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Tsabari, Stav; Segal, Avi; Gal, Kobi – International Educational Data Mining Society, 2023
Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting "bug-fix-time", the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide…
Descriptors: College Students, Computer Science Education, Programming, Error Patterns
Adu-Gyamfi, Kwaku; Stiff, Lee V.; Bosse, Michael J. – School Science and Mathematics, 2012
Translation errors and conceptual misunderstandings made by students translating among graphical, tabular, and symbolic representations of linear functions were examined. The study situated student errors in the context of the "Translation-Verification Model" developed specifically for the purpose of explaining student behavior during the process…
Descriptors: Student Behavior, Translation, Error Patterns, Models
Tu, Yuancheng – ProQuest LLC, 2012
The fundamental problem faced by automatic text understanding in Natural Language Processing (NLP) is to identify semantically related pieces of text and integrate them together to compute the meaning of the whole text. However, the principle of compositionality runs into trouble very quickly when real language is examined with its frequent…
Descriptors: English, Verbs, Computational Linguistics, Natural Language Processing
Le, Nguyen-Thinh; Menzel, Wolfgang – International Journal of Artificial Intelligence in Education, 2009
In this paper, we introduce logic programming as a domain that exhibits some characteristics of being ill-defined. In order to diagnose student errors in such a domain, we need a means to hypothesise the student's intention, that is the strategy underlying her solution. This is achieved by weighting constraints, so that hypotheses about solution…
Descriptors: Intelligent Tutoring Systems, Logical Thinking, Programming, Models
Howe, Harry; Simkin, Mark G. – Decision Sciences Journal of Innovative Education, 2006
The extensive computational and formatting capabilities of today's spreadsheets enable users to create sophisticated analytical models with professionally formatted outputs. But good-looking reports can mask a host of input errors, formula mistakes, and computational problems. This article examines the subject of spreadsheet error detection in…
Descriptors: Spreadsheets, Error Patterns, Models, Computation
Brown, Scott D.; Marley, A. A. J.; Donkin, Christopher; Heathcote, Andrew – Psychological Review, 2008
Recent theoretical developments in the field of absolute identification have stressed differences between relative and absolute processes, that is, whether stimulus magnitudes are judged relative to a shorter term context provided by recently presented stimuli or a longer term context provided by the entire set of stimuli. The authors developed a…
Descriptors: Stimuli, Reaction Time, Models, Attention

Weiss, Alexander – Evaluation Review, 1994
An analytical technique is reviewed that allows a researcher to decompose informant reports into their respective trait, informant bias, and measure specificity using LISREL. The technique is illustrated in a study of decision making in 151 police agencies. (SLD)
Descriptors: Bias, Decision Making, Error Patterns, Identification

Ohlsson, Stellan – Psychological Review, 1996
A theory of how people detect and correct their own performance errors during skill practice is proposed. Blame assignment, error attribution, and knowledge revision are identified as three cognitive functions in explaining error correction. The theory is embodied in a computer model that learns cognitive skills in ecologically valid domains. (SLD)
Descriptors: Computer Software, Error Correction, Error Patterns, Feedback