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This bibliometric study aims to analyze publication trends, active countries, collaborations, influential citations and thematic evolution in learning analytics (LA) research focused on higher education (HE) during and after the COVID-19 lockdown period.

From the Scopus database, this bibliometric analysis extracts and evaluates 609 scholarly articles on LA in HE starting in 2019. The multidimensional process identifies the scope impacts, advancing the understanding of LA in HE. An analysis of co-citation data uncovers the key influences that have shaped the literature. This study uses the stimulus-organism-response (SOR) theory to suggest future research directions and organizational adaptations to new LA technologies and learner responses to LA-enabled personalized interventions.

Learning analytics are becoming important in the HE environment during and after the COVID-19 lockout. Institutions have used LA to collect socio-technical data from digital platforms, giving them important insights into learning processes and systems. The data gathered through LA has assisted in identifying areas for development, opening the path for improved student success and academic performance evaluation and helping students transition to the workforce.

The study’s concentration on the post-COVID-19 timeframe may lead to paying attention to potential pandemic developments. Nonetheless, the findings provide a thorough picture of LA’s contributions to HE and valuable ideas for future study initiatives. Future research with the SOR framework suggests areas for additional study to maximize LA’s potential in diverse HE situations.

This study adds to the growing corpus of knowledge on learning analytics in HE, especially in light of the COVID-19 lockdown and its aftermath. By using bibliometric analysis, the study provides a complete and evidence-based understanding of how LA has been used to address challenges related to HE. This study uses bibliometric analysis and SOR theory to appraise and map HE learning analytics research. The selected study themes can help scholars, educators and institutions shape their future efforts to improve teaching, learning and support mechanisms through learning analytics.

Population ageing is a demographic transition and burdens every country’s health sector. A lack of social security combined with low government health-sector spending has made health-care utilisation difficult among elderly in many developing countries like India. This makes population ageing a global phenomenon for policymakers, researchers and stakeholders to discuss and debate in 21st century. The purpose of this paper is to examine the behavioural determinants of health-care utilisation among elderly population in Punjab.

This study examines health-care utilisation among the elderly population in Punjab. A structured questionnaire was used to collect the data in the state of Punjab, with a multi-stage stratified random sample of 385 respondents. Logit regression was used to analyse the association between several behavioural factors like age, gender, place of residence, source of livelihood, education and frequency of visits with health-care utilisation among elderly aged 60 and above.

This study revealed that gender, age, place of residence, source of livelihood, education and frequency of visits were significant determinants of health-care utilisation among elderly population.

This study directs the need to improve health-care utilisation in a country, reducing inequalities that exist among the elderly population with respect to socio-economic and demographic differences.