## 3 Survey Methodology
### Inclusion Criteria  
Table 3 lists the inclusion criteria that were applied to shortlist the candidate articles for consideration in this review. In other words, the studies that did not satisfy the criteria listed below, were disregarded. For instance, non-refereed articles, such as technical reports, and non-English papers were excluded.  
Figure 1: Major steps of our survey methodology.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Inclusion Criteria** & **Description of Criteria** \\ \hline \hline I1. Focus of study & \begin{tabular}{l} Studies that explicitly predict student performance \\ with a direct reference to the learning outcomes \\ \end{tabular} \\ \hline I2. Empirical evidence of prediction & \begin{tabular}{l} Studies that contain empirical evidence of the \\ performance prediction \\ \end{tabular} \\ \hline I3. Language of publication & \begin{tabular}{l} Only articles written in English are considered \\ \end{tabular} \\ \hline I4. Year of publication & \
\begin{tabular}{l} Studies published between 2010 and 2020 (both \\ years inclusive) \\ \end{tabular} \\ \hline I5. Publication venue &
\begin{tabular}{l} Studies published in peer-reviewed scientific venues \\ (e.g., conference or journal) \\ \end{tabular} \\ \hline I6. Availability of text & Full text is accessible for analysis \\ \hline \hline \end{tabular}
\end{table}
Table 3: Inclusion criteria in our systematic literature review.  
To ensure clarity and quality of our methodology, this systematic review followed the four stages advocated by the PRISMA statement and the reporting guidelines [42]. The first phase of PRISMA identifies the potential studies to investigate using automated and manual searches. The screening phase of the studies follows the identification phase to exclude duplicate and irrelevant studies. Next, the qualified articles are thoroughly read and assessed for eligibility, leading to the final set of studies to be included in our synthesis. In the screening and eligibility phases, we strictly applied the inclusion criteria listed in Table 3. Studies that did not directly refer to the prediction of learning outcomes (i.e., the outcome variable) were excluded from the synthesis. Moreover, it is not uncommon to see such a high drop in the number of papers that do not meet the inclusion criteria. Figure 2 shows the PRISMA flow diagram of our survey.  
The initial round of automated searches on the electronic databases gave a corpus containing a total of 586 articles, as listed in Table 4. After removing the duplicate publications and scanning the titles and abstracts, the number was reduced to 187 potentially relevant articles. Full scanning of the eligible articles reduced the search results to 51 relevant articles. Moreover, manual searches were executed by the authors to consider a further 11 primary articles.  
To sum up, the automated search yielded 51 relevant articles. However, SLR guidelines suggest carrying out manual searches to overcome the threat of missing primary studies and improve the reliability of the survey [10]. To this end, we (1) hand searched different journal and conference publications and (2) looked into the reference lists of our candidate articles to identify new relevant articles. These manual search approaches gave an additional 11 primary articles. Hence, the final sample of articles judged to be relevant to the prediction of student outcomes using intelligent approaches, e.g., machine learning, amounted to 62 papers.  
Figure 2: PRISMA flow diagram of our survey methodology.