# An Early Warning System to Detect At-Risk Students in Online Higher Education
## 6 Case Studies: Computer Fundamentals and Databases  
The EWS has been tested in two case studies to answer the research question RQ3. The EWS aims to detect but also support at-risk students, which basically means, to help them to continue their learning process by providing accurate and valuable feedback for not failing or dropping out. Specifically, the EWS has been introduced in Computer Fundamentals and Databases courses. Both courses are online courses offered in the Computer Science bachelor's degree and in Telecommunication bachelor's degree at UOC. While Computer Fundamentals is a first-semester course, Databases is offered in the fourth one. Thus, students' knowledge and expertise are quite different when performing each one. Both courses are 6 ECTS and their assessment model is based on continuous assessment  
Figure 7: Decision tree for warning level classification. (several assessment activities are proposed along the semester, some of them are practical activities that imply the development of artifacts) and a final face-to-face exam at the end of the semester. In Computer Fundamentals (a first-year course), students are devoted to learning the main principles for designing digital circuits and the basis of the computer architecture. In Databases, students are devoted to learning what databases are (and the specialized software that manages them), and how to create and manipulate relational databases using the SQL language (both interactive and embedded SQL). For the Databases course, previous knowledge is required (in programming and logic), so this is the reason to be in the fourth semester. The Computer Fundamentals course has a high number of students enrolled each semester (between 500-600 students) and becomes the first contact with the custom LMS. The Databases course has over 300 students each semester, but students already know very well the UOC educational model as well as how the LMS works. So, they are not as new in Databases as they are in Computer Fundamentals. Thus, both courses are excellent ones to evaluate the EWS and analyze whether at-risk students can be correctly identified and help them through the early feedback system. The system has been tested in both courses during one semester. Previously, a proof of concept was also tested as it can be seen in [3], but now a new version of the EWS is tested with new features and enhancements suggested by teachers and students when collecting their perception, as explained in [60]. As mentioned in Section 3.7, this process has been done following the design and creation research method that uses an iterative model to better develop IT artifacts to solve real problems. This new version of the EWS is the second iteration. The EWS was tested during the first semester of the academic course 2019/2020 (i.e., 20191 semester). A total of 313 students from Computer Fundamentals (CF) gave their consent to test the system, and a total of 71 students from Databases (DB) gave it too. To sum up, 384 students were testing it. In both courses, and after teachers graded each assessment activity, the EWS was triggering predictions to students, as well as personalized feedback messages, which depended on the warning level classification of the students presented in Section 5. According to the numbers of students taking part in the pilot, and the number of assessment activities performed, the system triggered 1607 predictions (CF: 1252; DB: 355). Feedback messages attached to each prediction and assessment activity were previously designed and analyzed by teachers, with the aim of motivating students for further assessment activities. Feedback messages tended to be as personalized as the system allowed, and they included some nudges to support the students learning process according to their grades.