. We use educational data mining to arrive at models of help-seeking behaviors associated with learning from datasets from three countries: Costa Rica, the Philippines, and the USA. The models were then tested on each country’s data to find out how effective help-seeking behavior varies across countries. This study found that models of effective help-seeking are not necessarily transferrable ac...

Researchers use many different metrics for evaluation of performance of student models. The aim of this paper is to provide an overview of commonly used metrics, to discuss properties, advantages, and disadvantages of different metrics, to summarize current practice in educational data mining, and to provide guidance for evaluation of student models. In the discussion we mention the relation of...

The following link provides a well-referenced introduction to learning analytics, a relatively new field of data collection for educational purposes. The site offers definitions of terms, history, methods, uses, software, ethical issues, and resources. https://en.wikipedia.org/wiki/Learning_analytics [1] The link below provides information on educational data mining, including such topics as us...

This paper focuses on the prediction of student learning styles using data mining techniques within their institutions. This prediction was aimed at finding out how different learning styles are achieved within learning environments which are specifically influenced by already existing factors. These learning styles, have been affected by different factors that are mainly engraved and found wit...

Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...