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Sayac, Nathalie; Veldhuis, Michiel – International Journal of Science and Mathematics Education, 2022
We investigated French primary school teachers' assessment practice in mathematics. Using an online questionnaire on teachers' background, teaching, and grading practice, we were able to determine assessment profiles of 604 primary school teachers. As evidenced by the teachers' scores on the latent factors Assessment purposes, Assessment…
Descriptors: Foreign Countries, Elementary School Teachers, Mathematics Instruction, Evaluation Methods
Rockliffe, Andrew; Mckay, Jane – Research in Education, 2023
In this paper, we present a novel approach to defining, teaching, and assessing creativity by examining its origins and delineating the processes involved. The rationale for introducing this framework developed from studying existing thinking and questioning the current metrics for measuring creativity, which we posit are unfit for purpose. We…
Descriptors: Teaching Methods, Creative Teaching, Creativity, Learning Processes
Kunter, Mareike; Baumert, Jurgen – Learning Environments Research, 2006
In this article, we examine the construct and criteria validity of student and teacher ratings as indicators of instructional features. As parsimonious measures, student and teacher reports are frequently used to assess aspects of the learning environment. From a methodological perspective, however, both approaches have been questioned. Whereas…
Descriptors: Student Attitudes, Construct Validity, Evaluation Criteria, Teacher Attitudes
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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