## 2 Related Work
(2016) performed a survey that examined online learners' data to predict their outcomes, using machine- and deep-learning techniques. They stated that most studies in the field utilized statistical features, such as the number of downloaded materials and the total time spent on course-related video watching in a given time period. [PERSON] et al. (2017) reviewed 83 studies, finding that ensemble-learning models achieved the highest accuracy rate (87.7%), followed by the support-vector-machine (84.3%) approach. Demographic, academic, and behavioral factors were significant predictors of academic achievement. [PERSON] et al. (2018) applied machine-learning techniques (regression trees and random forests) to Tunisian secondary schools, identifying school size, competition, class size, parental pressure, and the proportion of girls as key performance factors. Random forests additionally revealed the significant roles of the proportion of girls and the school size in improving predictive accuracy and, hence, could influence school efficiency. Similarly, [PERSON] et al. (2019) found random forests to be the best-performing algorithm for predicting students' performance, with an accuracy rate of 85.4%. The authors also emphasized challenges such as the lack of standardization in performance metrics, limited model generalization abilities, and potential bias in training data. Finally, [PERSON] et al. (2020) combined machine-learning algorithms and Shapley values to analyze factors influencing Colombian students' academic performance. The results showed that the best accuracy was achieved with extreme gradient boosting. Furthermore, according to the Shapley values, the socioeconomic level index, gender, age, region, and school location were identified as key predictors. As shown in related studies on students' performance in academic contexts, authors typically base their analyses on various subsets of demographic data (as we do in our study), as well as on non-demographic and environmental factors. The authors in (2011) suggested a multilevel heterogeneous predictive model, which yielded an ensemble-learning model for predicting students' performance and assessments. The authors claimed a predictive accuracy rate of 99.5%. [PERSON] and [PERSON] (2020)investigated the prediction of students' academic performance using ensemble machine-learning algorithms, specifically, random forest, k-nearest neighbors, XGBoost, and a stacked ensemble approach. Their results demonstrated that the stacked ensemble model achieved the best performance. This study builds upon previous work, such as that by [PERSON] [23] and [PERSON] et al. [24], by demonstrating the effectiveness of ensemble methods for improving the accuracy of student performance predictions. Similar to the authors of these studies, we also employed ensemble methods (voting classifiers), which yielded solid, though not the best, results in our case. Finally, the authors of a review study [25] stated that individuals tend to validly use different multiple-machine-learning models to solve the problems related to predicting students at risk and assessing their performance. Multiple authors have tackled the problem of predicting degree completion with various approaches in terms of machine-learning models and the underlying type of the used data [26; 27; 28]. The authors in [29] focused on demographic data as predictors of learners' performance. In this study, we adopted a similar approach by evaluating multiple models to determine which would yield the best results, as we did not have a prior indication of which model might perform optimally. Machine learning and artificial intelligence have, in general, gained significant traction in the fields of marketing and consumer behavioral analysis. The authors in [30] demonstrated that AI-driven marketing strategies, such as targeted ads and chatbots, significantly improved outcomes, with increases in click-through rates and general improvements in customer satisfaction.