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Yasuda, Jun-ichro; Mae, Naohiro; Hull, Michael M.; Taniguchi, Masa-aki – Physical Review Physics Education Research, 2018
In this study, we analyze the systematic error from false positives of the Force Concept Inventory (FCI). We compare the systematic errors of question 6 (Q.6), Q.7, and Q.16, for which clearly erroneous reasoning has been found, with Q.5, for which clearly erroneous reasoning has not been found. We determine whether or not a correct response to a…
Descriptors: Scientific Concepts, Science Instruction, College Science, Physics
Leighton, Jacqueline P.; Tang, Wei; Guo, Qi – Assessment & Evaluation in Higher Education, 2018
The objective of the present study was to better understand a relatively under-researched topic, namely, undergraduate students' attitudes towards mistakes and how their attitudes relate to academic achievement. A series of online surveys were administered to a sample of 207 first- and second-year undergraduate students. Using structural…
Descriptors: Undergraduate Students, Student Attitudes, Error Patterns, Academic Achievement
Balanga, Roselle A.; Fidel, Irish Van B.; Gumapac, Mone Virma Ginry P.; Ho, Howell T.; Tullo, Riza Mae C.; Villaraza, Patricia Monette L.; Vizconde, Camilla J. – Journal on English Language Teaching, 2016
The study identified the beliefs of high school students toward Written Corrective Feedback (WCF), based on the framework of Anderson (2010). It also investigated the most common errors that students commit in writing stories and the type of WCF students receive from teachers. Data in the form of stories which were checked by teachers were…
Descriptors: Foreign Countries, High School Students, Student Attitudes, Beliefs
Becker, Brett A.; Glanville, Graham; Iwashima, Ricardo; McDonnell, Claire; Goslin, Kyle; Mooney, Catherine – Computer Science Education, 2016
Programming is an essential skill that many computing students are expected to master. However, programming can be difficult to learn. Successfully interpreting compiler error messages (CEMs) is crucial for correcting errors and progressing toward success in programming. Yet these messages are often difficult to understand and pose a barrier to…
Descriptors: Computer Science Education, Programming, Novices, Error Patterns
Ayachi, Zeineb – Reading Matrix: An International Online Journal, 2017
This study investigated the difference between peer and teacher assessment of writing compositions of advanced university students in English. Besides, it investigated the likely effect this type of evaluation might have on the learners' writing skill. To this end, 17 participants were surveyed over seven weeks. During every session, the…
Descriptors: Peer Evaluation, English (Second Language), Second Language Learning, Second Language Instruction
Adesemowo, A. Kayode; Johannes, Hermien; Goldstone, Simon; Terblanche, Koshala – Africa Education Review, 2016
This article illustrates the experience of introducing e-assessment to a foundational ICT networking course for a "large" group of first-year students at a South African Higher Education Institution. The study employed a mix of participatory action research, systems design, and survey (quantitative and qualitative) analysis over an…
Descriptors: Foreign Countries, College Freshmen, Social Networks, Introductory Courses
Bringula, Rex P.; Manabat, Geecee Maybelline A.; Tolentino, Miguel Angelo A.; Torres, Edmon L. – World Journal of Education, 2012
This descriptive study determined which of the sources of errors would predict the errors committed by novice Java programmers. Descriptive statistics revealed that the respondents perceived that they committed the identified eighteen errors infrequently. Thought error was perceived to be the main source of error during the laboratory programming…
Descriptors: Error Patterns, Programming, Programming Languages, Predictor Variables
Schochet, Peter Z. – Journal of Educational and Behavioral Statistics, 2013
In education randomized control trials (RCTs), the misreporting of student outcome data could lead to biased estimates of average treatment effects (ATEs) and their standard errors. This article discusses a statistical model that adjusts for misreported binary outcomes for two-level, school-based RCTs, where it is assumed that misreporting could…
Descriptors: Control Groups, Experimental Groups, Educational Research, Data Analysis
Schochet, Peter Z. – Society for Research on Educational Effectiveness, 2013
In randomized control trials (RCTs) of educational interventions, there is a growing literature on impact estimation methods to adjust for missing student outcome data using such methods as multiple imputation, the construction of nonresponse weights, casewise deletion, and maximum likelihood methods (see, for example, Allison, 2002; Graham, 2009;…
Descriptors: Control Groups, Experimental Groups, Educational Research, Data Analysis

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