Publication Date
In 2024 | 0 |
Since 2023 | 0 |
Since 2020 (last 5 years) | 4 |
Since 2015 (last 10 years) | 8 |
Since 2005 (last 20 years) | 11 |
Descriptor
Comparative Analysis | 11 |
Data Analysis | 8 |
Prediction | 5 |
Intelligent Tutoring Systems | 4 |
Models | 4 |
Accuracy | 3 |
Bayesian Statistics | 3 |
Computational Linguistics | 3 |
Factor Analysis | 3 |
Learning Analytics | 3 |
Scores | 3 |
More ▼ |
Source
Journal of Educational Data… | 11 |
Author
Bosch, Nigel | 1 |
Cardozo, Nicolás | 1 |
Cui, Jialin | 1 |
Flann, Nicholas | 1 |
Galyardt, April | 1 |
Gehringer, Edward | 1 |
Gervet, Theophile | 1 |
Goldin, Ilya | 1 |
Hao, Jiangang | 1 |
Jia, Qinjin | 1 |
Knowles, Jared E. | 1 |
More ▼ |
Publication Type
Journal Articles | 11 |
Reports - Research | 10 |
Reports - Evaluative | 1 |
Education Level
Higher Education | 3 |
Junior High Schools | 3 |
Middle Schools | 3 |
Postsecondary Education | 3 |
Secondary Education | 3 |
Elementary Education | 2 |
Grade 8 | 2 |
Grade 4 | 1 |
High Schools | 1 |
Intermediate Grades | 1 |
Audience
Laws, Policies, & Programs
Assessments and Surveys
National Assessment of… | 2 |
Motivated Strategies for… | 1 |
What Works Clearinghouse Rating
Sanguino, Juan Camilo; Manrique, Rubén; Mariño, Olga; Linares-Vásquez, Mario; Cardozo, Nicolás – Journal of Educational Data Mining, 2022
Recommender systems in educational contexts have proven to be effective in identifying learning resources that fit the interests and needs of learners. Their usage has been of special interest in online self-learning scenarios to increase student retention and improve the learning experience. In this article, we present the design of a hybrid…
Descriptors: Information Systems, Educational Resources, Independent Study, Online Courses
Jia, Qinjin; Young, Mitchell; Xiao, Yunkai; Cui, Jialin; Liu, Chengyuan; Rashid, Parvez; Gehringer, Edward – Journal of Educational Data Mining, 2022
Instant feedback plays a vital role in promoting academic achievement and student success. In practice, however, delivering timely feedback to students can be challenging for instructors for a variety of reasons (e.g., limited teaching resources). In many cases, feedback arrives too late for learners to act on the advice and reinforce their…
Descriptors: Student Projects, Learning Analytics, Intelligent Tutoring Systems, Feedback (Response)
Bosch, Nigel – Journal of Educational Data Mining, 2021
Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…
Descriptors: Accuracy, Learning Analytics, Models, National Competency Tests
Gervet, Theophile; Koedinger, Ken; Schneider, Jeff; Mitchell, Tom – Journal of Educational Data Mining, 2020
Intelligent tutoring systems (ITSs) teach skills using learning-by-doing principles and provide learners with individualized feedback and materials adapted to their level of understanding. Given a learner's history of past interactions with an ITS, a learner performance model estimates the current state of a learner's knowledge and predicts her…
Descriptors: Learning Processes, Intelligent Tutoring Systems, Feedback (Response), Knowledge Level
Galyardt, April; Goldin, Ilya – Journal of Educational Data Mining, 2015
In educational technology and learning sciences, there are multiple uses for a predictive model of whether a student will perform a task correctly or not. For example, an intelligent tutoring system may use such a model to estimate whether or not a student has mastered a skill. We analyze the significance of data recency in making such…
Descriptors: Achievement Rating, Performance Based Assessment, Bayesian Statistics, Data Analysis
Hao, Jiangang; Shu, Zhan; von Davier, Alina – Journal of Educational Data Mining, 2015
Students' activities in game/scenario-based tasks (G/SBTs) can be characterized by a sequence of time-stamped actions of different types with different attributes. For a subset of G/SBTs in which only the order of the actions is of great interest, the process data can be well characterized as a string of characters (i.e., action string) if we…
Descriptors: Task Analysis, Data Analysis, Vignettes, Correlation
Knowles, Jared E. – Journal of Educational Data Mining, 2015
The state of Wisconsin has one of the highest four year graduation rates in the nation, but deep disparities among student subgroups remain. To address this the state has created the Wisconsin Dropout Early Warning System (DEWS), a predictive model of student dropout risk for students in grades six through nine. The Wisconsin DEWS is in use…
Descriptors: Dropouts, Models, Prediction, Risk
Miller, L. Dee; Soh, Leen-Kiat; Samal, Ashok; Kupzyk, Kevin; Nugent, Gwen – Journal of Educational Data Mining, 2015
Learning objects (LOs) are important online resources for both learners and instructors and usage for LOs is growing. Automatic LO tracking collects large amounts of metadata about individual students as well as data aggregated across courses, learning objects, and other demographic characteristics (e.g. gender). The challenge becomes identifying…
Descriptors: Comparative Analysis, Data Analysis, Hierarchical Linear Modeling, Electronic Learning
Pavlik, Philip I., Jr. – Journal of Educational Data Mining, 2013
This paper describes the development of a dynamical systems model of motivation and metacognition during learning, which explains some of the practically and theoretically important relationships among three student engagement constructs and performance metrics during learning. In order to better calibrate and understand the model, the model was…
Descriptors: Vocabulary Development, Learning Strategies, Predictor Variables, Scores
Xu, Beijie; Recker, Mimi; Qi, Xiaojun; Flann, Nicholas; Ye, Lei – Journal of Educational Data Mining, 2013
This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data…
Descriptors: Electronic Libraries, Use Studies, Multivariate Analysis, Data Analysis
Sabourin, Jennifer L.; Rowe, Jonathan P.; Mott, Bradford W.; Lester, James C. – Journal of Educational Data Mining, 2013
Over the past decade, there has been growing interest in real-time assessment of student engagement and motivation during interactions with educational software. Detecting symptoms of disengagement, such as off-task behavior, has shown considerable promise for understanding students' motivational characteristics during learning. In this paper, we…
Descriptors: Student Behavior, Classification, Learner Engagement, Data Analysis