Modeling Students' Performances in Activity-Based E-Learning From a Learning Analytics Perspective: Implications and Relevance for Learning Design

Introduction

A number of researchers, practitioners and e-learning developers have recently been directing significant resources to demonstrate how learning analytics can help in better course design, improve student success and completion rate, and to improve performances. These are achieved through systematic and in-depth analysis of student data that is generated and/or stored in e-learning environments and platforms. Such data are mainly related to platform login frequencies, online content browsing, completion of drill and practice activities, frequency of online interaction with peers and tutors, and submission of coursework online (Retalis, Papasalouros, Psaromiligkos, Siscos and Kargidis, 2006; Johnson, Smith, Willis, Levine and Haywood, 2011). There are also other variables that may be stored depending on the learning design approaches such as learning styles and preferences, cultural factors, student feedback and satisfaction surveys. There are four types of analytics as reported in the literature, namely descriptive, diagnostic, predictive and prescriptive analytics (Gartner, 2012).

Literature suggests that many reported studies on learning analytics focus on classic elements such as frequency of access, time spent online, and number of online posts as key variables to be used as predictors of performance, student success and also the identification of students who are at risk, and most recommendations regarding learning design would be in line with what is normally referred to as the classic e-learning design model. The classic model is referred to as a course which is centred around the dissemination of online content, such as on-screen text, images, and videos where the maximum of the learning process happens online, and on the e-learning platform (Santally, Rajabalee and Cooshna-Naik, 2012). On the other hand, in line with Nichols (2003), the Web can also be seen as a way to transform the teaching and learning process. Schneider, Synteta, Frété, Girardin and Morand (2003) argued that traditional e-learning courses as described above, are not designed to achieve the development and acquisition of skills and competencies, and the same could be achieved, through online project-based learning, along with a redefinition of the role of the teacher. As such, an online course may be composed of a set of learning activities, which may or may not be completed online, depending on the intrinsic learning design and expected outcomes of each activity, and the learning domains might span and overlap different spheres of the cognitive, psychomotor and affective domains depending on the subject being taught. In such settings, applying learning analytics on the variables such as frequency of access, and online interaction alone may not suffice.

In this research, the aim was to explore the potential, constraints and impacts of a learning analytics approach to an online course that does not follow the conventional learning design approach, and that has been offered to first year undergraduate students as a General Education Module at the University of Mauritius for two consecutive academic years. There were 217 students in the first cohort and 844 students in the second cohort. The module title was “Education Technologies” and was destined to those having an interest to later take up a teaching career focusing on the integration of Information and Communication Technologies in Education. The module was designed using the activity-based approach as advocated by Schneider et al. (2003) and the learning design followed the hybrid three phased-model of knowledge acquisition, knowledge application and knowledge construction and reflection (Santally and Senteni, 2006; Santally and Raverdy, 2006; Santally et al, 2012). This model lays emphasis on the need to conceptualise learner-centred educational experience through activity-based learning, which leads to authentic learning outcomes and acquisition of competencies (Fernando, 2018; Reid, 2017; Morcke, Dornan and Eika, 2013). Literature in learning analytics studies do not really cover modules or courses that employ a learning designs similar to the module that is the subject of the current research.