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Fuchimoto, Kazuma; Ishii, Takatoshi; Ueno, Maomi – IEEE Transactions on Learning Technologies, 2022
Educational assessments often require uniform test forms, for which each test form has equivalent measurement accuracy but with a different set of items. For uniform test assembly, an important issue is the increase of the number of assembled uniform tests. Although many automatic uniform test assembly methods exist, the maximum clique algorithm…
Descriptors: Simulation, Efficiency, Test Items, Educational Assessment
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Yildirim, Osman Gazi; Ozdener, Nesrin – International Journal of Computer Science Education in Schools, 2022
The main goal of the current study is to develop a reliable instrument to measure programming anxiety in university students. A pool of 33 items based on extensive literature review and experts' opinions were created by researchers. The draft scale comprised three factors applied to 392 university students from two different universities in Turkey…
Descriptors: Anxiety, Undergraduate Students, Student Attitudes, Factor Analysis
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Pelánek, Radek; Effenberger, Tomáš; Kukucka, Adam – Journal of Educational Data Mining, 2022
We study the automatic identification of educational items worthy of content authors' attention. Based on the results of such analysis, content authors can revise and improve the content of learning environments. We provide an overview of item properties relevant to this task, including difficulty and complexity measures, item discrimination, and…
Descriptors: Item Analysis, Identification, Difficulty Level, Case Studies
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Çakiroglu, Ünal; Er, Betul – Informatics in Education, 2020
Considerable effort has been invested in innovative practices about teaching programming. Although the usefulness of metacognition in learning process is acknowledged, evidence demonstrating how metacognitive strategies effect in the programming classrooms is still very scarce. Given the importance of metacognitive strategies, this study seeks to…
Descriptors: Metacognition, Learning Strategies, Programming, Computer Science Education
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Ishii, Takatoshi; Songmuang, Pokpong; Ueno, Maomi – IEEE Transactions on Learning Technologies, 2014
Educational assessments occasionally require uniform test forms for which each test form comprises a different set of items, but the forms meet equivalent test specifications (i.e., qualities indicated by test information functions based on item response theory). We propose two maximum clique algorithms (MCA) for uniform test form assembly. The…
Descriptors: Simulation, Efficiency, Test Items, Educational Assessment
Kunkle, Wanda M. – ProQuest LLC, 2010
Many students experience difficulties learning to program. They find learning to program in the object-oriented paradigm particularly challenging. As a result, computing educators have tried a variety of instructional methods to assist beginning programmers. These include developing approaches geared specifically toward novices and experimenting…
Descriptors: Computer Science Education, Programming Languages, Language of Instruction, Academic Achievement
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Wauters, K.; Desmet, P.; Van den Noortgate, W. – Journal of Computer Assisted Learning, 2010
The popularity of intelligent tutoring systems (ITSs) is increasing rapidly. In order to make learning environments more efficient, researchers have been exploring the possibility of an automatic adaptation of the learning environment to the learner or the context. One of the possible adaptation techniques is adaptive item sequencing by matching…
Descriptors: Knowledge Level, Adaptive Testing, Test Items, Item Response Theory
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Kuechler, William L.; Simkin, Mark G. – Decision Sciences Journal of Innovative Education, 2010
Both professional certification and academic tests rely heavily on multiple-choice questions, despite the widespread belief that alternate, constructed-response questions are superior measures of a test taker's understanding of the underlying material. Empirically, the search for a link between these two assessment metrics has met with limited…
Descriptors: Multiple Choice Tests, Performance Based Assessment, Alternative Assessment, Knowledge Level
Hart, Robert S. – 1994
This guide to ERRATA, a set of HyperCard-based tools for response analysis and error diagnosis in language testing, is intended as a user manual and general reference and designed to be used with the software (not included here). It has three parts. The first is a brief survey of computational techniques available for dealing with student test…
Descriptors: Authoring Aids (Programming), Comparative Analysis, Computer Software, Data Processing
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Shuqun, Yang; Shuliang, Ding; Zhiqiang, Yao – International Journal of Distance Education Technologies, 2009
Cognitive diagnosis (CD) plays an important role in intelligent tutoring system. Computerized adaptive testing (CAT) is adaptive, fair, and efficient, which is suitable to large-scale examination. Traditional cognitive diagnostic test needs quite large number of items, the efficient and tailored CAT could be a remedy for it, so the CAT with…
Descriptors: Monte Carlo Methods, Distance Education, Adaptive Testing, Intelligent Tutoring Systems
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Rupp, Andre A. – International Journal of Testing, 2003
Item response theory (IRT) has become one of the most popular scoring frameworks for measurement data. IRT models are used frequently in computerized adaptive testing, cognitively diagnostic assessment, and test equating. This article reviews two of the most popular software packages for IRT model estimation, BILOG-MG (Zimowski, Muraki, Mislevy, &…
Descriptors: Test Items, Adaptive Testing, Item Response Theory, Computer Software
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Liu, Chao-Lin – Educational Technology & Society, 2005
The author analyzes properties of mutual information between dichotomous concepts and test items. The properties generalize some common intuitions about item comparison, and provide principled foundations for designing item-selection heuristics for student assessment in computer-assisted educational systems. The proposed item-selection strategies…
Descriptors: Test Items, Heuristics, Classification, Item Analysis