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Mostow, Jack; Beck, Joseph E. – International Working Group on Educational Data Mining, 2009
The ability to log tutorial interactions in comprehensive, longitudinal, fine-grained detail offers great potential for educational data mining--but what data is logged, and how, can facilitate or impede the realization of that potential. We propose guidelines gleaned over 15 years of logging, exploring, and analyzing millions of events from…
Descriptors: Data Analysis, Data Collection, Intelligent Tutoring Systems, Guidelines
Garcia, Enrique; Romero, Cristobal; Ventura, Sebastian; Gea, Miguel; de Castro, Carlos – International Working Group on Educational Data Mining, 2009
This paper describes a collaborative educational data mining tool based on association rule mining for the continuous improvement of e-learning courses allowing teachers with similar course's profile sharing and scoring the discovered information. This mining tool is oriented to be used by instructors non experts in data mining such that, its…
Descriptors: Data Analysis, Electronic Learning, Teacher Collaboration, Instructional Improvement
Nugent, Rebecca; Ayers, Elizabeth; Dean, Nema – International Working Group on Educational Data Mining, 2009
In educational research, a fundamental goal is identifying which skills students have mastered, which skills they have not, and which skills they are in the process of mastering. As the number of examinees, items, and skills increases, the estimation of even simple cognitive diagnosis models becomes difficult. We adopt a faster, simpler approach:…
Descriptors: Data Analysis, Students, Skills, Cluster Grouping
Bravo, Javier; Ortigosa, Alvaro – International Working Group on Educational Data Mining, 2009
E-Learning systems offer students innovative and attractive ways of learning through augmentation or substitution of traditional lectures and exercises with online learning material. Such material can be accessed at any time from anywhere using different devices, and can be personalized according to the individual student's needs, goals and…
Descriptors: Data Analysis, Electronic Learning, College Students, Low Achievement
Quevedo, J. R.; Montanes, E. – International Working Group on Educational Data Mining, 2009
Specifying the criteria of a rubric to assess an activity, establishing the different quality levels of proficiency of development and defining weights for every criterion is not as easy as one a priori might think. Besides, the complexity of these tasks increases when they involve more than one lecturer. Reaching an agreement about the criteria…
Descriptors: Data Analysis, Scoring Rubrics, Evaluation Criteria, Automation
Zoubek, Lukas; Burda, Michal – International Working Group on Educational Data Mining, 2009
Identification of significant differences in sets of data is a common task of data mining. This paper describes a novel visualization technique that allows the user to interactively explore and analyze differences in mean values of analyzed attributes. Statistical tests of hypotheses are used to identify the significant differences and the results…
Descriptors: Secondary School Students, Foreign Countries, Data Analysis, Visualization
Rai, Dovan; Gong, Yue; Beck, Joseph E. – International Working Group on Educational Data Mining, 2009
Student modeling is a widely used approach to make inference about a student's attributes like knowledge, learning, etc. If we wish to use these models to analyze and better understand student learning there are two problems. First, a model's ability to predict student performance is at best weakly related to the accuracy of any one of its…
Descriptors: Data Analysis, Statistical Analysis, Probability, Models
Anaya, Antonio R.; Boticario, Jesus G. – International Working Group on Educational Data Mining, 2009
Data mining methods are successful in educational environments to discover new knowledge or learner skills or features. Unfortunately, they have not been used in depth with collaboration. We have developed a scalable data mining method, whose objective is to infer information on the collaboration during the collaboration process in a…
Descriptors: Data Analysis, Cooperative Learning, College Students, Adult Students
Simko, Marian; Bielikova, Maria – International Working Group on Educational Data Mining, 2009
To make learning process more effective, the educational systems deliver content adapted to specific user needs. Adequate personalization requires the domain of learning to be described explicitly in a particular detail, involving relationships between knowledge elements referred to as concepts. Manual creation of necessary annotations is in the…
Descriptors: Foreign Countries, Data Analysis, Individualized Instruction, Electronic Learning
Rus, Vasile; Lintean, Mihai; Azevedo, Roger – International Working Group on Educational Data Mining, 2009
This paper presents several methods to automatically detecting students' mental models in MetaTutor, an intelligent tutoring system that teaches students self-regulatory processes during learning of complex science topics. In particular, we focus on detecting students' mental models based on student-generated paragraphs during prior knowledge…
Descriptors: Data Analysis, Prior Learning, Cognitive Structures, College Students
Prata, David Nadler; Baker, Ryan S. J. d.; Costa, Evandro d. B.; Rose, Carolyn P.; Cui, Yue; de Carvalho, Adriana M. J. B. – International Working Group on Educational Data Mining, 2009
This paper presents a model which can automatically detect a variety of student speech acts as students collaborate within a computer supported collaborative learning environment. In addition, an analysis is presented which gives substantial insight as to how students' learning is associated with students' speech acts, knowledge that will…
Descriptors: Data Analysis, Computer Uses in Education, Cooperative Learning, Speech Acts
Ben-Naim, Dror; Bain, Michael; Marcus, Nadine – International Working Group on Educational Data Mining, 2009
It has been recognized that in order to drive Intelligent Tutoring Systems (ITSs) into mainstream use by the teaching community, it is essential to support teachers through the entire ITS process: Design, Development, Deployment, Reflection and Adaptation. Although research has been done on supporting teachers through design to deployment of ITSs,…
Descriptors: Foreign Countries, Intelligent Tutoring Systems, Computer System Design, Computer Managed Instruction