Designing personalized curricula based on student preferences

Abstract

We address the problem of generating entire course sequences, given a set of target skills along with possibly prioritized student preferences over course descriptions. Compared to logic frameworks formulating course sequencing as a planning problem, our work relies on a set-theoretic framework for generating course sequences using preference-based queries. We introduce the concept of ordered partition for sequencing, and the ordered product of partitions, when it is necessary to combine more than one preference orderings. In our context, ordered partitions originate from preferences expressed over general relations, rather than on functional attributes of traditional database tuples (or objects) addressed by other approaches. We believe that the proposed framework is expressive enough to produce course sequences from descriptions expressed in diverse data models (e.g., XML, RDF/S) with respect to a variety of user preferences, also including priorities over the preferences.

A First-Order Logic Framework of Major Choosing Decision Making With an Uncertain Reasoning Function

“…Among them the most popular is curriculum sequencing [5], [8], [16]- [23], which provides the student with either knowledge sequencing (the most suitable individually planned sequence of knowledge units to learn) or task sequencing (sequence of learning tasks to work with). These approaches, as pointed out in [24], are of great use for remote students in helping them find an "optimal path" through the learning material, but before that they must know full well what goal (e.g., choosing a major) is expected to attain.…”