Intelligent tutors have become increasingly accurate at detecting whether a student knows a skill at a given time. However, these models do not tell us exactly at which point the skill was learned. In this paper, we present a machine-learned model that can assess the probability that a student learned a skill at a specific problem step (instead of at the next or previous problem step). Implicat...

A key challenge in educational data mining research is capturing student work in a form suitable for computational analysis. Online learning environments, such as intelligent tutoring systems, have proven to be one effective means for accomplishing this. Here, we investigate a method for capturing students’ ordinary handwritten coursework in digital form. We provided students with Livescribedig...

Data Mining is best-known for its analytical and prediction capabilities. It is used in several areas such as fraud detection, predicting client behavior, money market behavior, bankruptcy prediction. It can also help in establishing an educational ecosystem, which discovers useful knowledge, and assist educators to take proactive decisions to boost student performance and employability. This p...

As an interdisciplinary discipline, data mining (DM) is popular in education area especially when examining students’ learning performances. It focuses on analyzing educational related data to develop models for improving learners’ learning experiences and enhancing institutional effectiveness. Therefore, DM does help education institutions provide highquality education for its learners. Applyi...

Student Learning Outcome (SLO) has been a hot issue in the research fields of higher education quality assurance and institutional research. Based on the classic college student development theories and students' learning experiences, this paper combines two educational data mining techniques, regression analysis and neural network modeling to explore the influential mechanism of SLO by the mea...

In this paper we utilising some popular educational data mining (EDM) methods to explore and mine educational data resulted from a system that supports reflection for learning called Reflect. Our objective is to study whether using the Reflect helps students learn better in the subject. First, we explore data by means of statistical analyses. Then, we used clustering and classification techniqu...

The project LeMo (monitoring of learning processes on personalizing and non-personalizing e-Learning environments) aims to develop a prototype of a web based Educational Data Mining application, which shall provide detailed information on user pattern within e-Learning environments and identify needs of enhancement and revision of the learning offer. The poster presents a case study of analysis...

MATHia X is the next generation implementation of Carnegie Learning’s Cognitive Tutor (CT), a widely deployed, researchbased mathematics curriculum that has provided data for many educational data mining studies. While many researchers are familiar with the basic operation of the system, there are several features that may affect analysis and interpretation of data that are less well known. We ...

We present the TCS Alignment Toolbox, which offers a flexible framework to calculate and visualize (dis)similarities between sequences in the context of educational data mining and intelligent tutoring systems. The toolbox offers a variety of alignment algorithms, allows for complex input sequences comprised of multi-dimensional elements, and is adjustable via rich parameterization options, inc...

Technological support for personalized learning has the potential to transform the educational system in the United States. There is a growing interest in educational games and their potential for motivating learners. Techniques from the educational data mining and intelligent tutoring systems communities can be leveraged to better understand, design, and evaluate educational games for both lea...