Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning…

User clustering algorithms have been introduced to analyze users' learning behaviors and help to provide personalized learning guides in traditional Web-based learning systems. However, the explicit and implicit coupled interactions, which means the correlations between user attributes generated from learning actions, are not considered in these…

With a goal of better understanding the online discourse within the Massive Open Online Course (MOOC) context, this paper presents an open source visualisation dashboard developed to identify and classify emergent discussion topics (or themes). As an extension to the authors' previous work in identifying key topics from MOOC discussion contents,…

While Educational Data Mining research has traditionally emphasized the practical aspects of learner modeling, such as predictive modeling, estimating students knowledge, and informing adaptive instruction, in the current study, we argue that Educational Data Mining can also be used to test and improve our fundamental theories of human learning.…

Massive Open Online Courses (MOOCs) are widely used to deliver specialized education and training in different fields. Determining the effectiveness of these courses is an integral part of delivering comprehensive, high-quality learning. This study is an evaluation of a MOOC offered by Imperial College London in collaboration with Health iQ…

Characterizing the nature of students' affective and emotional states and detecting them is of fundamental importance in online course platforms. In this paper, we study this problem by using discussion forum posts derived from large open online courses. We find that posts identified as encoding confusion are actually manifestations of different…

Ethnographic research is an emerging research technique in the field of education. Ethnographic research was a procedure usually used in anthropology but now it is getting popular in educational field. This kind of research relies on qualitative data, its perspective is holistic and its procedures of data analysis involve contextualization. Data…

This paper presents a discussion on potential conflicts originated by sensorimotor distractions when learning with mobile phones on-the-move. While research in mobile learning points to the possibility of everywhere, all the time learning; research in the area suggests that tasks performed while on-the-move predominantly require low cognitive…

We propose a graph mining methodology to analyze the relationships among academic programs from the point of view of cooperative education. The input consists of student - job interview pairs, with each student labelled with his or her academic program. From this input, we build a weighted directed graph, which we refer to as a program graph, in…

This position paper discusses the role of open access research data within mathematics education, a relatively new initiative across the wider research community. International and national policy documents are explored and examples from both the scientific and social science paradigms of mathematical sciences and mathematics education…

This study aims to test the usability of MyLA (My Learning Analytics), an application for students at two German universities: The Cooperative State University Mannheim and University of Mannheim. The participating universities focus on the support of personalized and self-regulated learning. MyLA collects data such as learning behavior and…

The purpose of this study was to examine the mathematics instructional practices of 75 second-year elementary teachers (K-5) in terms of the learning opportunities provided to their students. On average, each teacher completed instructional logs for 43 days across the school year. Select items were analyzed in order to better understand the…

Machine Learning methods for Performance Prediction in Intelligent Tutoring Systems (ITS) have proven their efficacy; specific methods, e.g. Matrix Factorization (MF), however suffer from the lack of available information about new tasks or new students. In this paper we show how this problem could be solved by applying Transfer Learning (TL),…

Recently organizations have begun to realize the potential value in the huge amounts of raw, constantly fluctuating data sets that they generate and, with the help of advances in storage and processing technologies, collect. This leads to the phenomenon of big data. This data may be stored in structured format in relational database systems, but…

The usage of data mining has dramatically increased over the past few years and the education sector is leveraging this field in order to analyze and gain intuitive knowledge in terms of the vast accumulated data within its confines. The primary objective of this study is to compare the results of different classification techniques such as Naïve…