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Showing all 11 results Save | Export
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Michael C. Robbins; Zhuping Li – Field Methods, 2025
The Nolan Index (NI) is a normed, quantitative measure for comparing the degree of resemblance (similarity or dissimilarity) between free listings with an Excel program for calculating it. This article enhances that effort with the addition of an R program and additional applications. Free-list resemblance measures have been used to investigate…
Descriptors: Computation, Norm Referenced Tests, Comparative Analysis, Spreadsheets
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Demir, Seda; Doguyurt, Mehmet Fatih – African Educational Research Journal, 2022
The purpose of this research was to compare the performances of the Fixed Effect Model (FEM) and the Random Effects Model (REM) in the meta-analysis studies conducted through 5, 10, 20 and 40 studies with an outlier and 4, 9, 19 and 39 studies without an outlier in terms of estimated common effect size, confidence interval coverage rate and…
Descriptors: Meta Analysis, Comparative Analysis, Research Reports, Effect Size
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Mulder, J.; Raftery, A. E. – Sociological Methods & Research, 2022
The Schwarz or Bayesian information criterion (BIC) is one of the most widely used tools for model comparison in social science research. The BIC, however, is not suitable for evaluating models with order constraints on the parameters of interest. This article explores two extensions of the BIC for evaluating order-constrained models, one where a…
Descriptors: Models, Social Science Research, Programming Languages, Bayesian Statistics
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Shi, Yang; Chi, Min; Barnes, Tiffany; Price, Thomas W. – International Educational Data Mining Society, 2022
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been made in KT, most models including the state-of-the-art Deep KT (DKT) mainly leverage each student's response either as correct or incorrect, ignoring its content. In…
Descriptors: Programming, Knowledge Level, Prediction, Instructional Innovation
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Son, Ji Y.; Blake, Adam B.; Fries, Laura; Stigler, James W. – Journal of Statistics and Data Science Education, 2021
Students learn many concepts in the introductory statistics course, but even our most successful students end up with rigid, ritualized knowledge that does not transfer easily to new situations. In this article we describe our attempt to apply theories and findings from learning science to the design of a statistics course that aims to help…
Descriptors: Statistics Education, Introductory Courses, Teaching Methods, Data Analysis
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Orr, J. Walker; Russell, Nathaniel – International Educational Data Mining Society, 2021
The assessment of program functionality can generally be accomplished with straight-forward unit tests. However, assessing the design quality of a program is a much more difficult and nuanced problem. Design quality is an important consideration since it affects the readability and maintainability of programs. Assessing design quality and giving…
Descriptors: Programming Languages, Feedback (Response), Units of Study, Computer Science Education
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Finch, Holmes – Practical Assessment, Research & Evaluation, 2022
Researchers in many disciplines work with ranking data. This data type is unique in that it is often deterministic in nature (the ranks of items "k"-1 determine the rank of item "k"), and the difference in a pair of rank scores separated by "k" units is equivalent regardless of the actual values of the two ranks in…
Descriptors: Data Analysis, Statistical Inference, Models, College Faculty
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Flynt, Abby; Dean, Nema – Journal of Educational and Behavioral Statistics, 2016
Cluster analysis is a set of statistical methods for discovering new group/class structure when exploring data sets. This article reviews the following popular libraries/commands in the R software language for applying different types of cluster analysis: from the stats library, the kmeans, and hclust functions; the mclust library; the poLCA…
Descriptors: Multivariate Analysis, Computer Software, Comparative Analysis, Programming Languages
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Sanchez, Pablo; Zorrilla, Marta; Duque, Rafael; Nieto-Reyes, Alicia – Computer Science Education, 2011
Models in Software Engineering are considered as abstract representations of software systems. Models highlight relevant details for a certain purpose, whereas irrelevant ones are hidden. Models are supposed to make system comprehension easier by reducing complexity. Therefore, models should play a key role in education, since they would ease the…
Descriptors: Computer Science Education, Computer Software, Programming, Programming Languages
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Alonso, Fernando; Manrique, Daniel; Vines, Jose M. – Computers & Education, 2009
This paper presents a novel instructional model for e-learning and an evaluation study to determine the effectiveness of this model for teaching Java language programming to information technology specialists working for the Spanish Public Administration. This is a general-purpose model that combines objectivist and constructivist learning…
Descriptors: Constructivism (Learning), Distance Education, Information Technology, Foreign Countries
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Naumenko, Anton; Nikitin, Sergiy; Terziyan, Vagan; Zharko, Andriy – Learning Organization, 2005
Purpose: To identify cases related to design of ICT platforms for industrial alliances, where the use of Ontology-driven architectures based on Semantic web standards is more advantageous than application of conventional modeling together with XML standards. Design/methodology/approach: A comparative analysis of the two latest and the most obvious…
Descriptors: Technical Support, Industry, Models, Cooperation