Many approaches have been developed for academic performance evaluation using various fuzzy techniques. Initial methods rely greatly on experts' specification of analytical parameters, without making use of valuable information embedded in collected data. Given this insight, fuzzy rule induction has recently been studied as a data-driven alternative. Despite its efficiency and reported performance, the fuzzy subsethood metric representing the strength of relations between system variables is only used at a coarse level, with the underlying semantics being unfortunately distorted via a local re-scaling scheme. To overcome this problem, a novel fuzzy classification system is introduced in this paper, in which the existing measure is used to its full potential via the methodology of qualitative link analysis. With a network representation where variables and their relations are encoded as graph nodes and edges, the classification of a new instance conceptually becomes a problem of link-based similarity estimation that can be effectively resolved using the proposed fuzzy qualitative model. This new approach has been evaluated against the existing rule-based method, revealing significant advantages of the present work.