Interactive decision support for academic advising

Abstract

Purpose This paper aims to support academic advising, which plays a crucial role in student success and retention. The paper focuses on one of the most challenging tasks involved in academic advising: individual course scheduling. This task includes not only careful planning for different courses over several semesters according to students’ preferences and goals but also must conform to many student constraints and administrative regulations, some of which may rely on student-specific cases.. Design/methodology/approach This paper introduces a novel approach that tries to provide meaningful support to decision makers involved in the course scheduling problem. The approach uses optimization algorithms to perform a pro-active analysis of the impact of different problem aspects and eventually suggests a balanced study plan that tries to satisfy both student preferences and advisor recommendations without violating any constraints. Findings An initial application of the proposed system is used to discuss its benefits. Originality/value The paper introduces a novel approach that uses optimization techniques to support making efficient decisions during the academic advising process.

Academic Advising Systems: A Systematic Literature Review of Empirical Evidence

“…This will be quite useful for mining student profiles and analyzing and predicting student performance [15] • In small institutions, an issue that needs to be considered in an AAS is the number of students in each class to guarantee a minimum number of enrolled students [17] • Solving the Elective Course Planning Problem optimal can be used to reduce students' switch to another high school, which is highly undesirable [20] • During academic planning, an "explanation component" is important to explain to students the rationale behind courses assignments to various semesters, or changes to study plans after modifying any input setting [23] • More than 90% of respondents rated the online AAS increased their awareness of the curriculum [24] • Multiple course selections from different faculties and departments enable students to maximize their opportunities in registering courses of their own interest and completing their degree requirements in the best possible way [25] Long-Term Course Planning…”

“…More importantly, because the number and variety (in backgrounds, in knowledge, in goals) of students is expanding rapidly, it is more and more important to tailor course sequences to students, since the same learning pathway is unlikely to best serve all students [16,17,23,25]. For example, the system described in [15] implicitly implements, via the decision tree, many academic rules and allows a systematic and exhaustive browse of the different student plan instances, and it permits a methodological assessment and measurement of the appropriateness of a given student academic plan.…”

From Traditional to Intelligent Academic Advising: A Systematic Literature Review of e-Academic Advising

“…The system was implemented and evaluated by real students from Texas at Arlington-USA and Khalifa University-UAE. Author in [33] proposed a decision support system called IDiSCS. It is used for individual course scheduling according to students' goals and interests.…”

An optimization‐based decision support system for students' personalized long‐term course planning

“…Mohamed [15] proposed IDiSCs, a three-phase approach for individual course scheduling which is based on a linear-integer-programming model, considering several university regulations, advisors', and students' preferences. The final solution is provided by what-if scenarios to make informed decisions.…”

“…• Three objective functions, Equations (15)(16)(17), are replaced with three goal-based constraints Equations (18)(19)(20). • An aggregated objective functions is formulated to minimize total weighted deviations from goals determined in the goal-based constraint Equation (25).…”