The performance in higher secondary school education in India is a turning point in the academic lives of all students. As this academic performance is influenced by many factors, it is essential to develop predictive data mining model for students’ performance so as to identify the slow learners and study the influence of the dominant factors on their academic performance. In the present inves...

The ease with which data can be transferred without loss of meaning from a store to an analytical tool whether this tool is in the hands of a data scientist, a learning science researcher, a teacher, or a learner – and the ability of these users to select and apply a range of tools to data in formal and informal learn ing platforms are important factors in making learn ing analytics and educati...

Educational process mining is an emerging field in the educational data mining (EDM) discipline, concerned with discovering, analyzing, and improving educational processes based on information hidden in datasets and logs. These data are recorded by educational systems in different forms and at different levels of granularity. Often, process discovery and analysis techniques applied in the educa...

Educational Data Mining is an area of growing interest, given the increase of available data and generalization of online learning environments. In this paper we present a first approach to integrating Representation Learning techniques in Educational Data Mining by adding autoencoders as a preprocessing step in a standard performance prediction problem. Preliminary results do not show an impro...

Identification of significant differences in sets of data represents frequent data mining application. This paper presents initial study introducing application of MATLAB in the area of educational data mining tasks. In the concrete, statistical test called Analysis of Variance is used to determine significant difference of data sets analyzed. The study has been performed on real data coming fr...

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Spectral Clustering is a graph theoretic technique to represent data in such a way that clustering on this new representation is reduced to a trivial task. It is especially useful in complex datasets where traditional clustering methods would fail to find groupings. In previous work we have shown the utility of using K-means clustering for exploiting structure in the data to affect a significan...

We present our overall third ranking solution for the KDD Cup 2010 on educational data mining. The goal of the competition was to predict a student’s ability to answer questions correctly, based on historic results. In our approach we use an ensemble of collaborative filtering techniques, as used in the field of recommender systems and adopt them to fit the needs of the competition. The ensembl...

Researchers in the EDM community have always relied on sophisticated tools to analyze data and build models. As the amount of data that can be collected and stored grows, the need for tools capable of handling “big data” becomes ever more prevalent. SAS Analytics U is a new initiative for making SAS data analysis and mining tools available for free to educational researchers and instructors. Th...

This paper explores the potential intersection of two fascinating and increasingly burgeoning fields: Swarm Intelligence and Educational Data Mining. A thorough review of the existing works in both fields has revealed a lack of Swarm Intelligence applications in Educational Data Mining, despite its successful applications in other data mining domain. Consequently, this paper launches the inters...