Machine Learning algorithms for predicting university student dropout

Abstract Information: The Italian university system is characterized by a significant level of student dropout. This has adverse effects on several fronts: on the socio-economic side of the country, due to the lack of return on investments made in skills growth; on the university institutions, in terms of reduced national funding and revenue; and, finally, on students, who have to rethink and reorient their life paths. It is, therefore, important for universities to be able to make predictions about the outcomes of their students' academic careers. Indeed, knowing whether and how many students are at risk of not completing their studies allows the university to intervene with support and guidance, helping those students to continue and finish their studies on time. In recent years, an essential contribution in this regard has come from technologies related to Artificial Intelligence (AI) and, in particular, to Machine Learning algorithms (ML). These algorithms can analyze a student’s characteristics, academic history, and learning style to predict the likelihood of success or failure and recommend personalized interventions tailored to their needs. This can help improve the effectiveness of interventions and support. In this regard, this paper presents a project carried out at the University of Catania, Italy, where a novel ML algorithm capable of providing predictions of career outcomes for students in some of the University's departments was developed and tested. The model showed how helpful such tools can be in helping educators and counselors make more informed decisions about students' college careers by analyzing large amounts of data and identifying patterns and trends that may not be immediately apparent through traditional methods.

Relevance Statement: Tools that can assess and predict university student dropout can be important for identifying at-risk students and providing them with the support they need to succeed. Dropout prediction models can analyze various factors, such as academic performance, engagement, socio-economic status, and mental health, to identify students who may be struggling and need additional support. By identifying at-risk students early on, universities can provide targeted interventions and resources to help students stay on track and succeed academically. These interventions might include academic tutoring, mental health services, financial aid, or advising. In addition to benefiting individual students, dropout prediction models can help universities better allocate their resources and prioritize their efforts to improve student retention. For example, suppose a model identifies those students in specific majors or programs likelier to drop out. In that case, universities can focus on improving the support and resources available to those students. In this regard, Artificial Intelligence (AI) and, in particular, Machine Learning (ML) can be very effective. ML is a rapidly growing field that has the potential to transform evaluation practice by enabling more efficient, accurate, and objective analysis of data. The relevance and importance of ML to evaluation practice are evident in its ability to improve decision-making, increase the precision of evaluations, and offer more reliable and unbiased assessments. By providing a more detailed and nuanced understanding of the data being analyzed, ML algorithms can help evaluators to more accurately identify critical factors and relationships and detect more subtle changes or trends. This can lead to more precise and targeted evaluations that can better meet the needs of stakeholders. That being said, it is essential to approach dropout prediction models with caution and to recognize that they are not foolproof. Many factors can influence a student's decision to drop out of university, and models may not be able to capture all of them. It is crucial to consider the potential biases and limitations of the data and algorithms used in these models and to use them in conjunction with other sources of information and support, such as faculty and staff advisors, academic counselors, and student services.