## 3 Survey Methodology  
This research performed a systematic review where the relevant academic works predicting student performance using learning outcomes were identified, selected, and critically evaluated using several criteria, as presented in the results section. To streamline our contributions, we formulated three key research questions as follows:  
* RQ1-Learning Outcomes Prediction. How is student academic performance measured using learning outcomes? * RQ2-Academic Performance Prediction Approaches. What intelligent models and techniques are devised to forecast student academic performance using learning outcomes? * RQ3-Academic Performance Predictors. What dominant predictors of student performance using learning outcomes are reported? The main objective of this survey was to create a comprehensive understanding of the landscape of academic performance prediction by focusing on the attainment of learning outcomes. To answer the above research questions accurately, we adopted the well-founded PICO model [41]. The PICO protocol emphasizes the definition of four key elements, namely population, intervention, comparison, and outcome. Concerning our research, population refers to the investigation of the learning outcomes prediction studies, the intervention refers to the intelligent approaches and factors used to predict the attainment of student outcomes, comparison refers to the performance prediction variability between the surveyed models, and outcome refers to the accuracy of these approaches as well as the predictors of learning outcomes. Table 2 details the PICO elements of our survey. Moreover, we applied the best practices for conducting useful systematic reviews [10]. As such, we identified and searched seven major online bibliographic databases, which contain engineering and science publications. These databases include the ACM digital library, IEEE Xplore, Google Scholar, Science Direct, Scopus, Springer, and Web of Science. These are the common databases searched by software engineering reviews and are expected to incorporate the studies investigating the predictive modeling of student outcomes. Other electronic databases, such as DBLP and CiteSeer, were excluded from the search since their results are inclusive within the previous seven databases. Furthermore, the databases that publish non-reviewed articles were ignored. Figure 1 summarizes the general steps of our full systematic review. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Population/Problem** & **Intervention** & **Comparison** & **Outcome** \\ \hline \begin{tabular}{l} Studies predicting student \\ performance using the \\ learning outcomes \\ \end{tabular} & \begin{tabular}{l} List of intelligent models \\ and techniques \\ \end{tabular} & \
\begin{tabular}{l} Comparison across the \\ identified models \\ and techniques \\ \end{tabular} &
\begin{tabular}{l} Quality and accuracy of the approaches \\ Set of performance predictors of \\ learning outcomes \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PICO protocol adopted in our survey. We conducted the searches in November 2020 using the above databases with a focus on the studies published between 2010 and November 2020. The key terms that were devised to perform the searches were directly linked to the concepts of the research questions and PICO elements. (predict* OR forecasting)  
AND  
(\"student learning outcomes\" OR \"student outcomes\" OR \"learning outcomes\")  
AND  
(\"artificial intelligence\" OR \"machine learning\" OR \"data mining\" OR \"deep learning\" OR \"learning analytics\")  
It is worthwhile to note that the search string syntax was trialed multiple times and slightly modified in each database to obtain all relevant results, as recommended in [10]. When the searches were carried out on the full texts of our selected databases, thousands of irrelevant studies were fetched and returned.