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Abstract

This study aims to determine the mathematical model of student learning behavior. The model is built by analogizing the spread of learning behavior with infectious diseases, which is called the SEIR model. The survey was conducted through filling out a questionnaire on the learning behavior of junior high school students with a population of 1,143 students. The results of the simulation model show that the peak of students' vulnerability to changes in learning behavior increases rapidly in the first two days and will be stable when passing the 150th day. The results of the simulation of the SEIR mathematical model with an incubation period of 365 days found that student learning behavior in Non-Boarding Schools will be stable in on day 198, while in Boarding Schools it will be stable on day 201. Infection cases in Boarding Schools fell to 0 on day 25 while in Non-Boarding Schools decreased on day 21, meaning that infections occurring in Boarding Schools were slower and more resistant long, meaning that the influence of the social environment is very significant on student learning behavior. This study also serves as material for policy formulation for the Aceh Provincial Government regarding the junior high school curriculum.

Keywords

Learning Behavior Peers SEIR Models Social Interactions

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Mutiawati, Johar, R., Ramli, M., & Mailizar. (2022). Mathematical model of student learning behavior with the effect of learning motivation and student social interaction. Journal on Mathematics Education, 13(3), 415–436. https://doi.org/10.22342/jme.v13i3.pp415-436
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References

  1. Abdelrahman, R. M. (2020). Metacognitive awareness and academic motivation and their impact on academic achievement of Ajman University students. Heliyon, 6(9), e04192. https://doi.org/10.1016/j.heliyon.2020.e04192
  2. Ahmed, I., Modu, G. U., Yusuf, A., Kumam, P., & Yusuf, I. (2021). A mathematical model of Coronavirus Disease (COVID-19) containing asymptomatic and symptomatic classes. Results in Physics, 21, 103776. https://doi.org/10.1016/j.rinp.2020.103776
  3. Aiello, S., Crescimanno, G., Di Giovanni, G., & Casarrubea, M. (2020). T-patterns in the study of movement and behavioral disorders. Physiology and Behavior, 215(November 2019), 112790. https://doi.org/10.1016/j.physbeh.2019.112790
  4. Al-Osaimi, D. N., & Fawaz, M. (2022). Nursing students’ perceptions on motivation strategies to enhance academic achievement through blended learning: A qualitative study. Heliyon, 8(7), e09818. https://doi.org/10.1016/j.heliyon.2022.e09818
  5. Alban Conto, C., Akseer, S., Dreesen, T., Kamei, A., Mizunoya, S., & Rigole, A. (2021). Potential effects of COVID-19 school closures on foundational skills and Country responses for mitigating learning loss. International Journal of Educational Development, 87, 102434. https://doi.org/10.1016/j.ijedudev.2021.102434
  6. AlQaheri, H., & Panda, M. (2022). An Education Process Mining Framework: Unveiling Meaningful Information for Understanding Students’ Learning Behavior and Improving Teaching Quality. Information, 13(1), 29. https://doi.org/10.3390/info13010029
  7. Atangana, A., & Araz, S. İ. (2021). Application to non-linear ordinary differential equations. In New Numerical Scheme with Newton Polynomial (pp. 301–313). Elsevier. https://doi.org/10.1016/B978-0-32-385448-1.00022-6
  8. Bandura, A. (1977). Social Learning Theory (Vol. 148). NJ: Prentice-Hall.
  9. Canivez, G. L., & Beran, T. N. (2011). Learning behaviors scale and Canadian youths: Factorial validity generalization and comparisons to the U.S. standardization sample. Canadian Journal of School Psychology, 26(3), 193–208. https://doi.org/10.1177/0829573511416440
  10. Chandler, L., & Potter, A. (2012). Failure as opportunity-reflection and retention: Approaches to supporting first year university students experiencing early assessment failure. International Journal of Learning, 18(7), 73–88. https://doi.org/10.18848/1447-9494/cgp/v18i07/47680
  11. Changthong, J., Manmart, L., & Vongprasert, C. (2014). Learning styles: Factors affecting information behavior of Thai youth. Libres, 24(1), 50–61. https://doi.org/10.32655/libres.2014.1.5
  12. Chen, M., & Hastedt, D. (2022). The paradoxical relationship between students’ non-cognitive factors and mathematics & science achievement using TIMSS 2015 dataset. Studies in Educational Evaluation, 73. https://doi.org/10.1016/j.stueduc.2022.101145
  13. Chen, Q., Chen, Y., & Zhao, Q. (2020). Impacts of boarding on primary school students’ mental health outcomes – Instrumental-Variable evidence from rural northwestern China. Economics & Human Biology, 39, 100920. https://doi.org/10.1016/j.ehb.2020.100920
  14. Cheng, C. (2020). Duration matters: peer effects on academic achievement with random assignment in the Chinese context. Journal of Chinese Sociology, 7(1). https://doi.org/10.1186/s40711-020-0114-0
  15. Costa, A. P., & Steffgen, G. (2020). After the move to a new campus—effects on students’ satisfaction with the physical and learning environment. Education Sciences, 10(12), 1–13. https://doi.org/10.3390/educsci10120370
  16. Crone, E. A., & Achterberg, M. (2022a). Prosocial development in adolescence. Current Opinion in Psychology, 44, 220–225. https://doi.org/10.1016/j.copsyc.2021.09.020
  17. Crone, E. A., & Achterberg, M. (2022b). Prosocial development in adolescence. Current Opinion in Psychology, 44, 220–225. https://doi.org/10.1016/j.copsyc.2021.09.020
  18. Danovitch, J. H., Mills, C. M., Sands, K. R., & Williams, A. J. (2021). Mind the gap: How incomplete explanations influence children’s interest and learning behaviors. Cognitive Psychology, 130, 101421. https://doi.org/10.1016/j.cogpsych.2021.101421
  19. de Rivera, J., Gordo, Á., García-Arnau, A., & Díaz-Catalán, C. (2021). Youth digital socialization and their other intervening factors. Revista Complutense de Educacion, 32(3), 415–426. https://doi.org/10.5209/rced.70389
  20. Delamater, P. L., Street, E. J., Leslie, T. F., Yang, Y. T., & Jacobsen, K. H. (2019). Complexity of the Basic Reproduction Number (R 0 ). Emerging Infectious Diseases, 25(1), 1–4. https://doi.org/10.3201/eid2501.171901
  21. Dourado-Neto, D., Teruel, D. A., Reichardt, K., Nielsen, D. R., Frizzone, J. A., & Bacchi, O. O. S. (1998). Principles of crop modeling and simulation: I. Uses of mathematical models in agricultural science. Scientia Agricola, 55, 46–50
  22. Geitz, G., Brinke, D. J., & Kirschner, P. A. (2016). Changing learning behaviour : Self-ef fi cacy and goal orientation in PBL grsoups in higher education. International Journal of Educational Research, 75, 146–158. https://doi.org/10.1016/j.ijer.2015.11.001
  23. Ginevra, M. C., Nota, L., Heppner, P. P., Heppner, M., & Soresi, S. (2015). The Relationship of Personality Type, Problem-Solving Appraisal, and Learning Strategies. Journal of Career Assessment, 23(4), 545–558. https://doi.org/10.1177/1069072714553075
  24. Graziano, P. A., Reavis, R. D., Keane, S. P., & Calkins, S. D. (2007). The role of emotion regulation in children’s early academic success. Journal of School Psychology, 45(1), 3–19. https://doi.org/10.1016/j.jsp.2006.09.002
  25. Hadjar, A., Grecu, A., Scharf, J., de Moll, F., Morinaj, J., & Hascher, T. (2021). Changes in school alienation profiles among secondary school students and the role of teaching style: Results from a longitudinal study in Luxembourg and Switzerland. International Journal of Educational Research, 105, 101697. https://doi.org/10.1016/j.ijer.2020.101697
  26. Haghighi, M. M., & Jusan, M. M. (2012). Exploring Students Behavior on Seating Arrangements in Learning Environment: A Review. Procedia - Social and Behavioral Sciences, 36(June 2011), 287–294. https://doi.org/10.1016/j.sbspro.2012.03.032
  27. Hartman, C. J. (2022). Adult third culture kids: Impacts on adult lives of living internationally and attending boarding school. International Journal of Intercultural Relations, 86, 122–133. https://doi.org/10.1016/j.ijintrel.2021.11.007
  28. Hasan, S., & Bagde, S. (2013). The Mechanics of Social Capital and Academic Performance in an Indian College. American Sociological Review, 78(6), 1009–1032. https://doi.org/10.1177/0003122413505198
  29. Hata, M. (2017). Problems and Solutions in Real Analysis. In Series on Number Theory and Its Applications (Vol. 14). WORLD SCIENTIFIC. https://doi.org/10.1142/10110
  30. Hidayat, R., Syed Zamri, S. N. A., Zulnaidi, H., & Yuanita, P. (2020). Meta-cognitive behaviour and mathematical modelling competency: mediating effect of performance goals. Heliyon, 6(4). https://doi.org/10.1016/j.heliyon.2020.e03800
  31. Hock, K., & Fefferman, N. H. (2012). Social organization patterns can lower disease risk without associated disease avoidance or immunity. Ecological Complexity, 12, 34–42. https://doi.org/10.1016/j.ecocom.2012.09.003
  32. Hsieh, H. C., & Hsieh, H. L. (2019). Undergraduates’ out-of-class learning: Exploring EFL students’ autonomous learning behaviors and their usage of resources. Education Sciences, 9(3), 12–17. https://doi.org/10.3390/educsci9030159
  33. Imbrie, P. K., Lin, J. J. J., & Malyscheff, A. (2008). Artificial intelligence methods to forecast engineering students’ retention based on cognitive and non-cognitive factors. In ASEE Annual Conference and Exposition, Conference Proceedings. https://doi.org/10.18260/1-2--4315
  34. Jebaseelan, A. U. S. (2016). Student Learning Behavior and Academic Achievement - Unraveling its relationship Student Learning Behavior And Academic Achievement : Unraveling Its Relationship. September, 58. https://www.researchgate.net/publication/307964442_Student_Learning_Behavior_and_Academic_Achievement_-_Unraveling_its_relationship
  35. Jovanović, J., Saqr, M., Joksimović, S., & Gašević, D. (2021). Students matter the most in learning analytics: The effects of internal and instructional conditions in predicting academic success. Computers & Education, 172, 104251. https://doi.org/10.1016/j.compedu.2021.104251
  36. Khedher, N. ben, Kolsi, L., & Alsaif, H. (2021). A multi-stage SEIR model to predict the potential of a new COVID-19 wave in KSA after lifting all travel restrictions. Alexandria Engineering Journal, 60(4), 3965–3974. https://doi.org/10.1016/j.aej.2021.02.058
  37. Kiarie, J., Mwalili, S., & Mbogo, R. (2022). Forecasting the spread of the COVID-19 pandemic in Kenya using SEIR and ARIMA models. Infectious Disease Modelling, 7(2), 179–188. https://doi.org/10.1016/j.idm.2022.05.001
  38. Kline, R. B. (2016). Principles and practices of structural equation modelling 4th edition. In Methodology in the social sciences.
  39. Langford, P. E. (2004). Vygotsky's developmental and educational psychology. Psychology Press.
  40. Lanza Escobedo, D., & Sánchez Souto, V. (2015). Estrategias de aprendizaje en Educación Secundaria: un estudio comparativo sobre su uso entre alumnos españoles e inmigrantes. European Journal of Investigation in Health, Psychology and Education, 3(3), 227. https://doi.org/10.30552/ejihpe.v3i3.54
  41. Lekone, P. E., & Finkenstädt, B. F. (2006). Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study. Biometrics, 62(4), 1170–1177. https://doi.org/10.1111/j.1541-0420.2006.00609.x
  42. Lemay, D. J., Bazelais, P., & Doleck, T. (2021). Transition to online learning during the COVID-19 pandemic. Computers in Human Behavior Reports, 4, 100130. https://doi.org/10.1016/j.chbr.2021.100130
  43. Levinthal, C., Kuusisto, E., & Tirri, K. (2021). Finnish and portuguese parents’ perspectives on the role of teachers in parent-teacher partnerships and parental engagement. Education Sciences, 11(6). https://doi.org/10.3390/educsci11060306
  44. Liljeros, F., Edling, C. R., & Amaral, L. A. N. (2003). Sexual networks: Implications for the transmission of sexually transmitted infections. In Microbes and Infection (Vol. 5, Issue 2, pp. 189–196). https://doi.org/10.1016/S1286-4579(02)00058-8
  45. Liu, J., Ong, G. P., & Pang, V. J. (2022). Modelling effectiveness of COVID-19 pandemic control policies using an Area-based SEIR model with consideration of infection during interzonal travel. Transportation Research Part A: Policy and Practice, 161, 25–47. https://doi.org/10.1016/j.tra.2022.05.003
  46. Liu, J., Saeed, T., & Zeb, A. (2022). Delay effect of an e-epidemic SEIRS malware propagation model with a generalized non-monotone incidence rate. Results in Physics, 39, 105672. https://doi.org/10.1016/j.rinp.2022.105672
  47. Lohbeck, A., Schultheiß, J., Petermann, U., & Petermann, F. (2016). Factor structure and measurement invariance of the Students’ Self-report Checklist of Social and Learning Behaviour (SSL). Learning and Individual Differences, 51, 314–321. https://doi.org/10.1016/j.lindif.2016.08.038
  48. Magdalena, S. M. (2015). The Relationship of Learning Styles, Learning Behaviour and Learning Outcomes at the Romanian Students. Procedia - Social and Behavioral Sciences, 180(November 2014), 1667–1672. https://doi.org/10.1016/j.sbspro.2015.05.062
  49. Marta & Braselton. (2004). Differential Equations with Mathematica (Third Edit). Elasavier Academic Press.
  50. Mary, A., & Jebaseelan, A. U. S. (2014). Student learning behavior and academic achievement-unraveling its relationship. Social Science, 4(12), 57–59.
  51. Mazzetti, G., Paolucci, A., Guglielmi, D., & Vannini, I. (2020). The impact of learning strategies and future orientation on academic success: The moderating role of academic self-efficacy among Italian undergraduate students. Education Sciences, 10(5). https://doi.org/10.3390/educsci10050134
  52. Meyers, L. (2006). Applied Multivariate Research. SAGE Publicaton.
  53. Nagy, D. K., Hall, J. J., & Charrois, T. L. (2021). The impact of the COVID-19 pandemic on pharmacy students’ personal and professional learning. Currents in Pharmacy Teaching and Learning, 13(10), 1312–1318. https://doi.org/10.1016/j.cptl.2021.07.014
  54. Nevitt, J., & Hancock, G. (2001). Performance of Bootstrapping Approaches to Model Test Statistics and Parameter Standard Error Estimation in Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 8(3), 353–377. https://doi.org/10.1207/S15328007SEM0803_2
  55. Nikolovski, V., Trajanov, D., & Chorbev, I. (2021). LEARNING ANALYTICS - CASE STUDY: SEMANTIC AND SENTIMENT ANALYSES OF STUDENTS FEEDBACK IN HIGHER EDUCATION. 10191–10198. https://doi.org/10.21125/inted.2021.2125
  56. Ömeroğulları, M., Guill, K., & Köller, O. (2020). Effectiveness of private tutoring during secondary schooling in Germany: Do the duration of private tutoring and tutor qualification affect school achievement? Learning and Instruction, 66, 101306. https://doi.org/10.1016/j.learninstruc.2020.101306
  57. Opelt, F., & Schwinger, M. (2020). Relationships Between Narrow Personality Traits and Self-Regulated Learning Strategies: Exploring the Role of Mindfulness, Contingent Self-Esteem, and Self-Control. AERA Open, 6(3), 233285842094949. https://doi.org/10.1177/2332858420949499
  58. Pack, A. I., & Murray-Smith, D. J. (1972). Mathematical models and their applications in medicine. Scottish Medical Journal, 17(12), 401–409.
  59. Pandya, A., & Lodha, P. (2022). Mental health consequences of COVID-19 pandemic among college students and coping approaches adapted by higher education institutions: A scoping review. SSM - Mental Health, 2, 100122. https://doi.org/10.1016/j.ssmmh.2022.100122
  60. Penny. (2000). Elementary Differential Equation with Boudary Value Problems (Fourth). Prentice-Hall.
  61. Perko, L. (2013). Differential equations and dynamical systems (Vol. 7). Springer Science & Business Media.
  62. Perrusquía, A., Yu, W., & Li, X. (2021). Nonlinear control using human behavior learning. Information Sciences, 569, 358–375. https://doi.org/10.1016/j.ins.2021.03.043
  63. Pokhariyal, G. P., & Rodrigues, A. J. (1993). An accurate epidemiological model. Applied Mathematics and Computation, 53(1), 1–12. https://doi.org/10.1016/0096-3003(93)90112-R
  64. Politzer, R. L., & McGroarty, M. (1985). An Exploratory Study of Learning Behaviors and Their Relationship to Gains in Linguistic and Communicative Competence. TESOL Quarterly, 19(1), 103. https://doi.org/10.2307/3586774
  65. Prem, K., Cook, A. R., & Jit, M. (2017). Projecting social contact matrices in 152 countries using contact surveys and demographic data. PLoS Computational Biology, 13(9). https://doi.org/10.1371/journal.pcbi.1005697
  66. Rahiem, M. D. H. (2021). Remaining motivated despite the limitations: University students’ learning propensity during the COVID-19 pandemic. Children and Youth Services Review, 120, 105802. https://doi.org/10.1016/j.childyouth.2020.105802
  67. Rahimi, M. (2013). Is training student reviewers worth its while? A study of how training influences the quality of students’ feedback and writing. In Language Teaching Research (Vol. 17, Issue 1, pp. 67–89). https://doi.org/10.1177/1362168812459151
  68. Rahman, R. A., Mason, J. H., & Yusof, Y. M. (2012). Factors Affecting Students’ Change of Learning Behaviour. Procedia - Social and Behavioral Sciences, 56(Ictlhe), 213–222. https://doi.org/10.1016/j.sbspro.2012.09.648
  69. Rangasamy, M., Chesneau, C., & Martin-barreiro, C. (2022). SS symmetry On a Novel Dynamics of SEIR Epidemic Models with a Potential Application to COVID-19. 1–19.
  70. Read, J. M., Eames, K. T. D., & Edmunds, W. J. (2008). Dynamic social networks and the implications for the spread of infectious disease. Journal of the Royal Society Interface, 5(26), 1001–1007. https://doi.org/10.1098/rsif.2008.0013
  71. Sabato, H., Abraham, Y., & Kogut, T. (2021). Too Lonely to Help: Early Adolescents’ Social Connections and Willingness to Help During COVID‐19 Lockdown. Journal of Research on Adolescence, 31(3), 764–779. https://doi.org/10.1111/jora.12655
  72. Sacerdote, B. (2013). Social Networks and the Identification of Peer Effects Comment. In Journal of Business & Economic Statistics (Vol. 31, Issue 3, p. 275). AMER STATISTICAL ASSOC 732 N WASHINGTON ST, ALEXANDRIA, VA 22314-1943 USA.
  73. Sampath, S., & Bose, J. (2022). Modeling Effect of Lockdowns and Other Effects on India Covid-19 Infections Using SEIR Model and Machine Learning. In Lecture Notes in Networks and Systems: Vol. 417 LNNS (pp. 268–278). https://doi.org/10.1007/978-3-030-96302-6_25
  74. Saxena, C. (2002). Student Learning Behavior Scale. Arohi Manovigyan Kendra.
  75. Schmidt, M., Tawfik, A. A., Earnshaw, Y., & Jahnke, I. (2020). Learner and User Experience Research. EdTech Books.
  76. Schweder, S., & Raufelder, D. (2019). Positive emotions, learning behavior and teacher support in self-directed learning during adolescence: Do age and gender matter? Journal of Adolescence, 73(April), 73–84. https://doi.org/10.1016/j.adolescence.2019.04.004
  77. Seemiller, L. R., & Gould, T. J. (2020). The effects of adolescent alcohol exposure on learning and related neurobiology in humans and rodents. Neurobiology of Learning and Memory, 172, 107234. https://doi.org/10.1016/j.nlm.2020.107234
  78. Singh, M., James, P. S., Paul, H., & Bolar, K. (2022). Impact of cognitive-behavioral motivation on student engagement. Heliyon, 8(7), e09843. https://doi.org/10.1016/j.heliyon.2022.e09843
  79. Sitthirak, C. (2022). Impact of Interpersonal Relations and Positioning on the Resolution of Conflicts in the EFL Classroom. LEARN Journal: Language Education and Acquisition Research Network, 15(2), 751–775. https://api.elsevier.com/content/abstract/scopus_id/85134363599
  80. Stehlé, J., Voirin, N., Barrat, A., Cattuto, C., Isella, L., Pinton, J. F., Quaggiotto, M., van den Broeck, W., Régis, C., Lina, B., & Vanhems, P. (2011). High-resolution measurements of face-to-face contact patterns in a primary school. PLoS ONE, 6(8). https://doi.org/10.1371/journal.pone.0023176
  81. Stockmaier, S., Bolnick, D. I., Page, R. A., & Carter, G. G. (2018). An immune challenge reduces social grooming in vampire bats. Animal Behaviour, 140, 141–149. https://doi.org/10.1016/j.anbehav.2018.04.021
  82. Stoeger, H., Steinbach, J., Obergriesser, S., & Matthes, B. (2014). What is more important for fourth-grade primary school students for transforming their potential into achievement: the individual or the environmental box in multidimensional conceptions of giftedness? High Ability Studies, 25(1), 5–21. https://doi.org/10.1080/13598139.2014.914381
  83. Streicher, A., Schönbein, R., & Pickl, S. W. (2021). A General Framework and Control Theoretic Approach for Adaptive Interactive Learning Environments (pp. 243–257). https://doi.org/10.1007/978-3-030-64973-9_15
  84. Tabatabaie, S. M. H., Bolte, J. P., & Murthy, G. S. (2018). A regional scale modeling framework combining biogeochemical model with life cycle and economic analysis for integrated assessment of cropping systems. Science of The Total Environment, 625, 428–439. https://doi.org/10.1016/j.scitotenv.2017.12.208
  85. Tannert, S., & Gröschner, A. (2021). Joy of distance learning? How student self-efficacy and emotions relate to social support and school environment. European Educational Research Journal, 20(4), 498–519. https://doi.org/10.1177/14749041211024784
  86. Van de Velde, S., Buffel, V., van der Heijde, C., Çoksan, S., Bracke, P., Abel, T., Busse, H., Zeeb, H., Rabiee-khan, F., Stathopoulou, T., Van Hal, G., Ladner, J., Tavolacci, M., Tholen, R., & Wouters, E. (2021). Depressive symptoms in higher education students during the first wave of the COVID-19 pandemic. An examination of the association with various social risk factors across multiple high- and middle-income countries. SSM - Population Health, 16, 100936. https://doi.org/10.1016/j.ssmph.2021.100936
  87. Varachanon, P. (2015). Study of Learning Behaviors of Nursing Student at the Royal Thai Navy College of Nursing. Procedia - Social and Behavioral Sciences, 197(February), 1043–1047. https://doi.org/10.1016/j.sbspro.2015.07.313
  88. Velten, K. (2009). Mathematical modeling and simulation: introduction for scientists and engineers. John Wiley & Sons.
  89. Viola, T. W., & Nunes, M. L. (2022). Social and environmental effects of the COVID-19 pandemic on children. Jornal de Pediatria, 98, S4–S12. https://doi.org/10.1016/j.jped.2021.08.003
  90. Wang, S., Perry, M., Mingle, L., & McConney, M. (2020). Examining discourse structures in Chinese and U.S. elementary mathematics classes. International Journal of Educational Research, 99, 101493. https://doi.org/10.1016/j.ijer.2019.10.003
  91. Wang, X., & Zhang, W. (2022). Improvement of Students’ Autonomous Learning Behavior by Optimizing Foreign Language Blended Learning Mode. SAGE Open, 12(1). https://doi.org/10.1177/21582440211071108
  92. Wu, Y., Qi, L., Liu, Y., Hao, X., & Zang, S. (2021). Development and psychometric testing of a Learning Behaviour Questionnaire among Chinese undergraduate nursing students. BMJ Open, 11(6), 1–10. https://doi.org/10.1136/bmjopen-2020-043711
  93. Xia, X., & Qi, W. (2022). Temporal tracking and early warning of multi semantic features of learning behavior. Computers and Education: Artificial Intelligence, 3, 100045. https://doi.org/10.1016/j.caeai.2021.100045
  94. Xing, J., Leng, L., & Ho, R. T. H. (2021). Boarding school attendance and mental health among Chinese adolescents: The potential role of alienation from parents. Children and Youth Services Review, 127, 106074. https://doi.org/10.1016/j.childyouth.2021.106074
  95. Youkta, K., & Paramanik, R. N. (2021). Epidemiological model for India’s COVID-19 pandemic. Journal of Public Affairs, 21(4). https://doi.org/10.1002/pa.2639