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Jahnke, Maximilian; Höppner, Frank – International Educational Data Mining Society, 2022
The value of an instructor is that she exactly recognizes what the learner is struggling with and provides constructive feedback straight to the point. This work aims at a step towards this type of feedback in the context of an introductory programming course, where students perform program execution tracing to align their understanding of Java…
Descriptors: Programming, Coding, Computer Science Education, Error Patterns
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Liu, Chengyuan; Cui, Jialin; Shang, Ruixuan; Xiao, Yunkai; Jia, Qinjin; Gehringer, Edward – International Educational Data Mining Society, 2022
An online peer-assessment system typically allows students to give textual feedback to their peers, with the goal of helping the peers improve their work. The amount of help that students receive is highly dependent on the quality of the reviews. Previous studies have investigated using machine learning to detect characteristics of reviews (e.g.,…
Descriptors: Peer Evaluation, Feedback (Response), Computer Mediated Communication, Teaching Methods
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Nguyen, Huy; Liew, Chun Wai – International Educational Data Mining Society, 2018
Recent works on Intelligent Tutoring Systems have focused on more complicated knowledge domains, which pose challenges in automated assessment of student performance. In particular, while the system can log every user action and keep track of the student's solution state, it is unable to determine the hidden intermediate steps leading to such…
Descriptors: Bayesian Statistics, Intelligent Tutoring Systems, Data Analysis, Error Patterns
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Hubbard, Aleata – Grantee Submission, 2017
The results of educational research studies are only as accurate as the data used to produce them. Drawing on experiences conducting large-scale efficacy studies of classroom-based algebra interventions for community college and middle school students, I am developing practice-based data cleaning procedures to support scholars in conducting…
Descriptors: Educational Research, Mathematics Education, Algebra, Intervention
Klingler, Severin; Käser, Tanja; Solenthaler, Barbara; Gross, Markus – International Educational Data Mining Society, 2015
Modeling student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for modeling the acquisition of knowledge is Bayesian Knowledge Tracing (BKT). Various extensions to the original BKT model have been proposed, among them two novel models that unify BKT and Item Response Theory (IRT). Latent Factor Knowledge…
Descriptors: Intelligent Tutoring Systems, Knowledge Level, Item Response Theory, Prediction
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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
Ganesan, Raman; Dindyal, Jaguthsing – Mathematics Education Research Group of Australasia, 2014
In this study we set out to investigate the errors made by students in logarithms. A test with 16 items was administered to 89 Secondary three students (Year 9). The errors made by the students were categorized using four categories from a framework by Movshovitz-Hadar, Zaslavsky, and Inbar (1987). It was found that students in the top third were…
Descriptors: Foreign Countries, Secondary School Mathematics, Secondary School Students, Grade 8
Barker-Plummer, Dave; Cox, Richard; Dale, Robert – International Working Group on Educational Data Mining, 2009
In this paper, we present a study of a large corpus of student logic exercises in which we explore the relationship between two distinct measures of difficulty: the proportion of students whose initial attempt at a given natural language to first-order logic translation is incorrect, and the average number of attempts that are required in order to…
Descriptors: Data Analysis, Logical Thinking, Difficulty Level, Assignments
Love, Gloria C. – 1988
The probability of experiment-wise error is explored. Overall, the experiment-wise error rate is directly related to the test-wise error rate--the alpha level set by researchers to curtail the existence of a Type I error. A Type I error occurs when a true null hypothesis is rejected in a given study or experiment. The experiment-wise error rate is…
Descriptors: Data Analysis, Error Patterns, Estimation (Mathematics), Experiments
Davis, Benjamin G.; Irwin, Paul M. – 1974
This paper documents a successful methodology for the validation of data in general and for evaluating educational finance data in particular. The report addresses the errors found as a result of the independent completion of Part B-1 of the Elementary-Secondary General Information System (ELSEGIS)--the Local Education Agency Fiscal Report. This…
Descriptors: Data, Data Analysis, Data Collection, Educational Finance
Fredrich, Barbara E.; Griffin, Ernst C. – 1976
College students from colleges and universities throughout the United States were surveyed about the location of the world's 50 most populous countries. Over 3,300 responses were examined to determine the perceived geographic range of given countries, the regional degree of accuracy among students, and the overall significance of response errors.…
Descriptors: Area Studies, College Students, Data Analysis, Error Patterns