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Kim, Eun Mi; Oláh, Leslie Nabors; Peters, Stephanie – ETS Research Report Series, 2020
K-12 students are expected to acquire competence in data display as part of developing statistical literacy. To support research, assessment design, and instruction, we developed a hypothesized learning progression (LP) using existing empirical literature in the fields of mathematics and statistics education. The data display LP posits a…
Descriptors: Mathematics Education, Statistics Education, Teaching Methods, Data Analysis
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Fife, James H.; James, Kofi; Peters, Stephanie – ETS Research Report Series, 2020
The concept of variability is central to statistics. In this research report, we review mathematics education research on variability and, based on that review and on feedback from an expert panel, propose a learning progression (LP) for variability. The structure of the proposed LP consists of 5 levels of sophistication in understanding…
Descriptors: Mathematics Education, Statistics Education, Feedback (Response), Research Reports
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Xiong, Xiaolu; Zhao, Siyuan; Van Inwegen, Eric G.; Beck, Joseph E. – International Educational Data Mining Society, 2016
Over the last couple of decades, there have been a large variety of approaches towards modeling student knowledge within intelligent tutoring systems. With the booming development of deep learning and large-scale artificial neural networks, there have been empirical successes in a number of machine learning and data mining applications, including…
Descriptors: Intelligent Tutoring Systems, Computer Software, Bayesian Statistics, Knowledge Level
Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam – International Educational Data Mining Society, 2015
This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…
Descriptors: Learning Activities, Learning Processes, Data Collection, Student Behavior
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Lowes, Susan; Lin, Peiyi; Wang, Yan – Journal of Interactive Online Learning, 2007
As online professional development courses for teachers have grown, the discussion forum has become a locus of considerable research. This study analyzes the discussion forums in four different sessions of a short (4-week) online course for teachers from six schools in three states. This study also compares four methodologies, all of which have a…
Descriptors: Faculty Development, Online Courses, Program Effectiveness, Computer Mediated Communication
Wash, James A., Jr. – 1967
An evaluation of materials in the sequential anthropology curriculum project with regard to student learning and teacher training is presented. The project sample consisted of 2,183 students in grades 1, 2, 4, and 5. Two forms of the anthropology achievement test were administered as pretest and posttest measures to experimental and control groups…
Descriptors: Achievement, Achievement Rating, Achievement Tests, Anthropology
Fishburne, Robert Purdy, Jr. – 1971
Pupil learning about evolution under different types of instruction is examined. A branching style program in which written responses were required was compared with a program in which the same material was presented in straight narrative style. The project compiled and analyzed test results from 115 fifth-grade students. The students were divided…
Descriptors: Anthropology, Cognitive Measurement, Comparative Analysis, Data Analysis
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection