Educational Data Mining 

About: Educational Data Mining is an academic conference. The conference publishes majorly in the area(s): Educational data mining & Educational technology. Over the lifetime, 1631 publications have been published by the conference receiving 20806 citations.

The State of Educational Data Mining in 2009: A Review and Future Visions

Abstract: We review the history and current trends in the field of Educational Data Mining (EDM). We consider the methodological profile of research in the early years of EDM, compared to in 2008 and 2009, and discuss trends and shifts in the research conducted by this community. In particular, we discuss the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries ("discovery with models"), and the reduction in the frequency of relationship mining within the EDM community. We discuss two ways that researchers have attempted to categorize the diversity of research in educational data mining research, and review the types of research problems that these methods have been used to address. The most cited papers in EDM between 1995 and 2005 are listed, and their influence on the EDM community (and beyond the EDM community) is discussed.

Data Mining Algorithms to Classify Students

Abstract: In this paper we compare different data mining methods and techniques for classifying students based on their Moodle usage data and the final marks obtained in their respective courses. We have developed a specific mining tool for making the configuration and execution of data mining techniques easier for instructors. We have used real data from seven Moodle courses with Cordoba University students. We have also applied discretization and rebalance preprocessing techniques on the original numerical data in order to verify if better classifier models are obtained. Finally, we claim that a classifier model appropriate for educational use has to be both accurate and comprehensible for instructors in order to be of use for decision making.

Predicting Students Drop Out: A Case Study

Abstract: The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success-factors specific to the EE program. Our experimental results show that rather simple and intuitive classifiers (decision trees) give a useful result with accuracies between 75 and 80%. Besides, we demonstrate the usefulness of cost-sensitive learning and thorough analysis of misclassifications, and show a few ways of further prediction improvement without having to collect additional data about the students.

Sentiment Analysis in MOOC Discussion Forums: What does it tell us?

Abstract: Sentiment analysis is one of the great accomplishments of the last decade in the field of Language Technologies. In this paper, we explore mining collective sentiment from forum posts in a Massive Open Online Course (MOOC) in order to monitor students’ trending opinions towards the course and major course tools, such as lecture and peer-assessment. We observe a correlation between sentiment ratio measured based on daily forum posts and number of students who drop out each day. On a user-level, we evaluate the impact of sentiment on attrition over time. A qualitative analysis clarifies the subtle differences in how these language behaviors are used in practice across three MOOCs. Implications for research and practice are discussed.

Automatically Recognizing Facial Expression: Predicting Engagement and Frustration

Abstract: Learning involves a rich array of cognitive and affective states Recognizing and understanding these cognitive and affective dimensions of learning is key to designing informed interventions Prior research has highlighted the importance of facial expressions in learning-centered affective states, but tracking facial expression poses significant challenges This paper presents an automated analysis of fine-grained facial movements that occur during computer-mediated tutoring We use the Computer Expression Recognition Toolbox (CERT) to track fine-grained facial movements consisting of eyebrow raising (inner and outer), brow lowering, eyelid tightening, and mouth dimpling within a naturalistic video corpus of tutorial dialogue (N=65) Within the dataset, upper face movements were found to be predictive of engagement, frustration, and learning, while mouth dimpling was a positive predictor of learning and self-reported performance These results highlight how both intensity and frequency of facial expressions predict tutoring outcomes Additionally, this paper presents a novel validation of an automated tracking tool on a naturalistic tutoring dataset, comparing CERT results with manual annotations across a prior video corpus With the advent of readily available fine-grained facial expression recognition, the developments introduced here represent a next step toward automatically understanding moment-by-moment affective states during learning