Applying Genetic Programming to Predict the Final Grade of Students from an Online Higher Education Course

Abstract

In this research I consider the following three issues as the context for online higher education (OHE): (1) Higher education (HE) has a steady enrolment growth [1, 2] and budget issues are becoming structural and survival matters for universities and HE institutions [3]. Moreover, HE institutions (HEI) are required to pay attention of the society needs regarding to the demands of the workplace according to international organizations, which are considered as stakeholders, assessors and certifying bodies of HEIs [4, 5, 6, 7]: HEIs should meet international organizations quality criteria; and OHE as a subsystem within HE system is embedded in this environment. (2) Most of the OHE offers are dealing with self-sustainability issues and are experiencing an enrolment growth [8, 9]. Thus, the increasing of economic resources is a matter of concern for the OHEs. (3) Investment is dependent on evaluation [10], and based on indicators such as failure, success, and terminal efficiency; these three indicators are quality issues for OHE [11, 12], they become important in order to sustain and increase the inflow of resources. Furthermore, economic indicators are related to knowledge production, long term education, and training of human resources of a country: these indicators are affected by student success, failure, retention, dropout, and graduation rates among other educational indicators. OHE as an extension of HE [7] has a similar behavior to HE: the enrolment growth, failure, success and dropout rates have comparable patterns. However, OHE has its own challenges and characteristics, which I describe in the following sections. Failure rates are high in OHE [13]: to deal with this major issue I will link the field of information technologies with the educational field; my contribution is to propose a solution to handle one of the indicators that are important for the development of the OHE institutions and programs: the student failure rate.