Classroom Conditions in Relation to Learners’ Reading Motivation: An Assessment
DOI:
https://doi.org/10.64612/ijiv.v2i3.94Keywords:
Classroom conditions, Reading motivation, Grade 10 learners, Learning environment, Literacy development, Public secondary school, Descriptive-correlational studyAbstract
This study assessed how the conditions in the classroom affected the reading drive of 10th graders at a public secondary school in Davao de Oro, Philippines during the 2023–2024 school year. Based on the idea that a good learning setting is important for boosting literacy, the study looked at how classroom amenities, equipment, and safety affect students’ desire to read. The study used a descriptive-correlational design and had 90 learners who were chosen at random to fill out an approved self-made Likert-scale questionnaire. To find out how students felt about the conditions in the classroom, descriptive statistics were used. Pearson’s correlation coefficient was used to look at the link between the conditions in the classroom and students’ desire to read. The results showed that students thought their classroom settings were great, which suggests that it was a good place to learn. Positive classroom conditions were also strongly linked to better levels of reading motivation, especially when it came to enjoying reading tasks and being willing to do them. The results show that classes that are well-equipped, safe, and comfortable can make students more motivated to read. Overall, the study shows how important it is to make classrooms better in Philippine public secondary schools to help students learn to read and write, get more involved in school, and do well in their studies.
References
Akram, H., & Li, S. (2024). Understanding the role of teacher-student relationships in students’ online learning engagement: Mediating role of academic motivation. Perceptual and Motor Skills, 131(4), 1415-1438. https://doi.org/10.1177/00315125241248709
Aldosari, M., Heydarnejad, T., Hashemifardnia, A., & Abdalgane, M. (2023). The interplay among self-assessment, using reflection for assessment, classroom enjoyment, and immunity: into prospects of effective language learning. Language Testing in Asia, 13(1), 1. https://doi.org/10.1186/s40468-022-00213-1
Arrogante, R. (2025). Multilingual Legitimacy in Japanese Eikaiwa: A Conceptual Framework for Teacher Authority. International Journal of Interdisciplinary Viewpoints, 1(5), 646–652. https://doi.org/10.64612/ijiv.v1i5.42
Azuela, E. B., Coballes, F. E. B., Iraula, N. M., & Paterno, K. V. (2023). Investigating factors contributing to the reading challenges among intermediate learners of San Rafael-Agpo Elementary School. International Journal of Research and Innovation in Social Science, 7(10). https://doi.org/10.47772/IJRISS.2023.71069
Babbie, E. (2020). The practice of social research (15th ed.). Cengage Learning.
Balalle, H. (2024). Exploring student engagement in technology-based education in relation to gamification, online/distance learning, and other factors: A systematic literature review. Social Sciences & Humanities Open, 9, 100870. https://doi.org/10.1016/j.ssaho.2024.100870
Cariaga, R., El Halaissi, M., Refugio, C., Dagunan, M. A., Sabidalas, M. A., Cariaga, V., … Gerodias, E. (2025). Local Voices, Global Technologies: AI Integration Barriers in K–12 Classrooms. International Journal of Interdisciplinary Viewpoints, 1(5), 672–680. https://doi.org/10.64612/ijiv.v1i5.45
Cariaga, R., Sabidalas, M. A., Dagunan, M. A., Refugio, C., Cariaga, V., Gerodias, E., & Cubero, G. (2025). Challenges of Pre-service Teachers in K–12 Classrooms: An Explanatory Case Study. International Journal of Interdisciplinary Viewpoints, 1(6), 732–737. https://doi.org/10.64612/ijiv.v1i6.54
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Da-anton, C. L., & Dioso, E. (2025). The Effects of Electronic Gadgets on the Learning Behavior of Students: A Correlation. International Journal of Interdisciplinary Viewpoints, 1(4), 529–551. https://doi.org/10.64612/ijiv.v1i4.31
Field, A. (2024). Discovering statistics using IBM SPSS statistics. Sage publications limited.
Galaura, R. J., & Simpal, E. A. (2025). Challenges in the Implementation of K to 12 Program and Their Influence on the Instructional Competence of Teachers. International Journal of Interdisciplinary Viewpoints, 1(2), 121–132. https://doi.org/10.64612/ijiv.v1i2.13
Hayati, H. A., & Puspitaloka, N. (2022). An analysis of students’ reading comprehension difficulties during covid-19 pandemic with online classes in junior high school. JET (Journal of English Teaching), 8(2), 293-300.
Li, J., Wang, C., Zhao, Y., & Li, Y. (2024). Boosting Learners’ Confidence in Learning English: Can Self‐Efficacy‐Based Intervention Make a Difference?. Tesol Quarterly, 58(4), 1518-1547. https://doi.org/10.1002/tesq.3292
Lu, G., Xie, K., & Liu, Q. (2022). What influences student situational engagement in smart classrooms: Perception of the learning environment and students’ motivation. British Journal of Educational Technology, 53(6), 1665-1687. https://doi.org/10.1111/bjet.13204
Ludewig, U., Kleinkorres, R., Schaufelberger, R., Schlitter, T., Lorenz, R., König, C., ... & McElvany, N. (2022). COVID-19 pandemic and student reading achievement: Findings from a school panel study. Frontiers in psychology, 13, 876485. https://doi.org/10.3389/fpsyg.2022.876485
Reeve, J. (2023). Cognitive evaluation theory: The seedling that keeps self-determination theory growing. The Oxford handbook of self-determination theory, 33-52.
Reeve, J., Jang, H. R., Cheon, S. H., Moss, J. D., Ko, H., & Jang, H. (2023). Extending self-determination theory’s dual-process model to a new tripartite model to explain diminished functioning. Motivation and Emotion, 47(5), 691-710. https://doi.org/10.1007/s11031-023-10019-0
Schunk, D. H., & DiBenedetto, M. K. (2021). Self-Regulation, self-efficacy, and learning disabilities. In Learning disabilities-neurobiology, assessment, clinical features and treatments. IntechOpen. https://doi.org/10.5772/intechopen.99570
Trabelsi, Z., Alnajjar, F., Parambil, M. M. A., Gochoo, M., & Ali, L. (2023). Real-time attention monitoring system for classroom: A deep learning approach for student’s behavior recognition. Big Data and Cognitive Computing, 7(1), 48. https://doi.org/10.3390/bdcc7010048
Wigfield, A. (2022). An Interview with Allan Wigfield: A Leading Force in Developing Research on Expectancy-Value, Motivation, and Academic Literacy. https://doi.org/10.1108/978-1-64802-855-720251003
Yuan, L., & Liu, X. (2025). The effect of artificial intelligence tools on EFL learners’ engagement, enjoyment, and motivation. Computers in Human Behavior, 162, 108474. https://doi.org/10.1016/j.chb.2024.108474
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