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Paquette, Luc; Rowe, Jonathan; Baker, Ryan; Mott, Bradford; Lester, James; DeFalco, Jeanine; Brawner, Keith; Sottilare, Robert; Georgoulas, Vasiliki – International Educational Data Mining Society, 2016
Computational models that automatically detect learners' affective states are powerful tools for investigating the interplay of affect and learning. Over the past decade, affect detectors--which recognize learners' affective states at run-time using behavior logs and sensor data--have advanced substantially across a range of K-12 and postsecondary…
Descriptors: Models, Affective Behavior, Intelligent Tutoring Systems, Games
Santos, Olga Cristina, Ed.; Boticario, Jesus Gonzalez, Ed.; Romero, Cristobal, Ed.; Pechenizkiy, Mykola, Ed.; Merceron, Agathe, Ed.; Mitros, Piotr, Ed.; Luna, Jose Maria, Ed.; Mihaescu, Cristian, Ed.; Moreno, Pablo, Ed.; Hershkovitz, Arnon, Ed.; Ventura, Sebastian, Ed.; Desmarais, Michel, Ed. – International Educational Data Mining Society, 2015
The 8th International Conference on Educational Data Mining (EDM 2015) is held under auspices of the International Educational Data Mining Society at UNED, the National University for Distance Education in Spain. The conference held in Madrid, Spain, July 26-29, 2015, follows the seven previous editions (London 2014, Memphis 2013, Chania 2012,…
Descriptors: Data Analysis, Educational Research, Computer Uses in Education, Integrated Learning Systems
Bergner, Yoav; Kerr, Deirdre; Pritchard, David E. – International Educational Data Mining Society, 2015
Determining how learners use MOOCs effectively is critical to providing feedback to instructors, schools, and policy-makers on this highly scalable technology. However, drawing inferences about student learning outcomes in MOOCs has proven to be quite difficult due to large amounts of missing data (of various kinds) and to the diverse population…
Descriptors: Online Courses, Data Analysis, Discussion Groups, Outcomes of Education
Nižnan, Juraj; Pelánek, Radek; Rihák, Jirí – International Educational Data Mining Society, 2015
Intelligent behavior of adaptive educational systems is based on student models. Most research in student modeling focuses on student learning (acquisition of skills). We focus on prior knowledge, which gets much less attention in modeling and yet can be highly varied and have important consequences for the use of educational systems. We describe…
Descriptors: Prior Learning, Models, Intelligent Tutoring Systems, Bayesian Statistics
Chen, Yang; Wuillemin, Pierre-Henr; Labat, Jean-Marc – International Educational Data Mining Society, 2015
Estimating the prerequisite structure of skills is a crucial issue in domain modeling. Students usually learn skills in sequence since the preliminary skills need to be learned prior to the complex skills. The prerequisite relations between skills underlie the design of learning sequence and adaptation strategies for tutoring systems. The…
Descriptors: Skills, Data Analysis, Students, Performance
Rollinson, Joseph; Brunskill, Emma – International Educational Data Mining Society, 2015
At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way…
Descriptors: Prediction, Models, Educational Policy, Intelligent Tutoring Systems
Huang, Yun; González-Brenes, José P.; Kumar, Rohit; Brusilovsky, Peter – International Educational Data Mining Society, 2015
Latent variable models, such as the popular Knowledge Tracing method, are often used to enable adaptive tutoring systems to personalize education. However, finding optimal model parameters is usually a difficult non-convex optimization problem when considering latent variable models. Prior work has reported that latent variable models obtained…
Descriptors: Guidelines, Models, Prediction, Evaluation Methods
Desmarais, Michel C.; Xu, Peng; Beheshti, Behzad – International Educational Data Mining Society, 2015
The problem of mapping items to skills is gaining interest with the emergence of recent techniques that can use data for both defining this mapping, and for refining mappings given by experts. We investigate the problem of refining mapping from an expert by combining the output of different techniques. The combination is based on a partition tree…
Descriptors: Matrices, Test Items, Skills, Expertise
Bhat, Suma; Chinprutthiwong, Phakpoom; Perry, Michelle – International Educational Data Mining Society, 2015
Instructional content designers of online learning platforms are concerned about optimal video design guidelines that ensure course effectiveness, while keeping video production time and costs at reasonable levels. In order to address the concern, we use clickstream data from one Coursera course to analyze the engagement, motivational and…
Descriptors: Video Technology, Electronic Learning, Learner Engagement, Student Motivation
Streeter, Matthew – International Educational Data Mining Society, 2015
We show that student learning can be accurately modeled using a mixture of learning curves, each of which specifies error probability as a function of time. This approach generalizes Knowledge Tracing [7], which can be viewed as a mixture model in which the learning curves are step functions. We show that this generality yields order-of-magnitude…
Descriptors: Probability, Error Patterns, Learning Processes, Models
Bumbacher, Engin; Salehi, Shima; Wierzchula, Miriam; Blikstein, Paulo – International Educational Data Mining Society, 2015
Studies comparing virtual and physical manipulative environments (VME and PME) in inquiry-based science learning have mostly focused on students' learning outcomes but not on the actual processes they engage in during the learning activities. In this paper, we examined experimentation strategies in an inquiry activity and their relation to…
Descriptors: Physics, Science Instruction, College Students, Predictor Variables
Truong, Huong May – International Educational Data Mining Society, 2015
This paper provides an overview and update on my PhD research project which focuses on integrating learning styles into adaptive e-learning system. The project, firstly, aims to develop a system to classify students' learning styles through their online learning behaviour. This will be followed by a study on the complex relationship between…
Descriptors: Cognitive Style, Integrated Activities, Electronic Learning, Research Projects
San Pedro, Maria Ofelia Z. – International Educational Data Mining Society, 2015
This dissertation research focuses on assessing student behavior, academic emotions, and knowledge from a middle school online learning environment, and analyzing their potential effects on decisions about going to college. Using students' longitudinal data ranging from their middle school, to high school, to postsecondary years, I leverage…
Descriptors: Learner Engagement, Psychological Patterns, Middle School Students, Student Behavior
Nižnan, Juraj – International Educational Data Mining Society, 2015
Estimation is useful in situations where an exact answer is not as important as a quick answer that is good enough. A web-based adaptive system for practicing estimates is currently being developed. We propose a simple model for estimating student's latent skill of estimation. This model combines a continuous measure of correctness and response…
Descriptors: Accuracy, Computation, Models, Item Response Theory
Mills, Caitlin; D'Mello, Sidney – International Educational Data Mining Society, 2015
This paper reports the results from a sensor-free detector of mind wandering during an online reading task. Features consisted of reading behaviors (e.g., reading time) and textual features (e.g., level of difficulty) extracted from self-paced reading log files. Supervised machine learning was applied to two datasets in order to predict if…
Descriptors: Reading, Identification, Attention, Reading Rate
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