## 1 Introduction and Motivation  
Educational Data Mining (EDM) is a fast-growing scientific field offering the potential to analyze a variety of student features to harness valuable knowledge from them. To this end, a plethora of predictive algorithms were effectively applied in educational contexts for numerous purposes using a variety of data and student records. As compiled in the review paper [1], two main application purposes can be identified in the college contexts: predictors and early warning systems (EWS). A predictor, \"given a specific set of input data, aims to anticipate the outcome of a course or degree\" [1], and a EWS \"performs the same tasks as a predictor, and reports its findings to a teacher and/or to students at an early enough stage so that measures can be taken to avoid or mitigate potentially negative outcomes\" [1]. Common prediction goals are listed as risk of failing a course, dropout risk, grade prediction, and graduation rate. Among the various prediction goals, prediction of academic performance at graduation time especially, is of tremendous importance, as this information can be useful for:* Enabling the educational institution to identify students (not) likely to complete the program and help in their admission decisions,
* Identifying students at risk and provide adequate advising and tailored help towards reduced failure rates,
* Detecting high achiever students and help them enhance their career paths,
* Analyzing factors of key importance and mobilize educational efforts for continuous quality improvements. Looking at the literature on prediction of academic performance at the graduation time, we can observe that all studies rely mainly on four types of information on students, namely: (1) demographics and socio-economic, (2) high-school related, (3) college enrollment, and (4) college performance (up to the time of prediction). Commonly used demographics and socio-economic information are sex/race [2], household income [3], age, first generation student [4], marital status, parents' jobs and educational levels [2]. Among the high-school related information, high-school GPA [2], pre-college marks [5,6], college admission test scores [3], public or private high-school [2], are frequently observed. As college related, in terms of enrollment information, the major and campus [2], a student's full-time vs. part-time status as well as whether s/he has a scholarship [3], enrolled hours and earned credit hours [4], year of entry and program [2,7] are often used. Finally, we observe that college performance has mostly been represented with grades from courses taken earlier [2,4,6,8], unless the prediction model is meant to be used at admission time [3,8,9]. Based on the above background, we observe that the bulk of previous studies used datasets with relatively large dimensionality of observations, some of them being expensive to measure (when not already available in the records). This, often combined with small samples, caused the curse of dimensionality, potentially yielding models with sub-optimal predictive power. Recently, there is a trend to use only college performance [8,9], or using courses average per semester instead of individual courses grades (i.e., GPA per semester, or CGPA for cumulative average at time of prediction) [2,7,4]. However, there is no study comparing the performance of EDM models using individual course grades vs. grade point averages. It is unknown whether these two college performance representations are equivalent and can be used interchangeably, or if one is superior to the other in yielding better predictive power.