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Liu, Ran; Koedinger, Kenneth R. – Journal of Educational Data Mining, 2017
As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it…
Descriptors: Educational Technology, Technology Uses in Education, Data Collection, Data Analysis
MacLellan, Christopher J.; Liu, Ran; Koedinger, Kenneth R. – International Educational Data Mining Society, 2015
Additive Factors Model (AFM) and Performance Factors Analysis (PFA) are two popular models of student learning that employ logistic regression to estimate parameters and predict performance. This is in contrast to Bayesian Knowledge Tracing (BKT) which uses a Hidden Markov Model formalism. While all three models tend to make similar predictions,…
Descriptors: Factor Analysis, Regression (Statistics), Knowledge Level, Markov Processes
Pavlik, Philip I., Jr.; Yudelson, Michael; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2015
Efforts to improve instructional task design often make reference to the mental structures, such as "schemas" (e.g., Gick & Holyoak, 1983) or "identical elements" (Thorndike & Woodworth, 1901), that are common to both the instructional and target tasks. This component based (e.g., Singley & Anderson, 1989) approach…
Descriptors: Models, Measurement, Instructional Design, Transfer of Training
Pavlik, Philip I., Jr.; Yudelson, Michael; Koedinger, Kenneth R. – Society for Research on Educational Effectiveness, 2011
The objective of this research was to better understand the transfer of learning between different variations of pre-algebra problems. While the authors could have addressed a specific variation that might address transfer, they were interested in developing a general model of transfer, so we gathered data from multiple problem types and their…
Descriptors: Transfer of Training, Item Analysis, Educational Technology, Algebra
Pavlik, Philip I., Jr.; Cen, Hao; Koedinger, Kenneth R. – Online Submission, 2009
Knowledge tracing (KT)[1] has been used in various forms for adaptive computerized instruction for more than 40 years. However, despite its long history of application, it is difficult to use in domain model search procedures, has not been used to capture learning where multiple skills are needed to perform a single action, and has not been used…
Descriptors: Performance Factors, Factor Analysis, Computer Software, Computer Assisted Instruction
Pavlik, Philip I. Jr.; Cen, Hao; Koedinger, Kenneth R. – Online Submission, 2009
This paper describes a novel method to create a quantitative model of an educational content domain of related practice item-types using learning curves. By using a pairwise test to search for the relationships between learning curves for these item-types, we show how the test results in a set of pairwise transfer relationships that can be…
Descriptors: Instructional Design, Test Results, Models, Pattern Recognition