## 4 Survey Results
### Learning Outcomes as Indicators of Student Performance  
As stated earlier, we considered only the research articles that predicted student outcomes as a representative of student performance and success. It is worth noting that the articles that define the learning outcomes as their outcome variable were considered in our analysis, irrespective of the form of the learning outcomes. However, other studies that referred to student academic achievements using in-class assessment metrics, such as GPA or grades, without any reference to the learning outcomes were excluded from the survey. Overall, 56 (90.32%) studies attempted to forecast course outcomes, while three studies looked at the feasibility of predicting program outcomes. Only two studies, (i.e., [103, 104]), calculated student performance at both the course and program levels. Furthermore, most of the predictive models estimated the learning attainment of students individually (55 studies, 88.70%) rather than collectively (i.e., cohorts of students) (4 studies, 6.45%). However, three studies (i.e., [52, 77, 94]) predicted the performance of individual students as well as student cohorts.  
The prediction of student performance was achieved in two ways, formative and summative. In the formative prediction of learning outcomes, student features are considered throughout different points of the academic semester, in a bid to inform the instructors about the expected achievements of their students. This formative prediction empowers instructors to implement the necessary interventions early enough in the course. However, in the summative prediction, the learning outcomes are predicted at the end of the semester. Thirty-eight (61.29%) models provided summative predictions, while 19 (30.64%) models provided formative predictions (e.g., weekly or monthly) of student performance. Only five studies calculated both formative and summative predictions of student performance, i.e., [46, 68, 76, 84, 88].  
Typically, the attainment of student outcomes, whether at the course or program level, might be assessed and measured via direct or indirect methods. The direct methods use various types of course-level assessments, such as assignments and examinations, to obtain insights about student achievements. However, the indirect methods of assessment depend mainly on student opinions and feedback about their learning experiences. In our survey, most studies (50 studies, 80.64%) predicted student learning using direct measures. Nine (14.51%) studies used self-reports of students on their learning experience (i.e., indirect measures) to predict their performance. However, three notable studies [56, 57, 97] provided learning outcomes predictions using direct and indirect assessment.  
Furthermore, we inspected the form in which the learning outcomes were forecasted in the surveyed studies. Table 6 shows the results of the thematic analysis, revealing six distinctive types. The learning outcomes were predicted mostly in the form of performance classes (34 occurrences), achievement scores (20 occurrences), perceived competence (5 occurrences), self-reports of educational aspects (3 occurrences), and failure/graduation rates (3 occurrences).  
Figure 11 depicts that 80% of the models predicting academic performance standings classified the outcomes into two to four classes. The remaining 20% of the models forecasted more than 4 class labels of learning outcome performance. Examples of binary (dichotomous) classes are 'pass' and 'fail' [86, 87], 'certification' and 'no certification' [85], and 'on-time graduation' and 'not on-time graduation' [60]. A 4-class outcome example predicted students with variable risks [101], e.g., high risk (HR), medium risk (MR), low  
risk (LR), and no risk (NR). Ordinal performance ranks were also predicted; for instance, student outcomes were classified into five performance ranks, specifically fail, satisfactory, good, very good, and excellent [93].