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Per Nilsson; Andreas Eckert – Mathematical Thinking and Learning: An International Journal, 2024
This study contributes to the call for influencing practice by increasing attention to how learning environments can be designed to support learning in statistical inference. We report on a design experiment in secondary school (students 14-16 years old), that resulted in a set of lessons with the learning goal of teaching students how to apply…
Descriptors: Mathematics Instruction, Teaching Methods, Hypothesis Testing, Secondary School Students
Sun-Joo Cho; Amanda Goodwin; Matthew Naveiras; Paul De Boeck – Grantee Submission, 2024
Explanatory item response models (EIRMs) have been applied to investigate the effects of person covariates, item covariates, and their interactions in the fields of reading education and psycholinguistics. In practice, it is often assumed that the relationships between the covariates and the logit transformation of item response probability are…
Descriptors: Item Response Theory, Test Items, Models, Maximum Likelihood Statistics
Sun-Joo Cho; Amanda Goodwin; Matthew Naveiras; Paul De Boeck – Journal of Educational Measurement, 2024
Explanatory item response models (EIRMs) have been applied to investigate the effects of person covariates, item covariates, and their interactions in the fields of reading education and psycholinguistics. In practice, it is often assumed that the relationships between the covariates and the logit transformation of item response probability are…
Descriptors: Item Response Theory, Test Items, Models, Maximum Likelihood Statistics
Shin, Hyo Jeong; Wilson, Mark; Choi, In-Hee – Journal of Educational Measurement, 2017
This study proposes a structured constructs model (SCM) to examine measurement in the context of a multidimensional learning progression (LP). The LP is assumed to have features that go beyond a typical multidimentional IRT model, in that there are hypothesized to be certain cross-dimensional linkages that correspond to requirements between the…
Descriptors: Middle School Students, Student Evaluation, Measurement Techniques, Learning Processes
Horiguchi, Tomoya; Imai, Isao; Toumoto, Takahito; Hirashima, Tsukasa – Educational Technology & Society, 2014
Error-based simulation (EBS) has been developed to generate phenomena by using students' erroneous ideas and also offers promise for promoting students' awareness of errors. In this paper, we report the evaluation of EBS used in learning "normal reaction" in a junior high school. An EBS class, where students learned the concept…
Descriptors: Simulation, Error Correction, Learning Processes, Misconceptions
Franklin, Teresa J. – Journal of Educational Technology, 2008
This paper presents the development of a 3-D virtual environment in Second Life for the delivery of standards-based science content for middle school students in the rural Appalachian region of Southeast Ohio. A mixed method approach in which quantitative results of improved student learning and qualitative observations of implementation within…
Descriptors: Middle School Students, Secondary School Science, Science Education, Computer Simulation
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries
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

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