Undergraduate students have different levels of motivation, different attitudes about learning, and different responses to specific instructional practices. Predicting the academic performance of students is an issue faced by many universities in emerging countries. Although those institutions store large amounts of educational data they are unsuccessful to detect which students are at risk of ...

The field of educational data mining (EDM) has been slowly expanding to embrace various graph-based approaches to interpretation and analysis of educational data. However, there is a great wealth of software tools for graph creation, visualization, and analysis, both general-purpose and domain-specific, which may discourage EDM practitioners from finding a tool suitable for their graph-related ...

The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success-factors specific ...

This paper considers the use of log data provided by learning management systems when studying whether students obey the problem-based learning (PBL) method. Log analysis turns out to be a valuable tool in measuring the use of the learning material of interest. It gives reliable figures concerning not only the number of use sessions but also the interlocking of various course activities. The lo...

A basic question of instructional interventions is how effective it is in promoting student learning. This paper presents a study to determine the relative efficacy of different instructional strategies by applying an educational data mining technique, learning decomposition. We use logistic regression to determine how much learning is caused by different methods of teaching the same skill, rel...

The increasing use of educational technologies in classrooms is producing vast amounts of process data that capture rich information about learning as it unfolds. The field of educational data mining has made great progress in using log data to build models that improve instruction and advance the science of learning. Thus far, however, the predictive and explanatory power of such models has of...

There has been a long-standing interest in how to estimate the prerequisite structure among a set of skills. This prerequisite structure is important for intelligent tutoring systems and the educational data mining community, because it has important implications for student modeling, and for automatically constructing teaching policies. Here we present preliminary work towards inferring skill ...

We consider the problem of visualizing and exploring a dataset about research publications from the fields of Learning Analytics (LA) and Educational Data Mining (EDM). Our approach is based on semantic annotation that associates publications from the dataset with Wikipedia topics. We present a visualization and exploration tool, called Paperista (www.uzrok.com/paperista), which presents these ...

Simulated data plays a central role in Educational Data Mining and in particular in Bayesian Knowledge Tracing (BKT) research. The initial motivation for this paper was to try to answer the question: given two datasets could you tell which of them is real and which of them is simulated? The ability to answer this question may provide an additional indication of the goodness of the model, thus, ...

The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnorma...