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Demographic Factors Influencing Students’ Academic Achievement

Friday 5 July: Conference day three, 10:30am – 11:00am parallel session



Room 4 – 303-G16, Sem



Dr Theresa Kwong
Hong Kong Baptist University, Hong Kong

Dr Isaac Chan
Hong Kong Baptist University, Hong Kong

Muhammad Hafiz
Hong Kong Baptist University, Hong King



Numerous factors such as pre-admission examination scores and demographic factors such as age, gender, race and ethnicity have been identified to predict students’ learning and academic success. However, the efficacy of these predictors seemed to be inconsistent. Despite its inconsistencies, standardized tests and pre-admission examination scores continue to be used as means of university admission screening. It is postulated that students with high grades are able to cope with the academic demands of higher education successfully. Demographic factors such as age, gender, race and ethnicity and academic success have also been posited to predict students’ academic success. However, the efficacy of these predictors is also in doubt.

Universities in Hong Kong transited from a 3-year curriculum to a 4-year curriculum programme (4YC) in 2012. The graduation of the first cohort of the 4YC in 2015/16 provided an opportunity to examine the academic performance of graduates under the revised curriculum. It is also an opportune time to revisit the debate on factors associated with academic achievements in the context of a liberal arts university in Hong Kong.

The current study is intended to identify significant predictors of academic achievement in a Hong Kong university. Specifically, the relationship between pre-admission examination scores and demographic factors with students’ final cumulative grade point average (cGPA) was examined. A total of over 6500 students’ information and data were analysed. Linear regression and other parametric statistics analyses, e.g. correlation, was performed. Findings from the analyses showed that gender and nationality were significant predictors of students’ cGPA. Age, however, was not a significant predictor. A detailed breakdown of the analyses will also be shared in the presentation.

These findings provided valuable insights into the significant contributors to students’ academic success. It also serves as an illustration on how universities could use big data analytics to indirectly enhance student learning and academic success. The captured institutional dataset could be useful in the identification of strategies towards students’ academic success. Having an overall picture on the students who could be academically at risk during the course of undergraduate studies allows the policy makers to introduce both direct and indirect interventions to get them back on track. A contextual approach to enhance student learning and academic success could be useful since most universities are unique and different from each other.


Session Outline

At the end of this session, participants will be able to:

  • Identify demographic factors that influence students’ academic success
  • Discuss possible implication of the findings in the context of higher education

The session will be divided into following sections:

Part 1: Rationale and Introduction (5 min)
Part 2: Current study, including research questions and methodology (10 min)
Part 3: Discussion and implication of findings (5 min)
Part 4: Engaging the participants, take home message and conclusion (5 min)


Presentation topic

Students – Well-being and success

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