## Discussion
### Key Findings
Our first observation was that the synthesized literature used the term'student outcomes' or 'learning outcomes' incautiously, without adopting or linking them to any formal definition. The rather vague definition of the predicted variable (i.e., learning outcomes) by the predictive models was considered a major weakness that raises concerns about the usefulness and validity of the learning analytics results. Therefore, it is important for the researchers to clearly define the student learning outcomes variable that their intelligent models would estimate. Our next observation was that most experimental datasets came from a single educational entity, and 35% of studies predicted the learning outcomes for no more than four courses. The datasets used to train the predictive models were relatively small in many studies, with a sample size less than 1000 students. Notably, most surveyed models attempted to predict outcomes at the course level (90%). The academic performance was mostly measured for individual students instead of cohorts. Only a few studies modeled program-level outcomes. The predictions of educational outcomes were made both during and at the end of the semester. However, the projections focused on the direct measures of student performance more than the student perceptions of the learning process. Generally, the developed models analyzed student data to predict the learning outcomes in their variant forms, including student achievements, dropout and at-risk rates, and feedback and recommendation. Approximately 34 studies forecasted student performance in the form of academic standing classes (majority from two to four academic classes). Education program assessment is the epicenter activity undertaken to achieve a myriad of strategic goals [17], such as the improvement of program quality and realization of outcome-based education. Usually, student performance, whether assessed directly (e.g., examinations) or indirectly (e.g., student self-reports) is measured using rubrics. Rubrics can be considered the equivalent of academic performance classes to evaluate whether the learning outcomes fulfill certain thresholds or attainment levels. * RQ2-Academic Performance Prediction Approaches. What intelligent approaches and techniques are devised to forecast student academic performance using learning outcomes? Although the number of publications in the field of educational data mining is growing yearly [109; 110], the research efforts focused on developing models that can estimate learning outcomes are still unsatisfying. For instance, many outcome assessment tools lack sufficient intelligence to predict student performance [15]. In our survey, we found that the performance prediction models were developed, in most cases, as stand-alone modules and not part of a program assessment software. About 87.70% of the devised models relied on a single intelligent technique, even though the ensemble techniques are well-known to boost the prediction accuracy [3]. Moreover, fewer models were augmented to explain and justify the prediction of learning outcomes, despite their importance [111]. Nearly 86% of the synthesized models fall within the statistical modeling and super machine learning. Only a few models tried to forecast student outcomes using unsupervised learning techniques. We used the taxonomy presented in [109] to classify the predictive techniques emerging from our synthesis. Regression, neural network, and tree-based models were the most used classification techniques for predicting the attainment of student learning outcomes. Accuracy was the most calculated metric for evaluating the performance of the predictive models. Other evaluation metrics reported included RMSE, ROC-AUC, R square, and MAE. The best performing predictive models were the Hybrid Random Forest, Feedforward 3-L Neural Network, and Naive Bayes, while the worst-performing models were the Linear Regression and Mixed-effects Logistic Regression.