##plugins.themes.academic_pro.article.sidebar##
Published
Feb 26, 2025
Download
Statistic
Read Counter : 105
Download : 32
Downloads
Download data is not yet available.
Main Article Content
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.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Martos , G., & Jose, D. S. (2025). Diminishing Marginal Utility of Technological Devices toward Academic Performance in Mathematics, Reading, and Science. International Journal of Multidisciplinary: Applied Business and Education Research, 6(2), 971-992. https://doi.org/10.11594/ijmaber.06.02.38
References
Abd. Rashid, J., Abdul Aziz, A., Abdul Rahman, A., Saaid, S. A., & Ahmad, Z. (2020). The influence of mobile phone addiction on academic 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 Percep-tion 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 im-pact of gadgets on student learning out-comes: A case study in indonesia junior high school students. https://doi.org/10.5281/ZENODO.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 performance in a mechanics of materials course*. International Journal of Engi-neering 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, average, and high perceived academic performance levels among high school students 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 outcomes: 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 be-tween student profile, tool use, participa-tion, and academic performance with the use of Augmented Reality technology for visualized architecture models. Comput-ers 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 pri-or 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 education: 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/advance.24226158.v1
Giray, L., Nemeño, J., Braganaza, J., Lucero, S. M., & Bacarra, R. (2024). A survey on dig-ital 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. Ameri-can 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://newsinfo.inquirer.net/1402235/sws-58-of-pinoy-students-used-devices-for-distance-learning
Kostić, J., & Ranđelović, K. R. (2022). Digital distractions: Learning in multitasking en-vironment. Psychological Applications and Trends. https://api.semanticscholar.org/CorpusID:248626251
Kutzhan, A., Shaikym, A., & Sadyk, U. (2023). A comparative study of the impact of elec-tronic devices on university students’ ac-ademic performance. 2023 17th Interna-tional Conference on Electronics Comput-er 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 Medi-cal 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 con-cept map for e-book via automatic key-word extraction. In T.-T. Wu, R. Gennari, Y.-M. Huang, H. Xie, & Y. Cao (Eds.), Emerging Technologies for Education (Vol. 10108, pp. 75–85). Springer Interna-tional Publishing. 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/record.uri?eid=2-s2.0-85131330370&partnerID=40&md5=1ada1ec867dd5d24d163adef88f5f523
Mafunda, B., & Swart, A. J. (2020). The impact of MindTap on the academic achievement of first-year software application stu-dents. World Transactions on Engineering and Technology Education, 18(1), 63–67. Scopus.
Maydeu-Olivares, A., & Cai, L. (2006). A cau-tionary note on using g2 (dif) to assess relative model fit in categorical data analysis. Multivariate Behavioral Re-search, 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 me-dia use and academic performance in late childhood: A longitudinal study. PLOS ONE, 15(9), e0237908. https://doi.org/10.1371/journal.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. Ba-lakrishnan, & B. Vidakovic (Eds.), Ency-clopedia of Statistical Sciences (2nd ed.). Wiley. https://doi.org/10.1002/0471667196.ess1311
Pohlert, T. (2024). PMCMRplus: Calculate pairwise multiple comparisons of mean rank sums extended. https://CRAN.R-project.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 academic performance. International Journal of Information and Education Technology, 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 laptop 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 Com-munication Load and Internet Multitask-ing 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 technology 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 Mul-timedia Devices: Negative Consequences for Socioemotional Development in Early Childhood. Children, 10(11), 1807. https://doi.org/10.3390/children10111807
Rodríguez-Arelis, G. A., Lourenzutti, R., & Coia, V. (2024). Lecture 3—Generalized linear models: Ordinal logistic regression—Dsci 562—Regression ii [Github]. DSCI 562: Regression II. https://ubc-mds.github.io/DSCI_562_regr-2/notes/lecture3_glm_ordinal_regression.html
Sattar Chaudhry, A. (2014). Student response to e-books: Study of attitude toward reading among elementary school chil-dren 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/package=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 methods for r developed in the political science computational laboratory (Ver-sion 1.5.9.) [R]. https://github.com/atahk/pscl (Original 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 sys-tématique et méta-analyse des études longitudinales réalisées à ce jour. Cana-dian Journal of Behavioural Science / Re-vue Canadienne Des Sciences Du Com-portement, 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., Web-ber, S., Smith, W., Koval, P., Hollenstein, T., Goncalves, J., & Kostakos, V. (2022). Impact 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). Spring-er. 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 learn-ing 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/curated/en/796871601650398190/pdf/Philippines-Digital-Economy-Report-2020-A-Better-Normal-Under-COVID-19-Digitalizing-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 nega-tive emotions and physical complaints in the home office: A follow up study. Health Psychology and Behavioral Medi-cine, 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). Spring-er 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-
pro-ject.org/web/packages/VGAM/index.html
Yee, T. W. (2022). On the hauck-donner effect in wald tests: Detection, tipping points, and parameter space characterization. Journal of the American Statistical Asso-ciation, 117(540), 1763–1774. https://doi.org/10.1080/01621459.2021.1886936
Zhou, Y., & Deng, L. (2023). A systematic re-view of media multitasking in educational contexts: Trends, gaps, and antecedents. Interactive Learning Environments, 31(10), 6279–6294. Scopus. https://doi.org/10.1080/10494820.2022.2032760
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 Percep-tion 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 im-pact of gadgets on student learning out-comes: A case study in indonesia junior high school students. https://doi.org/10.5281/ZENODO.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 performance in a mechanics of materials course*. International Journal of Engi-neering 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, average, and high perceived academic performance levels among high school students 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 outcomes: 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 be-tween student profile, tool use, participa-tion, and academic performance with the use of Augmented Reality technology for visualized architecture models. Comput-ers 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 pri-or 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 education: 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/advance.24226158.v1
Giray, L., Nemeño, J., Braganaza, J., Lucero, S. M., & Bacarra, R. (2024). A survey on dig-ital 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. Ameri-can 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://newsinfo.inquirer.net/1402235/sws-58-of-pinoy-students-used-devices-for-distance-learning
Kostić, J., & Ranđelović, K. R. (2022). Digital distractions: Learning in multitasking en-vironment. Psychological Applications and Trends. https://api.semanticscholar.org/CorpusID:248626251
Kutzhan, A., Shaikym, A., & Sadyk, U. (2023). A comparative study of the impact of elec-tronic devices on university students’ ac-ademic performance. 2023 17th Interna-tional Conference on Electronics Comput-er 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 Medi-cal 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 con-cept map for e-book via automatic key-word extraction. In T.-T. Wu, R. Gennari, Y.-M. Huang, H. Xie, & Y. Cao (Eds.), Emerging Technologies for Education (Vol. 10108, pp. 75–85). Springer Interna-tional Publishing. 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/record.uri?eid=2-s2.0-85131330370&partnerID=40&md5=1ada1ec867dd5d24d163adef88f5f523
Mafunda, B., & Swart, A. J. (2020). The impact of MindTap on the academic achievement of first-year software application stu-dents. World Transactions on Engineering and Technology Education, 18(1), 63–67. Scopus.
Maydeu-Olivares, A., & Cai, L. (2006). A cau-tionary note on using g2 (dif) to assess relative model fit in categorical data analysis. Multivariate Behavioral Re-search, 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 me-dia use and academic performance in late childhood: A longitudinal study. PLOS ONE, 15(9), e0237908. https://doi.org/10.1371/journal.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. Ba-lakrishnan, & B. Vidakovic (Eds.), Ency-clopedia of Statistical Sciences (2nd ed.). Wiley. https://doi.org/10.1002/0471667196.ess1311
Pohlert, T. (2024). PMCMRplus: Calculate pairwise multiple comparisons of mean rank sums extended. https://CRAN.R-project.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 academic performance. International Journal of Information and Education Technology, 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 laptop 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 Com-munication Load and Internet Multitask-ing 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 technology 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 Mul-timedia Devices: Negative Consequences for Socioemotional Development in Early Childhood. Children, 10(11), 1807. https://doi.org/10.3390/children10111807
Rodríguez-Arelis, G. A., Lourenzutti, R., & Coia, V. (2024). Lecture 3—Generalized linear models: Ordinal logistic regression—Dsci 562—Regression ii [Github]. DSCI 562: Regression II. https://ubc-mds.github.io/DSCI_562_regr-2/notes/lecture3_glm_ordinal_regression.html
Sattar Chaudhry, A. (2014). Student response to e-books: Study of attitude toward reading among elementary school chil-dren 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/package=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 methods for r developed in the political science computational laboratory (Ver-sion 1.5.9.) [R]. https://github.com/atahk/pscl (Original 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 sys-tématique et méta-analyse des études longitudinales réalisées à ce jour. Cana-dian Journal of Behavioural Science / Re-vue Canadienne Des Sciences Du Com-portement, 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., Web-ber, S., Smith, W., Koval, P., Hollenstein, T., Goncalves, J., & Kostakos, V. (2022). Impact 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). Spring-er. 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 learn-ing 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/curated/en/796871601650398190/pdf/Philippines-Digital-Economy-Report-2020-A-Better-Normal-Under-COVID-19-Digitalizing-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 nega-tive emotions and physical complaints in the home office: A follow up study. Health Psychology and Behavioral Medi-cine, 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). Spring-er 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-
pro-ject.org/web/packages/VGAM/index.html
Yee, T. W. (2022). On the hauck-donner effect in wald tests: Detection, tipping points, and parameter space characterization. Journal of the American Statistical Asso-ciation, 117(540), 1763–1774. https://doi.org/10.1080/01621459.2021.1886936
Zhou, Y., & Deng, L. (2023). A systematic re-view of media multitasking in educational contexts: Trends, gaps, and antecedents. Interactive Learning Environments, 31(10), 6279–6294. Scopus. https://doi.org/10.1080/10494820.2022.2032760