management Science, INFORMS, vol. 65(2), pages 619-641, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Baur, Dirk G. & Smales, Lee A., 2020. "Hedging geopolitical risk with precious metals," Journal of Banking & Finance, Elsevier, vol. 117(C).
    2. Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2023. "The commodity risk premium and neural networks," Journal of Empirical Finance, Elsevier, vol. 74(C).
    3. Zaremba, Adam & Bianchi, Robert J. & Mikutowski, Mateusz, 2021. "Long-run reversal in commodity returns: Insights from seven centuries of evidence," Journal of Banking & Finance, Elsevier, vol. 133(C).
    4. Nicole M. Moran & Scott H. Irwin & Philip Garcia, 2020. "Who Wins and Who Loses? Trader Returns and Risk Premiums in Agricultural Futures Markets," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(4), pages 611-652, December.
    5. Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2020. "Does sophistication of the weighting scheme enhance the performance of long-short commodity portfolios?," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 164-180.
    6. Logan Cochrane & Eric P. H. Li & Melisew Dejene & M. Mustahid Husain, 2024. "Why foreign agricultural investment fails? Five lessons from Ethiopia," Journal of International Development, John Wiley & Sons, Ltd., vol. 36(1), pages 541-558, January.
    7. Jun Yuan & Qi Xu & Ying Wang, 2023. "Probability weighting in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(4), pages 516-548, April.
    8. Fernandez-Perez, Adrian & Fuertes, Ana-Maria & Miffre, Joelle, 2021. "The risk premia of energy futures," Energy Economics, Elsevier, vol. 102(C).
    9. Jangkoo Kang & Kyung Yoon Kwon, 2021. "Volatility‐managed commodity futures portfolios," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(2), pages 159-178, February.
    10. Fuertes, Ana-Maria & Zhao, Nan, 2023. "A Bayesian perspective on commodity style integration," Journal of Commodity Markets, Elsevier, vol. 30(C).
    11. Pu, Yingjian & Yang, Baochen, 2022. "The commodity futures' historical basis in trading strategy and portfolio investment," Energy Economics, Elsevier, vol. 105(C).
    12. Daxuan Cheng & Yin Liao & Zheyao Pan, 2023. "The geopolitical risk premium in the commodity futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(8), pages 1069-1090, August.
    13. Cortazar, Gonzalo & Ortega, Hector & Rojas, Maximiliano & Schwartz, Eduardo S., 2021. "Commodity index risk premium," Journal of Commodity Markets, Elsevier, vol. 22(C).
    14. Prachi Jain & Debasish Maitra, 2025. "Commodity Price Crash Risk and Crash Risk Contagion," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(4), pages 343-378, April.
    15. Immo Stadtmüller & Benjamin R. Auer & Frank Schuhmacher, 2024. "Core-satellite investing with commodity futures momentum," Journal of Asset Management, Palgrave Macmillan, vol. 25(3), pages 261-287, May.
    16. Shimeng Shi & Jia Zhai & Yingying Wu, 2024. "Informational inefficiency on bitcoin futures," The European Journal of Finance, Taylor & Francis Journals, vol. 30(6), pages 642-667, April.
    17. Liu, Zhenya & Lu, Shanglin & Li, Bo & Wang, Shixuan, 2023. "Time series momentum and reversal: Intraday information from realized semivariance," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 54-77.
    18. Pan, Zhiyuan & Zhong, Hao & Wang, Yudong & Huang, Juan, 2024. "Forecasting oil futures returns with news," Energy Economics, Elsevier, vol. 134(C).
    19. Cotter, John & Eyiah-Donkor, Emmanuel & Potì, Valerio, 2023. "Commodity futures return predictability and intertemporal asset pricing," Journal of Commodity Markets, Elsevier, vol. 31(C).
    20. Qi Xu & Yang Ye, 2023. "Commodity network and predictable returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(10), pages 1423-1449, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bcp:journl:v:8:y:2024:i:12:p:2025-2037. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Pawan Verma (email available below). General contact details of provider: https://rsisinternational.org/journals/ijriss/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.