Two major goals in Educational Data Mining are determining students’ state of knowledge and determining their affective state as students progress through the learning session. While many models and solutions have been explored for each of these problems, relatively little work has been done on examining these states in parallel, even though the psychology literature suggests that it is an inte...

Educational Data Mining (EDM) methods can expand the reach of microgenetic research. This paper presents an example of our pilot work using microgenetic analysis in the context of fraction game data, where we characterize student activity based on clustered sequences of actions. We cluster sequences by the similarity between them, calculated using optimal matching techniques.

The article describes the basic principles and methods of Data mining and Process mining, their similarities and differences. The authors examine the research in Educational Data Mining field, associated with the use of Data mining techniques in education, give examples of problems to be solved with the use of Data mining and Process mining techniques in the area of traditional and e-learning, ...

With the aid of educational data mining and statistical analysis, we investigate the relationship between collaboration outcomes and collaborative problem solving (CPS) skills exhibited during the collaboration process. We found that negotiation skill contributes positively to the collaboration outcomes while purely sharing information does the opposite.

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

Data mining methods have in recent years enabled the development of more sophisticated student models which represent and detect a broader range of student behaviors than was previously possible. This chapter summarizes key data mining methods that have supported student modeling efforts, discussing also the specific constructs that have been modeled with the use of educational data mining. We ...

This mixed panel of different professionals working in EDM will be a conversation about increasing the connection between research and real-world applications. What’s going on now to scale techniques for use ”out there”in the field? What should researchers, funders, regulators, publishers, trainers, schools/universities and others be doing to get ready for practical work? What’s in the way that...

Data mining techniques are used to extract useful knowledge from raw data. The extracted knowledge is valuable and significantly affects the decision maker. Educational data mining (EDM) is a method for extracting useful information that could potentially affect an organization. The increase of technology use in educational systems has led to the storage of large amounts of student data, which ...

Validation is one of the key aspects in data mining and even more so in educational data mining (EDM) owing to the nature of the data. In this chapter, a brief overview of validation in the context of EDM is given and a case-study is presented. The field of the case study is related to motivational issues in general and disengagement detection in particular. There are several approaches to elic...

While the field of educational data mining (EDM) has generated many innovations for improving educational software and student learning, the mining of student data has recently come under a great deal of scrutiny. Many stakeholder groups, including public officials, media outlets, and parents, have voiced concern over the privacy of student data and their efforts have garnered national attentio...