Diminishing Marginal Utility of Technological Devices toward Academic Performance in Mathematics, Reading, and Science
Gerald Martos | David S. Jose
Discipline: Education
Abstract:
The Philippines, in its pursuit of aligning its education system with global standards, has participated in the Program for International Student Assessment (PISA) which evaluates 15-year-olds' reading, scientific, and mathematical proficiency. However, the 2022 PISA report ranked Filipino learners among the lowest five in reading, science, and mathematics. This study explores how ownership of technological devices influences student performance in these domains. Using Ordinal Logistic Regression, we analyze the 2022 PISA ordinal data for 7608 Filipino students. Results show a diminishing marginal return on academic achievement as device ownership increases. While initial access to technology boosts performance, the effect weakens as students own more devices. This trend is stronger among learners without siblings and persists regardless of internal or external digital distractions. Findings emphasize the need for balanced digital engagement. Rather than restricting access or full enablement, families and policymakers should focus on strategic technology use to enhance education, aligning with Sustainable Development Goals for quality learning.
References:
- Abd. Rashid, J., Abdul Aziz, A., Abdul Rahman, A., Saaid, S. A., & Ahmad, Z. (2020). The in-fluence of mobile phone addiction on aca-demic performance among teenagers. Jurnal Komunikasi: Malaysian Journal of Communication, 36(3), 408–424. https://doi.org/10.17576/JKMJC-2020-3603-25
- Alzahabi, R., Becker, M. W., & Hambrick, D. Z. (2017). Investigating the relationship be-tween media multitasking and processes involved in task-switching. Journal of Ex-perimental Psychology: Human Perception and Performance, 43(11), 1872–1894. https://doi.org/10.1037/xhp0000412
- Aprianti, F., Dayurni, P., Fajari, L. E. W., Per-nanda, D., & Meilisa, R. (2022). The impact of gadgets on student learning outcomes: A case study in indonesia junior high school students. https://doi.org/10.5281/ZE-NODO.7446724
- Attia, N., Baig, L., Marzouk, Y. I., & Khan, A. (2017). The potential effect of technology and distractions on undergraduate stu-dents’ concentration. Pakistan Journal of Medical Sciences, 33(4). https://doi.org/10.12669/pjms.334.12560
- Badir, A., Tsegaye, S., & Girimurugan, S. (2023). The effect of mcgraw-hill connect online assessment on students’ academic perfor-mance in a mechanics of materials course*. International Journal of Engineer-ing Education, 39(5), 1242–1255. Scopus.
- Behnamian, A., Millard, K., Banks, S. N., White, L., Richardson, M., & Pasher, J. (2017). A systematic approach for variable selec-tion with random forests: Achieving sta-ble variable importance values. IEEE Geo-science and Remote Sensing Letters, 14(11), 1988–1992. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2017.2745049
- Bjerre-Nielsen, A., Andersen, A., Minor, K., & Lassen, D. D. (2020). The negative effect of smartphone use on academic performance may be overestimated: Evi-dence from a 2-year panel study. Psycho-logical Science, 31(11), 1351–1362. https://doi.org/10.1177/0956797620956613
- Buctot, D. B., Kim, N., & Kim, S.-H. (2021). Per-sonal profiles, family environment, pat-terns of smartphone use, nomophobia, and smartphone addiction across low, av-erage, and high perceived academic per-formance levels among high school stu-dents in the philippines. International Journal of Environmental Research and Public Health, 18(10). https://doi.org/10.3390/ijerph18105219
- Davidov, O., & Peddada, S. (2013). Testing for the multivariate stochastic order among ordered experimental groups with appli-cation to dose–response studies. Biomet-rics, 69(4), 10.1111/biom.12070. https://doi.org/10.1111/biom.12070
- Djinovic, V., & Giannakopoulos, N. (2024). Home computer ownership and educa-tional outcomes of adolescents in greece. Education Economics, 32(4), 523–537. https://doi.org/10.1080/09645292.2023.2243550
- Dolgun, A., & Saracbasi, O. (2014). Assessing proportionality assumption in the adja-cent category logistic regression model. Statistics and Its Interface, 7(2), 275–295. https://doi.org/10.4310/SII.2014.v7.n2.a12
- Drain, T., Grier, L., & Wenying, S. (2012). Is the growing use of electronic devices benefi-cial to academic performance? Results from archival data and a survey. Issues In Information Systems, 13(1), 225–231. https://doi.org/10.48009/1_iis_2012_225-231
- Fairlie, R. W., Beltran, D. O., & Das, K. K. (2010). Home computers and educational out-comes: Evidence from the nlsy97 and cps. Economic Inquiry, 48(3), 771–792. https://doi.org/10.1111/j.1465-7295.2009.00218.x
- Fairlie, R. W., & Robinson, J. (2013). Experi-mental evidence on the effects of home computers on academic achievement among schoolchildren. American Eco-nomic Journal: Applied Economics, 5(3), 211–240. https://doi.org/10.1257/app.5.3.211
- Fernández-Navarro, F. (2017). A generalized logistic link function for cumulative link models in ordinal regression. Neural Pro-cessing Letters, 46(1), 251–269. https://doi.org/10.1007/s11063-017-9589-3
- Fonseca, D., Martí, N., Redondo, E., Navarro, I., & Sánchez, A. (2014). Relationship between student profile, tool use, participation, and academic performance with the use of Augmented Reality technology for visual-ized architecture models. Computers in Human Behavior, 31, 434–445. https://doi.org/10.1016/j.chb.2013.03.006
- Gelman, A., Jakulin, A., Pittau, M. G., & Su, Y.-S. (2008). A weakly informative default prior distribution for logistic and other re-gression models. The Annals of Applied Statistics, 2(4). https://doi.org/10.1214/08-AOAS191
- GEM Report UNESCO. (2023). Global education monitoring report 2023: Technology in ed-ucation: a tool on whose terms?(1st ed.). GEM Report UNESCO. https://doi.org/10.54676/UZQV8501
- Ghimire, N. (2024). Understanding Disparities: Examining Demographic, socioeconomic, and Linguistic Impacts on U.S. Students’ Outcomes in Reading, Math, and Science. https://doi.org/10.31124/ad-vance.24226158.v1
- Giray, L., Nemeño, J., Braganaza, J., Lucero, S. M., & Bacarra, R. (2024). A survey on digital device engagement, digital stress, and coping strategies among college students in the Philippines. International Journal of Adolescence and Youth, 29(1), 2371413. https://doi.org/10.1080/02673843.2024.2371413
- Gnona, K. M., & Stewart, W. C. L. (2022). Revis-iting the wald test in small case-control studies with a skewed covariate. American Journal of Epidemiology, 191(8), 1508–1518. https://doi.org/10.1093/aje/kwac058
- Gunawan, A., Fong Cheong, M. L., & Poh, J. (2018). An essential applied statistical analysis course using rstudio with pro-ject-based learning for data science. 2018 IEEE International Conference on Teach-ing, Assessment, and Learning for Engi-neering (TALE), 581–588. https://doi.org/10.1109/TALE.2018.8615145
- Inquirer. (2021). 58% of Filipino students used devices for distance learning –SWS | In-quirer News. Inquirer.Net. https://news-info.inquirer.net/1402235/sws-58-of-pi-noy-students-used-devices-for-distance-learning
- Kostić, J., & Ranđelović, K. R. (2022). Digital dis-tractions: Learning in multitasking envi-ronment. Psychological Applications and Trends. https://api.seman-ticscholar.org/CorpusID:248626251
- Kutzhan, A., Shaikym, A., & Sadyk, U. (2023). A comparative study of the impact of elec-tronic devices on university students’ aca-demic performance. 2023 17th Interna-tional Conference on Electronics Computer and Computation (ICECCO), 1–4. https://doi.org/10.1109/ICECCO58239.2023.10147158
- Lahcene, B. (2015). Control charts for skewed distributions: Johnson’s distributions. In-ternational Journal of Statistics in Medical Research, 4(2), 217–223. https://doi.org/10.6000/1929-6029.2015.04.02.8
- Lai, C.-F., Tsai, C.-W., Chen, S.-Y., Hwang, R.-H., & Yang, C.-S. (2017). An intelligent concept map for e-book via automatic keyword ex-traction. In T.-T. Wu, R. Gennari, Y.-M. Huang, H. Xie, & Y. Cao (Eds.), Emerging Technologies for Education(Vol. 10108, pp. 75–85). Springer International Pub-lishing. https://doi.org/10.1007/978-3-319-52836-6_10
- Liaw, A., & Wiener, M. (2002). Classification and regression by randomforest. R News, 2(3), 18–22.Lu, F., Ferraro, F., & Raff, E. (2022). Continu-ously generalized ordinal regression for linear and deep models. Proc. SIAM Int. Conf. Data Min., SDM, 28–36. Scopus. https://www.scopus.com/inward/rec-ord.uri?eid=2-s2.0-85131330370&part-nerID=40&md5=1ada1ec867dd5d24d163adef88f5f523
- Mafunda, B., & Swart, A. J. (2020). The impact of MindTap on the academic achievement of first-year software application students. World Transactions on Engineering and Technology Education, 18(1), 63–67. Sco-pus.
- Maydeu-Olivares, A., & Cai, L. (2006). A caution-ary note on using g2(dif) to assess relative model fit in categorical data analysis. Mul-tivariate Behavioral Research, 41(1), 55–64. https://doi.org/10.1207/s15327906mbr4101_4
- Mundy, L. K., Canterford, L., Hoq, M., Olds, T., Moreno-Betancur, M., Sawyer, S., Kosola, S., & Patton, G. C. (2020). Electronic media use and academic performance in late childhood: A longitudinal study. PLOS ONE, 15(9), e0237908. https://doi.org/10.1371/jour-nal.pone.0237908
- Pinto, M., & Leite, C. (2020). Digital technolo-gies in support of students learning in Higher Education: Literature review. Dig-ital Education Review, 37, 343–360. https://doi.org/10.1344/der.2020.37.343-360
- Pirie, W. (2006). Jonckheere tests for ordered alternatives. In S. Kotz, C. B. Read, N. Bala-krishnan, & B. Vidakovic (Eds.), Encyclope-dia of Statistical Sciences(2nd ed.). Wiley. https://doi.org/10.1002/0471667196.ess1311
- Pohlert, T. (2024). PMCMRplus: Calculate pair-wise multiple comparisons of mean rank sums extended. https://CRAN.R-pro-ject.org/package=PMCMRplus
- Rahali, E. A., Chikhaoui, A., Khattabi, K. E., & Ouzennou, F. (2023). Learning with tab-lets in the primary school: Learners’ per-ceptions and impact on motivation and ac-ademic performance. International Jour-nal of Information and Education Technol-ogy, 13(3), 489–495. https://doi.org/10.18178/ijiet.2023.13.3.1830
- Ravizza, S. M., Uitvlugt, M. G., & Fenn, K. M. (2017). Logged in and zoned out: How lap-top internet use relates to classroom learning. Psychological Science, 28(2), 171–180. https://doi.org/10.1177/0956797616677314
- Reinecke, L., Aufenanger, S., Beutel, M. E., Dreier, M., Quiring, O., Stark, B., Wölfling, K., & Müller, K. W. (2017). Digital Stress over the Life Span: The Effects of Commu-nication Load and Internet Multitasking on Perceived Stress and Psychological Health Impairments in a German Proba-bility Sample. Media Psychology, 20(1), 90–115. https://doi.org/10.1080/15213269.2015.1121832
- Reisdorf, B. C., Triwibowo, W., & Yankelevich, A. (2020). Laptop or bust: How lack of tech-nology affects student achievement. American Behavioral Scientist, 64(7), 927–949. https://doi.org/10.1177/0002764220919145
- Rocha, B., Ferreira, L. I., Martins, C., Santos, R., & Nunes, C. (2023). The Dark Side of Multi-media Devices: Negative Consequences for Socioemotional Development in Early Childhood. Children, 10(11), 1807. https://doi.org/10.3390/chil-dren10111807
- Rodríguez-Arelis, G. A., Lourenzutti, R., & Coia, V. (2024). Lecture 3—Generalized linear models: Ordinal logistic regression—Dsci 562—Regression ii[Github]. DSCI 562: Re-gression II. https://ubc-mds.github.io/DSCI_562_regr-2/notes/lecture3_glm_ordinal_regres-sion.html
- Sattar Chaudhry, A. (2014). Student response to e-books: Study of attitude toward read-ing among elementary school children in kuwait. The Electronic Library, 32(4), 458–472. https://doi.org/10.1108/EL-04-2012-0041
- Schlegel, B., & Steenbergen, M. (2020). Brant: Test for parallel regression assumption. https://CRAN.R-project.org/pack-age=brant
- Shejwal, B. R., & Purayidathil, J. (2006). Televi-sion viewing of higher secondary stu-dents: Does it affect their academic achievement and mathematical reason-ing? Psychology and Developing Societies, 18(2), 201–213. https://doi.org/10.1177/097133360601800203
- Simon Jackman. (2024). PSCL: classes and meth-ods for r developed in the political science computational laboratory(Version 1.5.9.) [R]. https://github.com/atahk/pscl (Orig-inal work published 2017)
- Supper, W., Talbot, D., & Guay, F. (2022). Asso-ciation entre le temps d’écoute de la télé-vision et le rendement scolaire des en-fants et des adolescents: Recension systé-matique et méta-analyse des études longi-tudinales réalisées à ce jour. Canadian Journal of Behavioural Science / Revue Canadienne Des Sciences Du Comporte-ment, 54(4), 304–314. https://doi.org/10.1037/cbs0000275
- Tag, B., Van Berkel, N., Vargo, A. W., Sarsenba-yeva, Z., Colasante, T., Wadley, G., Webber, S., Smith, W., Koval, P., Hollenstein, T., Goncalves, J., & Kostakos, V. (2022). Im-pact of the global pandemic upon young people’s use of technology for emotion regulation. Computers in Human Behavior Reports, 6, 100192. https://doi.org/10.1016/j.chbr.2022.100192
- Tang, H., & Ji, P. (2014). Using the statistical program r instead of spss to analyze data. In Tools of Chemistry Education Research(Vol. 1166, pp. 135–151). American Chemical Society. https://doi.org/10.1021/bk-2014-1166.ch008
- Tutz, G., & Berger, M. (2017). Separating loca-tion and dispersion in ordinal regression models. Econometrics and Statistics, 2, 131–148. https://doi.org/10.1016/j.ecosta.2016.10.002
- Twenge, J. M., Martin, G. N., & Campbell, W. K. (2018). Decreases in psychological well-being among American adolescents after 2012 and links to screen time during the rise of smartphone technology. Emotion, 18(6), 765–780. https://doi.org/10.1037/emo0000403
- Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with s(Fourth). Springer. https://www.stats.ox.ac.uk/pub/MASS4/
- Wang, F., Ni, X., Zhang, M., & Zhang, J. (2024). Educational digital inequality: A meta-analysis of the relationship between digi-tal device use and academic performance in adolescents. Computers & Education, 213, 105003. https://doi.org/10.1016/j.compedu.2024.105003
- Wang, J. C., Hsieh, C.-Y., & Kung, S.-H. (2023). The impact of smartphone use on learning effectiveness: A case study of primary school students. Education and Infor-mation Technologies, 28(6), 6287–6320. https://doi.org/10.1007/s10639-022-11430-9
- World Bank. (2020). Philippines Digital Econ-omy Report 2020. World Bank. https://documents1.worldbank.org/cu-rated/en/796871601650398190/pdf/Philippines-Digital-Economy-Report-2020-A-Better-Normal-Under-COVID-19-Digi-talizing-the-Philippine-Economy-Now.pdf
- Wrede, S. J. S., Claassen, K., Rodil Dos Anjos, D., Kettschau, J. P., & Broding, H. C. (2023).
- Impact of digital stress on negative emo-tions and physical complaints in the home office: A follow up study. Health Psychol-ogy and Behavioral Medicine, 11(1), 2263068. https://doi.org/10.1080/21642850.2023.2263068
- Xu, W., Huang, R., Zhang, H., El-Khamra, Y., & Walling, D. (2016). Empowering r with high performance computing resources for big data analytics. In R. Arora (Ed.), Conquering Big Data with High Perfor-mance Computing(pp. 191–217). Springer International Publishing. https://doi.org/10.1007/978-3-319-33742-5_9
- Yee, T., cre, & src), C. M. (LINPACK routines in. (2024). VGAM: Vector generalized linear and additive models(Version 1.1-12) [Computer software]. https://cran.r-project.org/web/packages/VGAM/in-dex.html
- Yee, T. W. (2022). On the hauck-donner effect in waldtests: Detection, tipping points, and parameter space characterization. Journal of the American Statistical Associa-tion, 117(540), 1763–1774. https://doi.org/10.1080/01621459.2021.1886936
- Zhou, Y., & Deng, L. (2023). A systematic review of media multitasking in educational con-texts: Trends, gaps, and antecedents. In-teractive Learning Environments, 31(10), 6279–6294. Scopus. https://doi.org/10.1080/10494820.2022.2032760