International Conference on Educational Data Mining (EDM) 2011. Proceedings of the 4th International Conference on Educational Data Mining. Eindhoven, 6-8 July, 2011
The 4th International Conference on Educational Data Mining (EDM 2011) brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large datasets to answer educational research questions. The conference, held in Eindhoven, The Netherlands, July 6-9, 2011, follows the three previous editions (Pittsburgh 2010, Cordoba 2009 and Montreal 2008), and a series of workshops within the AAAI, AIED, EC-TEL, ICALT, ITS, and UM conferences. The increase of e-learning resources such as interactive learning environments, learning management systems, intelligent tutoring systems, and hypermedia systems, as well as the establishment of state databases of student test scores, has created large repositories of data that can be explored to understand how students learn. The EDM conference focuses on data mining techniques for using these data to address important educational questions.
This paper presents an application of formal concept analysis to the study of student assessment data. Formal concept analysis (FCA) is an algebraic framework for data analysis and knowledge representation that has been proven useful in a wide range of application areas such as life sciences, psychology, sociology, linguistics, information technology and computer science. We use the FCA approach to represent the structure of an educational domain under consideration as a concept lattice. In this paper, we aim at building lattice-based taxonomies to represent the structure of the assessment data to identify the most stable student groups w.r.t the students achievements (and dually for courses marks) at certain periods of time and to track the changes in their state over time.
The aim of this paper is to present a case study in the analysis of university applications to the Higher School of Economics (U-HSE), Moscow. Our approach uses lattice-based taxonomies of entrants’ decisions about undergraduate programmes. These taxonomies were built by means of Formal Concept Analysis (FCA). FCA is a well-known algebraic technique for objectattribute data analysis. Admission data as well as formalised survey data were used to reveal possibly significant factors of entrants’ decisions. In this paper we argued that institutional characteristics of the admission process are highly correlated with entrants’ choice. The obtained results are helpful to the university to correct the structure and positioning of its undergraduate programmes.