Analysis of Sex Education Learning Methods for Early Childhood: The Role of Parents and Family
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Background. Early childhood education plays a critical role in shaping children's values and behaviors. Sex education, a fundamental aspect of life skills education, is increasingly recognized as an important subject for young children. However, how to effectively teach sex education to early learners remains a complex challenge, with particular emphasis on the roles of parents and family in this educational process.
Purpose. This study aims to analyze the effectiveness of various learning methods for sex education in early childhood, focusing on the involvement of parents and the family as primary educators. Specifically, the research explores how different approaches to teaching sex education can influence young children's understanding of gender roles, body awareness, and respect for personal boundaries.
Method. The study utilized a mixed-methods design, surveying 200 parents and educators in early childhood institutions. Additionally, interviews and focus groups were conducted to gather qualitative insights into the role of family dynamics in the implementation of sex education programs. The data collected were analyzed using both statistical and thematic analysis.
Results. The results indicated that children whose families were more actively involved in discussions about sex education demonstrated higher levels of body awareness and respect for privacy. Furthermore, families that used age-appropriate, interactive methods such as storytelling, role-playing, and guided discussions were found to have a positive impact on children's understanding of sex education topics.
Conclusion. This study underscores the significant role of parents and family in early childhood sex education. It suggests that effective sex education for young children requires a collaborative approach between educators and families, incorporating both structured and informal learning methods. Future research should focus on developing comprehensive sex education frameworks that support family involvement in various cultural contexts.
Abdelwahab, M. M. (2024). Autism Spectrum Disorder Prediction in Children Using Machine Learning. Journal of Disability Research, 3(1). https://doi.org/10.57197/JDR-2023-0064
Ahmad, I. (2024). Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks. Healthcare Technology Letters, 11(4), 227–239. https://doi.org/10.1049/htl2.12073
Ari, B. (2022). Accurate detection of autism using Douglas-Peucker algorithm, sparse coding based feature mapping and convolutional neural network techniques with EEG signals. Computers in Biology and Medicine, 143(Query date: 2026-04-28 22:32:56). https://doi.org/10.1016/j.compbiomed.2022.105311
Barratt, M. J. (2022). Bifidobacterium infantis treatment promotes weight gain in Bangladeshi infants with severe acute malnutrition. Science Translational Medicine, 14(640). https://doi.org/10.1126/scitranslmed.abk1107
Behnamnia, N. (2023). A review of using digital game-based learning for preschoolers. Journal of Computers in Education, 10(4), 603–636. https://doi.org/10.1007/s40692-022-00240-0
Cay, M. (2022). Childhood maltreatment and its role in the development of pain and psychopathology. Lancet Child and Adolescent Health, 6(3), 195–206. https://doi.org/10.1016/S2352-4642(21)00339-4
Cunningham, S. A. (2022). Changes in the Incidence of Childhood Obesity. Pediatrics, 150(2). https://doi.org/10.1542/peds.2021-053708
Dai, D. L. Y. (2023). Breastfeeding enrichment of B. longum subsp. Infantis mitigates the effect of antibiotics on the microbiota and childhood asthma risk. Med, 4(2), 92–112. https://doi.org/10.1016/j.medj.2022.12.002
Eskenazi, B. (2023). Association of Lifetime Exposure to Glyphosate and Aminomethylphosphonic Acid (AMPA) with Liver Inflammation and Metabolic Syndrome at Young Adulthood: Findings from the CHAMACOS Study. Environmental Health Perspectives, 131(3). https://doi.org/10.1289/EHP11721
Garot, E. (2022). An update of the aetiological factors involved in molar incisor hypomineralisation (MIH): A systematic review and meta-analysis. European Archives of Paediatric Dentistry, 23(1), 23–38. https://doi.org/10.1007/s40368-021-00646-x
Grummitt, L. R. (2022). Associations of childhood emotional and physical neglect with mental health and substance use in young adults. Australian and New Zealand Journal of Psychiatry, 56(4), 365–375. https://doi.org/10.1177/00048674211025691
Gupta, C. (2022). Bringing machine learning to research on intellectual and developmental disabilities: Taking inspiration from neurological diseases. Journal of Neurodevelopmental Disorders, 14(1). https://doi.org/10.1186/s11689-022-09438-w
Gupta, S. (2025). Blinatumomab in Standard-Risk B-Cell Acute Lymphoblastic Leukemia in Children. New England Journal of Medicine, 392(9), 875–891. https://doi.org/10.1056/NEJMoa2411680
Hou, H. (2022). Childhood Experiences and Psychological Distress: Can Benevolent Childhood Experiences Counteract the Negative Effects of Adverse Childhood Experiences? Frontiers in Psychology, 13(Query date: 2026-04-28 22:32:56). https://doi.org/10.3389/fpsyg.2022.800871
Lin, C. (2024). A scoping review of social determinants of health’s impact on substance use disorders over the life course. Journal of Substance Use and Addiction Treatment, 166(Query date: 2026-04-28 22:32:56). https://doi.org/10.1016/j.josat.2024.209484
Liu, C. (2022). Association of both prenatal and early childhood multiple metals exposure with neurodevelopment in infant: A prospective cohort study. Environmental Research, 205(Query date: 2026-04-28 22:32:56). https://doi.org/10.1016/j.envres.2021.112450
Liu, Y. (2023). Associations of Gestational Perfluoroalkyl Substances Exposure with Early Childhood BMI z-Scores and Risk of Overweight/Obesity: Results from the ECHO Cohorts. Environmental Health Perspectives, 131(6). https://doi.org/10.1289/EHP11545
Madan, N. (2022). Artificial intelligence and imaging: Opportunities in cardio-oncology. American Heart Journal Plus Cardiology Research and Practice, 15(Query date: 2026-04-28 22:32:56). https://doi.org/10.1016/j.ahjo.2022.100126
Mallawaarachchi, S. R. (2022). Associations of smartphone and tablet use in early childhood with psychosocial, cognitive and sleep factors: A systematic review and meta-analysis. Early Childhood Research Quarterly, 60(Query date: 2026-04-28 22:32:56), 13–33. https://doi.org/10.1016/j.ecresq.2021.12.008
Midya, V. (2022). Association of Prenatal Exposure to Endocrine-Disrupting Chemicals with Liver Injury in Children. JAMA Network Open, 5(7). https://doi.org/10.1001/jamanetworkopen.2022.20176
Moira, A. P. de. (2022). Associations of early-life pet ownership with asthma and allergic sensitization: A meta-analysis of more than 77,000 children from the EU Child Cohort Network. Journal of Allergy and Clinical Immunology, 150(1), 82–92. https://doi.org/10.1016/j.jaci.2022.01.023
Moridian, P. (2022). Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review. Frontiers in Molecular Neuroscience, 15(Query date: 2026-04-28 22:32:56). https://doi.org/10.3389/fnmol.2022.999605
Papadakis, D. S. (2022). Apps to Promote Computational Thinking and Coding Skills to Young Age Children: A Pedagogical Challenge for the 21st Century Learners. Educational Process International Journal, 11(1), 7–13. https://doi.org/10.22521/edupij.2022.111.1
Parsons, D. W. (2022). Actionable Tumor Alterations and Treatment Protocol Enrollment of Pediatric and Young Adult Patients with Refractory Cancers in the National Cancer Institute-Children’s Oncology Group Pediatric MATCH Trial. Journal of Clinical Oncology, 40(20), 2224–2234. https://doi.org/10.1200/JCO.21.02838
Qasrawi, R. (2022). Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis. Jmir Formative Research, 6(8). https://doi.org/10.2196/32736
Rafiee, F. (2022). Brain MRI in Autism Spectrum Disorder: Narrative Review and Recent Advances. Journal of Magnetic Resonance Imaging, 55(6), 1613–1624. https://doi.org/10.1002/jmri.27949
Sandbank, M. (2023). Autism intervention meta-analysis of early childhood studies (Project AIM): Updated systematic review and secondary analysis. BMJ, 383(Query date: 2026-04-28 22:32:56). https://doi.org/10.1136/bmj-2023-076733
Sathishkumar, K. (2022). Cancer incidence estimates for 2022 & projection for 2025: Result from National Cancer Registry Programme, India. Indian Journal of Medical Research, 156(4), 598–607. https://doi.org/10.4103/ijmr.ijmr_1821_22
Schwarzer, C. (2022). Associations of media use and early childhood development: Cross-sectional findings from the LIFE Child study. Pediatric Research, 91(1), 247–253. https://doi.org/10.1038/s41390-021-01433-6
Sripada, K. (2022). A Children’s Health Perspective on Nano-and Microplastics. Environmental Health Perspectives, 130(1). https://doi.org/10.1289/EHP9086
Su, J. (2022). Artificial Intelligence (AI) in early childhood education: Curriculum design and future directions. Computers and Education Artificial Intelligence, 3(Query date: 2026-04-28 22:32:56). https://doi.org/10.1016/j.caeai.2022.100072
Su, J. (2023a). A systematic review of integrating computational thinking in early childhood education. Computers and Education Open, 4(Query date: 2026-04-28 22:32:56). https://doi.org/10.1016/j.caeo.2023.100122
Su, J. (2023b). Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities. Computers and Education Artificial Intelligence, 4(Query date: 2026-04-28 22:32:56). https://doi.org/10.1016/j.caeai.2023.100124
Su, J. (2024). AI literacy curriculum and its relation to children’s perceptions of robots and attitudes towards engineering and science: An intervention study in early childhood education. Journal of Computer Assisted Learning, 40(1), 241–253. https://doi.org/10.1111/jcal.12867
Talukdar, J. (2023). A comparative assessment of most widely used machine learning classifiers for analysing and classifying autism spectrum disorder in toddlers and adolescents. Healthcare Analytics, 3(Query date: 2026-04-28 22:32:56). https://doi.org/10.1016/j.health.2023.100178
Whiteley, W. N. (2022). Association of COVID-19 vaccines ChAdOx1 and BNT162b2 with major venous, arterial, or thrombocytopenic events: A population-based cohort study of 46 million adults in England. Plos Medicine, 19(2). https://doi.org/10.1371/journal.pmed.1003926
Yang, W. (2024). Artificial intelligence education for young children: A case study of technology-enhanced embodied learning. Journal of Computer Assisted Learning, 40(2), 465–477. https://doi.org/10.1111/jcal.12892
Yu, J. (2022). Adverse childhood experiences and premature mortality through mid-adulthood: A five-decade prospective study. Lancet Regional Health Americas, 15(Query date: 2026-04-28 22:32:56). https://doi.org/10.1016/j.lana.2022.100349
Zhang, T. (2023). Adverse childhood experiences and their impacts on subsequent depression and cognitive impairment in Chinese adults: A nationwide multi-center study. Journal of Affective Disorders, 323(Query date: 2026-04-28 22:32:56), 884–892. https://doi.org/10.1016/j.jad.2022.12.058
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