Clustering of educational data allows similar students to be grouped, in either crisp or fuzzy sets, based on their similarities. Standard approaches are well suited to identifying common student behaviors; however, by design, they put much less emphasis on less common behaviors or outliers. The approach presented in this paper employs fuzzing…

The idea of clustering students according to their online learning behavior has the potential of providing more adaptive scaffolding by the intelligent tutoring system itself or by a human teacher. With the aim of identifying student groups who would benefit from the same intervention in AC-ware Tutor, this research examined online learning…

In this paper, we focus on optimizing the assignment of students to courses. The target courses are conducted by different teachers using the same syllabus, course design, and lecture materials. More than 1,300 students are mechanically assigned to one of ten courses taught by different teachers. Therefore, mismatches often occur between students'…

Peer assessment (PA), as formative procedure, enhances learning by providing students with the opportunity to peer assess each other's work. However, since students exhibit different value systems (abilities, experiences, attitudes, cognitive styles, etc.) we propose a diagnostic procedure, which can be applied at the beginning of a course, in…

The Motivation and Engagement Scale-University/College (MES-UC) was used to identify student typologies on the basis of adaptive and maladaptive academic cognitions and behaviours. The sample comprised first-year (n = 390), second-year (n = 300), and third-year (n = 251) undergraduate students with 4 student typologies identified: high…

Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are first grouped into clusters. Then, an…

New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and…

The development of a typology of community college students is a topic of long-standing and growing interest among educational researchers, policy-makers, administrators, and other stakeholders, but prior work on this topic has been limited in a number of important ways. In this paper, I develop a behavioral typology based on students'…

This exploratory data mining project used distance-based clustering algorithms to study three indicators of student behavioral data collectively called AB-Index, and established a typology of six types of learners for a suburban community college. The study is based on the notion that student behavioral data are a good basis for new ways of doing…