## 3 Results of Systematic Literature Review
### Datasets  
Several datasets have been used in different student performance prediction studies in the literature. Although some datasets are more commonly used, studies have generally been performed by considering their primary or current datasets. In this section, the datasets that form the basis of student performance prediction studies are analyzed and their relations are revealed. As mentioned above, datasets are the essential features that determine whether a study can be characterized as classification or regression, high school or HEI, online, on-campus or blended, or all. On the other hand, the dataset's content significantly determines the target of the studies, such as end-of-term, drop-out, in-term, etc. The most significant characteristic that distinguishes in-term and end-of-term studies from each other is that the datasets used in in-term studies contain records of online activities (assignments, quizzes, exams, logins, etc.) that students participate in at certain time intervals during the semester [61]. This makes it possible to predict student performance in the following period. These datasets are also used in end-of-term studies, and more accurate results could be achieved from the detailed information that they provide [18]. However, on-campus data contain less information than online data on student activities, and on-campus studies are therefore used to predict end-of-term performance [55]. Questionnaire are also an essential source of information for learning about students and focus on a different approach to end-of-term studies. In the questionnaires, student habits, family information, demographic information, etc., were used to predict the students' general success in the courses [11; 62]. Since these studies did not consider any continuous course information during the semester, they can be included in future prediction studies. However, the diversity of factors affecting a student's success in a course limited the efficiency of such studies. [PERSON] and [PERSON] [62] developed the most widely used questionnaire dataset in the literature. The questionnaire results, including demographic, social, and educational questions, were collected in a Portuguese high school, and the dataset included 33 attributes and 649 instances (students). The effects of demographic, social, and educational habits on the prediction of student performance have been analyzed in many studies [50; 56]. With the raw results provided, this dataset is considered in both regression and classification studies for end-of-term and future estimation purposes [27]. Another different dataset is the Kaggle MOOC drop-out prediction dataset available at: ([[https://www.kaggle.com/samyakjhaveri/mooc-final](https://www.kaggle.com/samyakjhaveri/mooc-final)]([https://www.kaggle.com/samyakjhaveri/mooc-final](https://www.kaggle.com/samyakjhaveri/mooc-final)), accessed on 17 May 20021). The dataset consists of logs for more than 155,000 students for 247 courses conducted through MOOC at a Chinese University. This dataset has been used in drop-out and end-of-term performance prediction studies [63]. The WorldUC dataset ([[http://www.worlduc.com](http://www.worlduc.com)]([http://www.worlduc.com](http://www.worlduc.com)) accessed on 20 August 2020) includes 10,523 records for three years of ten continuous lessons in an online course. The features are considered static, such as gender, age, and expectation score, and dynamic, such as the relevance of learning resources and content, duration time, etc. The Liru dataset is obtained from Liru Online Courses web site (available at ([[https://moodle.scnu.edu.cn](https://moodle.scnu.edu.cn)]([https://moodle.scnu.edu.cn](https://moodle.scnu.edu.cn)), accessed on 22 August 2020) and contains data on 1046 students for 18 consecutive lessons. In addition, the dataset includes both static features, such as grade, class, and college, and two dynamic features, namely knowledge level and assignment score, which are different from the WorldUC dataset.