Conceptual clustering of university graduate students' trajectories using formal concept analysis: a case study in Lebanon

Obeid, Charbel; Lahoud, Inaya; Khoury, Hicham El; Champin, Pierre Antoine

doi:10.1504/ijceell.2020.108541

Cited by 2 publications

(1 citation statement)

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“…Therefore, FCA becomes a suitable tool to formally reason with attributes related to the acquisition of concepts, skills and competencies, as well as for the study of academic performance and the knowledge structure of a course, as proposed in the present work. FCA has been recently used to cluster students according to their academic behaviour to guide them in their academic path [16] or to build a decision support system capable of identifying students at risk of dropping out in Massive Online Open Courses (MOOCs) [17].…”

Section: Introductionmentioning

confidence: 99%

Minimal Generators from Positive and Negative Attributes: Analysing the Knowledge Space of a Mathematics Course

Ojeda-Hernández

Pérez-Gámez

López-Rodríguez

et al. 2022

Int J Comput Intell Syst

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Formal concept analysis is a data analysis framework based on lattice theory. In this paper, we analyse the use, inside this framework, of positive and negative (mixed) attributes of a dataset, which has proved to represent more information on the use of just positive attributes. From a theoretical point of view, in this paper we show the structure and the relationships between minimal generators of the simple and mixed concept lattices. From a practical point of view, the obtained theoretical results allow us to ensure a greater granularity in the retrieved information. Furthermore, due to the relationship between FCA and Knowledge Space theory, on a practical level, we analyse the marks of a Mathematics course to establish the knowledge structure of the course and determine the key items providing new relevant information that is not evident without the use of the proposed tools.

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Section: Introductionmentioning

confidence: 99%