## Discussion  
### Key Findings  
Outcome-based education has become pivotal for higher education leaders and accreditation organizations [12]. Moreover, learning analytics has gained tremendous momentum in the past decade to overcome the barriers hindering student learning [107]. Learning analytics and educational data mining (i.e., EDM) are proclaimed to improve the attainment of student learning outcomes [108]. There are also several calls for automating the assessment of student outcomes, which represent a proxy for student performance and success [9; 29]. However, it is unclear how student outcomes are modeled and predicted at the course and program level using data mining and machine learning models. The current survey was carried out as an attempt to bridge this research gap. The choice for surveying the last decade was motivated by the recent technological advances in artificial intelligence and data mining, coupled with the prominence of the outcome-based theory in education. The closest study to ours was the survey reported in [36], which explored 39 studies predicting learning outcomes in learning analytics between 2002 and 2016. Although the survey tried to summarize the main techniques used for predicting the learning outcomes, it failed to detail the results of the predictions. Our findings confirmed some previous observations. For instance, there seemed to be a growing interest in understanding student performance in learning management systems (LMS). The sample size of the datasets remained small to train the predictive models sufficiently. The predicted variable (i.e., learning outcome) evolved from a binary term to take a multi \begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Assessment Criterion**} & **Yes (\%)** & **No (\%)** \\ \hline
1. Verification of predictive model with a second dataset & 8.06\% & 91.94\% \\ \hline
2. Threats to validity reported & 12.90\% & 87.10\% \\ \hline
3. Research implications and recommendations & 20.96\% & 79.04\% \\ \hline
4. Well-defined research questions & 33.87\% & 66.13\% \\ \hline
5. Use of separate training and testing datasets & 35.48\% & 64.52\% \\ \hline
6. Research limitations and challenges & 37.09\% & 62.91\% \\ \hline
7. Results detailed sufficiently & 56.45\% & 43.56\% \\ \hline
8. Predictor variables clearly described & 77.42\% & 22.58\% \\ \hline
9. Predictions being made are clear & 82.25\% & 17.75\% \\ \hline
10. Data collection instruments stated & 82.25\% & 17.75\% \\ \hline
11. Sound research methodology & 83.87\% & 16.13\% \\ \hline
12. Clear research contributions & 90.32\% & 9.68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Quality assessment of surveyed studies; Yes = Condition satisfied, No = Condition not satisfied. rank form; however, student grades are still used to refer to the learning outcomes. Lastly, the accuracy of the supervised learning models improved to reach unprecedented levels. Our survey showed that developing models that forecast student learning outcomes is on the rise since 2017, with a significant portion of the articles published in computing and IT venues. Approximately half of the surveyed studies predicted learning outcomes of traditional classroom learning, while the other half focused on online and blended learning, due to its ever-increasing importance. More emphasis is directed toward undergraduate university courses and the STEM specialties (i.e., science, technology, engineering, and mathematics). The developed countries (e.g., the USA and Europe) are taking the lead in researching learning analytics of student outcomes. Below, we revisit each research question separately and highlight the main findings. * RQ1-Learning Outcomes Prediction. How is student academic performance measured using learning outcomes? Here our analysis focused on understanding the forms in which the learning outcomes were measured in the selected studies.