Abstract:

In the early 1990s, the emergence of online learning formed the conception of blended learning (Senge, 2006). Blended courses, which combine online and face-to-face delivery, can affect learners' learning experiences and outcomes, and ultimately their academic performance. Garnham and Kaleta (2002) found that learners in blended courses obtained improved grades than did their counterparts in face-to-face or online courses. López-Pérez et al. (2011) also found that blended courses achieved decreased attrition rates and improved examination scores. Poon (2013) explained that blended courses benefited learners and institutions as they improved learners’ learning experience and effective use of resources. Many studies on blended courses are skewed towards definitions, models and the potential of blended courses (Halverson et al., 2012). Not many research studies have been done on the evaluation of factors that affect the performance of learners in blended courses. Prior studies are conducted mainly on full-time students who are fresh school leavers. Such being the case, this study is timely in filling the gaps in literature.

This study examines factors affecting the performance of adult learners in blended courses vis-à-vis face-to-face courses in the Singapore University of Social Sciences (SUSS is formerly known as SIM University). SUSS is a university that caters primarily to working adults with a mission to provide lifelong education that equips learners to serve society. The study performs a cross-sectional analysis of blended and face-to-face courses, with the average final score of the learners in each course as the variable of interest. This analysis is done at a course level, with variables such as the discipline (e.g., accountancy, finance, sociology), nature (i.e., qualitative, quantitative or mixed), assessment method (e.g., written or project) and level (i.e., beginner, intermediate or advanced). Data mining techniques such as decision trees and logistics regression are used to evaluate learner’s performance and its determinants in blended and face-to-face courses. Hence, this study can provide a different perspective as it focuses on working adult learners in part-time higher education programmes. With insights on the factors that affect the performance of learners in blended vis-à-vis face-to-face courses, universities can better design their courses to maximise the benefits of both blended and face-to-face courses.

Future research can consider the role of faculty and course assessment as well as learners’ attributes (e.g., demographics and prior knowledge of the subject) in comparing the learners’ learning experience and academic performance. Furthermore, studies can be expanded to a larger dataset to include more courses and learners to enhance the generalisability of the findings. It is hoped that this study will provide deeper insights into the effect of the mode of course delivery on the learning experience and academic performance of learners, and the determinants of such effects.

Keywords:

Adult learners, blended learning, learning analytics, data mining.