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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
Abbas, Safia; Sawamura, Hajime – International Working Group on Educational Data Mining, 2009
This paper presents an agent-based educational environment to teach argument analysis (ALES). The idea is based on the Argumentation Interchange Format Ontology (AIF)using "Walton Theory". ALES uses different mining techniques to manage a highly structured arguments repertoire. This repertoire was designed, developed and implemented by us. Our aim…
Descriptors: Data Analysis, Persuasive Discourse, Intelligent Tutoring Systems, Models
Nagata, Ryo; Takeda, Keigo; Suda, Koji; Kakegawa, Junichi; Morihiro, Koichiro – International Working Group on Educational Data Mining, 2009
This paper proposes a novel method for recommending books to pupils based on a framework called Edu-mining. One of the properties of the proposed method is that it uses only loan histories (pupil ID, book ID, date of loan) whereas the conventional methods require additional information such as taste information from a great number of users which…
Descriptors: Data Analysis, Books, Automation, Library Services
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
Ritter, Steven; Harris, Thomas K.; Nixon, Tristan; Dickison, Daniel; Murray, R. Charles; Towle, Brendon – International Working Group on Educational Data Mining, 2009
In Cognitive Tutors, student skill is represented by estimates of student knowledge on various knowledge components. The estimate for each knowledge component is based on a four-parameter model developed by Corbett and Anderson [Nb]. In this paper, we investigate the nature of the parameter space defined by these four parameters by modeling data…
Descriptors: Data Analysis, Intelligent Tutoring Systems, Knowledge Level, Skills
Barker-Plummer, Dave; Cox, Richard; Dale, Robert – International Working Group on Educational Data Mining, 2009
In this paper, we present a study of a large corpus of student logic exercises in which we explore the relationship between two distinct measures of difficulty: the proportion of students whose initial attempt at a given natural language to first-order logic translation is incorrect, and the average number of attempts that are required in order to…
Descriptors: Data Analysis, Logical Thinking, Difficulty Level, Assignments
Ayers, Elizabeth; Nugent, Rebecca; Dean, Nema – International Working Group on Educational Data Mining, 2009
A fundamental goal of educational research is identifying students' current stage of skill mastery (complete/partial/none). In recent years a number of cognitive diagnosis models have become a popular means of estimating student skill knowledge. However, these models become difficult to estimate as the number of students, items, and skills grows.…
Descriptors: Data Analysis, Skills, Knowledge Level, Students
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
Vialardi, Cesar; Bravo, Javier; Shafti, Leila; Ortigosa, Alvaro – International Working Group on Educational Data Mining, 2009
One of the main problems faced by university students is to take the right decision in relation to their academic itinerary based on available information (for example courses, schedules, sections, classrooms and professors). In this context, this work proposes the use of a recommendation system based on data mining techniques to help students to…
Descriptors: Data Analysis, Higher Education, Course Selection (Students), Enrollment
Hardof-Jaffe, Sharon; Hershkovitz, Arnon; Abu-Kishk, Hama; Bergman, Ofer; Nachmias, Rafi – International Working Group on Educational Data Mining, 2009
The purpose of this study is to empirically reveal strategies of students' organization of learning-related digital materials within an online personal information archive. Research population included 518 students who utilized the personal Web space allocated to them on the university servers for archiving information items, and data describing…
Descriptors: Data Analysis, Organization, Information Management, Undergraduate Students
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
Stamper, John; Barnes, Tiffany – International Working Group on Educational Data Mining, 2009
We seek to simplify the creation of intelligent tutors by using student data acquired from standard computer aided instruction (CAI) in conjunction with educational data mining methods to automatically generate adaptive hints. In our previous work, we have automatically generated hints for logic tutoring by constructing a Markov Decision Process…
Descriptors: Data Analysis, Computer Assisted Instruction, Intelligent Tutoring Systems, Markov Processes
Gong, Yue; Rai, Dovan; Beck, Joseph E.; Heffernan, Neil T. – International Working Group on Educational Data Mining, 2009
In this study, we are interested to see the impact of self-discipline on students' knowledge and learning. Self-discipline can influence both learning rate as well as knowledge accumulation over time. We used a Knowledge Tracing (KT) model to make inferences about students' knowledge and learning. Based on a widely used questionnaire, we measured…
Descriptors: Data Analysis, Self Control, Knowledge Level, Learning
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
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