Emotional Intelligence in Relation to Academic Motivation among Higher Secondary School Students: An Empirical Investigation in District Baramulla, Jammu and Kashmir
Abstract
Emotional Intelligence (EI) is increasingly viewed as an important psychological resource for students' learning, adjustment, and sustained academic engagement. However, the relationship between EI and Academic Motivation may depend on the social and educational context in which students learn. The present study examined the relationship between EI and Academic Motivation among 200 students of Classes XI and XII enrolled in Government and Private higher secondary schools of District Baramulla, Jammu and Kashmir. A quantitative, descriptive-correlational design was employed, and multistage stratified random sampling was used to select the participants. EI was assessed through the Schutte Self-Report Emotional Intelligence Test (SSEIT), while Academic Motivation was measured using the Academic Motivation Scale adapted by Stover et al. (2012). Data were analysed using descriptive statistics, Pearson's product-moment correlation, and independent-samples t-tests. Students reported high levels of EI (M = 123.84, SD = 14.62) and Academic Motivation (M = 104.92, SD = 13.28). EI was positively associated with Academic Motivation, r = .648, p < .001. The correlation corresponds to approximately 42% shared variance, although the cross-sectional design does not permit causal interpretation. Private-school students reported higher EI and Academic Motivation than Government-school students, with moderate standardized mean differences. The findings are broadly consistent with recent international evidence but also highlight the need to interpret the relationship through contextual variables such as school climate, teacher support, socioeconomic conditions, academic stress, and experiences of educational disruption. The study concludes with context-sensitive implications for school practice and a future research agenda involving moderation, mediation, multilevel modelling, longitudinal designs, and multiple data sources.