The objective of this research is to analyze a system to improve the advising of students in a nontraditional environment. Non-traditional students are becoming more of the norm than the exception at University campuses across the U.S. Specifically, minority serving institutions, commuter campus and institutions with a high percentage of student transfers are unable to keep a tightly controlled cohort of students progressing through the curriculum. Students usually have varied course loads and different priorities due to family, financial need or other responsibilities. Therefore, students need an individualized approach to advising. This pilot program is starting a second year of using the system in department of Electrical and Computer Engineering.
The school administration faces more challenges scheduling courses and allocating diminishing resources to satisfy the student demand. In addition, the faculty needs to assess the efficacy of the curriculum in a program and collecting longitudinal student data is difficult.
This web application system (mobile compatible) is a multi-agent approach to allow the students to take more control over their individualized advising has been developed. In this context, the student tool becomes an agent and the school provides the environment with a desirable behavior for the system. We call the academic control objective the "Operator."
This research focuses on analyzing the performance of the distributed control agent system that collects students' information about their progress through the curriculum in a web-based program and generates advising recommendations specific to each student. This fills the advising forms to stream line face to face advising appointments. The agent logic employs principles used in project management tools designed to help the students optimize their resources to complete their degree sooner. It provides a visualization map of course sequences, customized for each student based on the course history, making advising adjustments that will optimize the time to obtain the degree under a constrained set of student resources. At the same time, the agent system provides real-time feedback to the Operator.
The second tool is the Operator dashboard that consolidates the collected data from the agents through several semesters (historical data) plus the predicted effects of the recommended plans. This enables a better resource allocation from the administration and deeper analysis of the curriculum effectiveness.
Last years’ data has presented some limited insight into the multi-agent approach performance. However, the proliferation of mobile devices and cloud computing enables a larger scale application of the proposed methodology. The results that we have acquired at this point show a very high acceptance of the system by the students. The complete dataset will be discussed extensively in the results section.
Are you a researcher? Would you like to cite this paper?
Visit the ASEE document repository at
for more tools and easy citations.