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Showing 61 to 75 of 109 results Save | Export
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Khosravi, Hassan; Kitto, Kirsty; Cooper, Kendra – Journal of Educational Data Mining, 2017
Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests…
Descriptors: Teaching Methods, College Students, Educational Technology, Technology Uses in Education
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Agrawal, Rakesh; Golshan, Behzad; Papalexakis, Evangelos – Journal of Educational Data Mining, 2016
A study plan is the choice of concepts and the organization and sequencing of the concepts to be covered in an educational course. While a good study plan is essential for the success of any course offering, the design of study plans currently remains largely a manual task. We present a novel data-driven method, which given a list of concepts can…
Descriptors: Lesson Plans, Educational Planning, Curriculum Design, Evidence Based Practice
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Sweeney, Mack; Rangwala, Huzefa; Lester, Jaime; Johri, Aditya – Journal of Educational Data Mining, 2016
An enduring issue in higher education is student retention to successful graduation. National statistics indicate that most higher education institutions have four-year degree completion rates around 50%, or just half of their student populations. While there are prediction models which illuminate what factors assist with college student success,…
Descriptors: Systems Approach, Data Analysis, Prediction, Academic Achievement
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Yang, Diyi; Kraut, Robert E.; Rose, Carolyn P. – Journal of Educational Data Mining, 2016
Although thousands of students enroll in Massive Open Online Courses (MOOCs) for learning and self-improvement, many get confused, harming learning and increasing dropout rates. In this paper, we quantify these effects in two large MOOCs. We first describe how we automatically estimate students' confusion by looking at their clicking behavior on…
Descriptors: Online Courses, Dropouts, Dropout Rate, Correlation
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Spoon, Kelly; Beemer, Joshua; Whitmer, John C.; Fan, Juanjuan; Frazee, James P.; Stronach, Jeanne; Bohonak, Andrew J.; Levine, Richard A. – Journal of Educational Data Mining, 2016
Random forests are presented as an analytics foundation for educational data mining tasks. The focus is on course- and program-level analytics including evaluating pedagogical approaches and interventions and identifying and characterizing at-risk students. As part of this development, the concept of individualized treatment effects (ITE) is…
Descriptors: Data Analysis, Individualized Instruction, Teaching Methods, Intervention
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Sonnenberg, Christoph; Bannert, Maria – Journal of Educational Data Mining, 2016
In computer-supported learning environments, the deployment of self-regulatory skills represents an essential prerequisite for successful learning. Metacognitive prompts are a promising type of instructional support to activate students' strategic learning activities. However, despite positive effects in previous studies, there are still a large…
Descriptors: Data Analysis, Metacognition, Prompting, Cues
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Crossley, Scott A.; Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S. – Journal of Educational Data Mining, 2016
This study investigates a novel approach to automatically assessing essay quality that combines natural language processing approaches that assess text features with approaches that assess individual differences in writers such as demographic information, standardized test scores, and survey results. The results demonstrate that combining text…
Descriptors: Essays, Scoring, Writing Evaluation, Natural Language Processing
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Pelanek, Radek – Journal of Educational Data Mining, 2015
Researchers use many different metrics for evaluation of performance of student models. The aim of this paper is to provide an overview of commonly used metrics, to discuss properties, advantages, and disadvantages of different metrics, to summarize current practice in educational data mining, and to provide guidance for evaluation of student…
Descriptors: Models, Data Analysis, Data Processing, Evaluation Criteria
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Clement, Benjamin; Roy, Didier; Oudeyer, Pierre-Yves; Lopes, Manuel – Journal of Educational Data Mining, 2015
We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two…
Descriptors: Learning Activities, Intelligent Tutoring Systems, Models, Teaching Methods
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Mallavarapu, Aditi; Lyons, Leilah; Shelley, Tia; Minor, Emily; Slattery, Brian; Zellner, Moria – Journal of Educational Data Mining, 2015
Interactive learning environments can provide learners with opportunities to explore rich, real-world problem spaces, but the nature of these problem spaces can make assessing learner progress difficult. Such assessment can be useful for providing formative and summative feedback to the learners, to educators, and to the designers of the…
Descriptors: Spatial Ability, Urban Areas, Neighborhoods, Conservation (Environment)
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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
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Saarela, Mirka; Karkkainen, Tommi – Journal of Educational Data Mining, 2015
Curricula for Computer Science (CS) degrees are characterized by the strong occupational orientation of the discipline. In the BSc degree structure, with clearly separate CS core studies, the learning skills for these and other required courses may vary a lot, which is shown in students' overall performance. To analyze this situation, we apply…
Descriptors: Data Analysis, Academic Achievement, Undergraduate Students, Core Curriculum
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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
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Ezen-Can, Aysu; Boyer, Kristy Elizabeth – Journal of Educational Data Mining, 2015
Within the landscape of educational data, textual natural language is an increasingly vast source of learning-centered interactions. In natural language dialogue, student contributions hold important information about knowledge and goals. Automatically modeling the dialogue act of these student utterances is crucial for scaling natural language…
Descriptors: Classification, Dialogs (Language), Computational Linguistics, Information Retrieval
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Zimmermann, Judith; Brodersen, Kay H.; Heinimann, Hans R.; Buhmann, Joachim M. – Journal of Educational Data Mining, 2015
The graduate admissions process is crucial for controlling the quality of higher education, yet, rules-of-thumb and domain-specific experiences often dominate evidence-based approaches. The goal of the present study is to dissect the predictive power of undergraduate performance indicators and their aggregates. We analyze 81 variables in 171…
Descriptors: Undergraduate Students, Graduate Students, Academic Achievement, Prediction
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