Dr. Alan Mutka and Dr. Ante Poljičak receive RIT's Provost’s Learning Innovations Grant

Dr. Alan Mutka and Dr. Ante Poljičak are the recipients of RIT's Provost’s Learning Innovations Grant for their project “Student Retention Predictive Model Using Advanced Learning Validation in Web and Mobile Computing”. This grant is effective from May 1, 2023 through April 30, 2024.

AssessMe is an advanced software that uses artificial intelligence in tracking the creation of digital text-based tasks in real time. Its aim is to achieve and maintain academic integrity and learning validation within educational institutions. Over the past two years, AssessMe has been actively utilized to monitor and validate the assignments and examinations of students enrolled in the introductory programming courses for freshman year of the Web and Mobile Computing BS program. Now, Dr. Alan Mutka and Dr. Ante Poljičak aim to apply this solution to create an AI-based student retention predictive model, which they will be using this grant for.

 “Researchers have been studying ways to predict student retention rates in higher education. These studies often involve analyzing various factors, such as demographic information, academic performance, and student engagement, to identify early warning signs of potential dropout or withdrawal. Data-driven approaches, such as machine learning and predictive modeling, are also being explored as potential tools to aid in retention rate prediction,” says Dr. Mutka. “Our approach stands out because we leverage the data acquired from AssessMe, which is directly linked to the specific course.”

AssessMe provides unique coding metrics for each student's assignment, including total and active coding time, added and modified lines, removed lines, and more. Professors Mutka and Poljičak plan to collect and analyze two years' of AssessMe data from RIT Croatia freshman courses and identify unique coding metrics that significantly impact final student success. “Our preliminary results from the two Web and Mobile Computing program courses show a strong correlation between some of these metrics and the final grade received,” said Dr. Mutka.

“We aim to develop an AI-based predictive model that can effectively identify students who are at risk of failing the course during the early stages, ideally before week 8, while there is still sufficient time to intervene. By detecting these students as early as possible, we aim to provide them with the necessary support to overcome any challenges they may face in a timely manner and ensure their success in completing the course. This approach will be highly beneficial to students, as it will enable us to identify any issues early on and offer them the support they need to succeed”, said Dr. Poljičak

Developed by our faculty members, Dr. Alan Mutka and Dr. Martin Žagar, this solution was among finalists of ZICER’s Startup Factory in 2022. More information about AssessMe is available HERE.