Phal Chea

The study applies the Hierarchical Linear Modeling（HLM）approach using the international learning assessment survey, PISA for Development (PISA-D), to examine
the effects of demand-side and supply-side factors associated with academic performance in Cambodia. Findings from the study suggest that students’ characteristics and family background are good predictors of students’ test scores in Cambodia, while demand-side factors such as school size, class size, student grouping by ability, remedial class, and teacher absenteeism have little or no influence on student learning performance. The study also highlights how these predictors affect learning performance differently in rural and urban areas.

This paper intends to review education-related datasets, including data from household surveys, learning assessments and field experiments, publicly available for researchers and students interested in conducting education research. It also presents ideas on how those data can be used in empirical studies and identifies some major potential sources of those datasets. Issues in education have shifted from access to quality learning and at the same time, randomized control trial (RCT) has become the gold standard in measuring the impacts of education programs. The paper also notices the emerging field of educational data mining employed to predict student performance or to identify at-risk students.

This paper provides empirical evidence on the trends of how demand-side factors predict the probabilities of enrollment in higher education in Cambodia between 2004 and 2014, using nationally representative household survey data with multinomial logistic regression approach. The findings suggest that higher education expansion in Cambodia is in favor of students from affluent families residing in the capital. However, students of disadvantaged backgrounds are likely to benefit more from the higher education expansion, given those students can finish high school.