# Systematic Literature Review of Predictive Analysis Tools in Higher Education +
## 3 Results
### Predictors
This predictor was tested in a first-year engineering course at an Australian institution, involving 1147 students across three different academic years. The study concluded that course-specific indicators had more predictive power than generic ones. The greatest R\({}^{2}\) value achieved was 0.377. [PERSON] and [PERSON] focused on studying the influence of demographic and socioeconomic factors over performance of undergraduate students [20]. The study was aimed at providing predictions of student success, which could potentially be used to enable early interventions targeting students at risk, supporting the decision making of instructors and advisors in the process. Two different prediction goals were set for this predictor, both indicative of student success. On the one hand, the students' cumulative GPA for the entire degree program was estimated using stepwise ordinary least squares (OLS) regression. On the other hand, logistic regression was applied to determine the probability that a student graduates within six years of entering university, which the authors word as the student being \"retained\". As input data, the predictive model received the gender and ethnicity of students as demographic indicators, and their household income and whether they are financially independent or not as socioeconomic indicators. Additionally, data regarding their high school grades and their status as full-time or part-time students were also provided. The predictive models were tested over a population of 1919 undergraduate students enrolled in a business program at a public university in Tennessee (USA). The OLS regression model used for predicting cumulative GPA yielded an adjusted R\({}^{2}\) value of 0.287, and determined that high school GPA was by far the variable with the highest impact over final GPA in the degree program. As for the logistic regression model, grades were once again the most important factor, and the logistic regression model was able to classify students into \"retained\" and \"not retained\" categories with an accuracy of 82%. In both cases, some of the demographic and economic factors were statistically significant (such as gender, ethnicity, or status as financially independent), although their impact over prediction outcomes was significantly lower than the one associated to grades. [PERSON] studied the impact of the quality and quantity of students' note-taking over their academic performance [21]. This study was done in the context of a first-year general psychology course at a Taiwanese university, involving 38 students. Both in-class and after-class notes were collected and copied by the professor at the end of each lecture. Note quantity was assessed in terms of number of Chinese characters, while the professor rated the quality of notes based on their accuracy and completeness regarding the actual contents of each lecture. These data were processed using hierarchical regression in order to estimate final test scores. As a result of this experiment, it was determined that the quality of notes was a significant predictor of test scores. However, quantity of notes was not related to performance in any meaningful way. An R\({}^{2}\) value of 0.3 was obtained in this study. [PERSON] and [PERSON] investigated the effects of stress overload--defined as the destructive form of stress--over the GPA obtained by a student in one semester [22]. To assess the students' level of stress, they were asked to fill a survey halfway through the semester, including questions regarding perceived burden of demands and insufficiencies in resources. This allowed stress to be quantified with the help of a \"stress overload scale\", defined by the authors. Stress scores, alongside student demographic characteristics, were the input data fed to the system. The predictor had two main tasks: first, proving a relationship between stress overload and academic failure using structural equation modeling (SEM), and then, determining the predictive power of stress scores using path analysis.