# An Early Warning System to Detect At-Risk Students in Online Higher Education
## 2 Related Work
### Early Warning Systems  
An EWS is a tool used to monitor students' progress. It identifies students at-risk of either failing or dropping out of a course or program [23]. It helps students to be on track and aid in their self-directed learning journey [34]. Also, it can help to reach the necessary information about student engagement and performance to facilitate personalized timely interventions [28].  
Based on the predictive models mentioned in the previous section, some examples of EWS are students' dropout detection on face-to-face environments [35], students' dropout on online settings [36; 37; 38], or early identification of at-risk students, which may allow some type of intervention to increase retention and success rate [16; 25; 26; 39].  
Many of the previously described approaches propose an EWS, but the EWS is just sketched from a conceptual point of view. Therefore, few full-fledged developments can be found. The most referenced one is the Course Signals at Purdue University [17] where different dashboards are available at the student's and teacher's point of view. Moreover, the system triggers a visual warning alert using a Green-Amber-Red semaphore. Other systems can be found where information is available in dashboards for teachers [31; 40; 41] and students [42].  
Personalized feedback [17] is one possible intervention mechanism for these kind of approaches. Other intervention mechanisms can be applied, such as pedagogical recommendations [43], mentoring [44], or academic support environment [45], with a significant impact on performance, dropout, and retention. Feeding students depending on his or her situation will affect students' perception and will impact his or her way of learning and self-regulation. If personalized feedback can be one of the possible intervention mechanisms, nudges can complement it in a very constructive way. Nudges according to the definition provided in [46] (p. 2) are \"interventions that preserve freedom of choice that nonetheless influence people's decisions\". Nudges have their origin in behavioral economics, which studies the effects of psychological knowledge about human behavior to enhance decision-making processes. In education [47], nudges intend to obtain a higher educational attainment (e.g., improvement of grades, to increase course completion rates and earned credits, or to increase course engagement and participation), and they have been used in primary, secondary, and higher education, including Massive Open Online Courses (MOOC), and involving different stakeholders (parents, students, and teachers). The information provided by nudges includes remainders, deadlines, goal setting, advice, etc. How the information included in nudges is stated can also increase social belonging and motivation.  
The EWS we propose is able to provide personalized feedback. First, students receive predictions from the first activity about the minimum grade they should obtain in the activity to succeed in the course prior to the submission deadline date. Thus, the student is knowledgeable about the effort he or she should apply in the activity to have a chance to pass the course. This prediction is updated after each graded activity and a new prediction is generated for the next one. This type of prediction differs from other approaches where the models can only be used after a certain number of activities, denoted as the best point of intervention when enough data have been gathered to have an accurate model. Second, the teacher can complement these predictions with constructive feedback from the very beginning of the course, including recommendations to revert (or even prevent) at-risk situations.