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Luz, Yael; Yerushalmy, Michal – Journal for Research in Mathematics Education, 2023
We report on an innovative design of algorithmic analysis that supports automatic online assessment of students' exploration of geometry propositions in a dynamic geometry environment. We hypothesized that difficulties with and misuse of terms or logic in conjectures are rooted in the early exploration stages of inquiry. We developed a generic…
Descriptors: Algorithms, Computer Assisted Testing, Geometry, Mathematics Instruction
Zhang, Mengxue; Wang, Zichao; Baraniuk, Richard; Lan, Andrew – International Educational Data Mining Society, 2021
Feedback on student answers and even during intermediate steps in their solutions to open-ended questions is an important element in math education. Such feedback can help students correct their errors and ultimately lead to improved learning outcomes. Most existing approaches for automated student solution analysis and feedback require manually…
Descriptors: Mathematics Instruction, Teaching Methods, Intelligent Tutoring Systems, Error Patterns
Strickland, S.; Rand, B. – PRIMUS, 2016
This paper describes a framework for identifying, classifying, and coding student proofs, modified from existing proof-grading rubrics. The framework includes 20 common errors, as well as categories for interpreting the severity of the error. The coding scheme is intended for use in a classroom context, for providing effective student feedback. In…
Descriptors: Guidelines, Undergraduate Students, Classification, Mathematics Instruction
Tooher, Helen; Johnson, Patrick – Issues in Educational Research, 2020
This pilot study explores the effectiveness of a strategy for overcoming post-primary students' misconceptions within the topic of algebra. Although central to the study of mathematics, algebra can be an area of difficulty for many students. A misconception is typically classified as flawed understanding of a concept causing repeated errors, and…
Descriptors: Misconceptions, Algebra, Secondary School Students, Mathematics Instruction
Rakes, Christopher R.; Ronau, Robert N. – International Journal of Research in Education and Science, 2019
The present study examined the ability of content domain (algebra, geometry, rational number, probability) to classify mathematics misconceptions. The study was conducted with 1,133 students in 53 algebra and geometry classes taught by 17 teachers from three high schools and one middle school across three school districts in a Midwestern state.…
Descriptors: Mathematics Instruction, Secondary School Teachers, Middle School Teachers, Misconceptions
Pelánek, Radek; Rihák, Ji?rí – International Educational Data Mining Society, 2016
In online educational systems we can easily collect and analyze extensive data about student learning. Current practice, however, focuses only on some aspects of these data, particularly on correctness of students answers. When a student answers incorrectly, the submitted wrong answer can give us valuable information. We provide an overview of…
Descriptors: Foreign Countries, Online Systems, Geography, Anatomy
Siyepu, Sibawu Witness – International Journal of STEM Education, 2015
Background: This article reports on an analysis of errors that were displayed by students who studied mathematics in Chemical Engineering in derivatives of mostly trigonometric functions. The poor performance of these students triggered this study. The researcher (lecturer) works in a mathematics support programme to enhance students'…
Descriptors: Mathematics Instruction, Error Patterns, Qualitative Research, Case Studies
Wang, Yutao; Heffernan, Neil T.; Heffernan, Cristina – Grantee Submission, 2015
The well-studied Baker et al., affect detectors on boredom, frustration, confusion and engagement concentration with ASSISTments dataset were used to predict state tests scores, college enrollment, and even whether a student majored in a STEM field. In this paper, we present three attempts to improve upon current affect detectors. The first…
Descriptors: Majors (Students), Affective Behavior, Psychological Patterns, Predictor Variables
Yang, Chin Wen; Sherman, Helene; Murdick, Nikki – Investigations in Mathematics Learning, 2011
The purpose of this research study was to investigate and classify particular categories of mathematical errors made by students with Limited English Proficiency. Participants included 15 general education teachers, two English as Second Language teachers, and 91 Limited English Proficiency students. General education teachers provided mathematics…
Descriptors: Intervention, Content Validity, Interrater Reliability, Error Patterns
Luneta, Kakoma; Makonye, Paul J. – Acta Didactica Napocensia, 2010
The paper focuses on analysing grade 12 learner errors and the misconceptions in calculus at a secondary school in Limpopo Province, South Africa. As part of the analysis the paper outlines the nature of mathematics errors and misconceptions. Coding of learners' errors was done through the lens of a typological framework. The analysis showed that…
Descriptors: Case Studies, Grade 12, Foreign Countries, Error Patterns
Selden, Annie; Selden, John – Online Submission, 2003
In this paper we describe a number of types of errors and underlying misconceptions that arise in mathematical reasoning. Other types of mathematical reasoning errors, not associated with specific misconceptions, are also discussed. We hope the characterization and cataloging of common reasoning errors will be useful in studying the teaching of…
Descriptors: Educational Strategies, Research Methodology, Misconceptions, Error Patterns
Borasi, Raffaella – 1989
The purpose of this study is to contribute to an understanding of how errors could be employed in mathematics instruction so that the students use them constructively in support of their learning of mathematics. A teaching experiment was designed to create an ideal context in which the pedagogical approach to errors as springboards could be…
Descriptors: Classification, Definitions, Error Patterns, Mathematical Concepts
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
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