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Bütüner, Suphi Önder; Filiz, Mehmet – International Journal of Mathematical Education in Science and Technology, 2017
The aim of this research was to investigate high achievers' erroneous answers and misconceptions on the angle concept. The participants consisted of 233 grade 6 students drawn from eight classes in two well-established elementary schools of Trabzon, Turkey. All the participants were considered to be current achievers in mathematics, graded 4 or 5…
Descriptors: High Achievement, Grade 6, Misconceptions, Foreign Countries
Sole, Marla A. – Mathematics Teacher, 2015
Every day, people use data to make decisions that affect their personal and professional lives, trusting that the data are correct. Many times, however, the data are inaccurate, as a result of a flaw in the design or methodology of the survey used to collect the data. Researchers agree that only questions that are clearly worded, unambiguous, free…
Descriptors: Test Construction, Surveys, Student Participation, Design
Cafarella, Brian V. – Research & Teaching in Developmental Education, 2016
Due to poor student success rates in developmental mathematics, many institutions have implemented various forms of redesign into their developmental math curricula. Since the goal of redesign is to increase student success, it is salient to explore all aspects of the redesign process. Many studies have focused on the positive outcomes of redesign…
Descriptors: Misconceptions, Instructional Design, Developmental Programs, Mathematics Education
Gardner, Kimberly – Teaching Statistics: An International Journal for Teachers, 2013
Statistics is an integral part of the K-12 mathematics curriculum (age 5-18). Naturally, students construct misconceptions of what they learn. This article discusses The Bop It[C]Challenge, a review activity assesses student understanding and reveals their misunderstandings of statistical concepts. (Contains 3 figures and 1 table.)
Descriptors: Statistics, Mathematics Instruction, Misconceptions, Mathematical Concepts
Muzheve, Michael T.; Capraro, Robert M. – Journal of Mathematical Behavior, 2012
Using qualitative data collection and analyses techniques, we examined mathematical representations used by sixteen (N=16) teachers while teaching the concepts of converting among fractions, decimals, and percents. We also studied representational choices by their students (N=581). In addition to using geometric figures and manipulatives, teachers…
Descriptors: Geometric Concepts, Mathematics, Misconceptions, Natural Language Processing
Boyer, Kristy Elizabeth, Ed.; Yudelson, Michael, Ed. – International Educational Data Mining Society, 2018
The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…
Descriptors: Data Collection, Data Analysis, Computer Science Education, Program Proposals
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis