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Davi Bernardo Silva; Deborah Ribeiro Carvalho; Carlos N. Silla – IEEE Transactions on Learning Technologies, 2024
Throughout a programming course, students develop various source code tasks. Using these tasks to track students' progress can provide clues to the strengths and weaknesses found in each learning topic. This practice allows the teacher to intervene in learning in the first few weeks of class and maximize student gains. However, the biggest…
Descriptors: Computation, Models, Ability Grouping, Programming
Christina Glasauer; Martin K. Yeh; Lois Anne DeLong; Yu Yan; Yanyan Zhuang – Computer Science Education, 2025
Background and Context: Feedback on one's progress is essential to new programming language learners, particularly in out-of-classroom settings. Though many study materials offer assessment mechanisms, most do not examine the accuracy of the feedback they deliver, nor give evidence on its validity. Objective: We investigate the potential use of a…
Descriptors: Novices, Computer Science Education, Programming, Accuracy
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
Gus Greivel; Alexandra Newman; Maxwell Brown; Kelly Eurek – INFORMS Transactions on Education, 2024
Industrial-scale models require considerable setup time; hence, once built, they are used in myriad ways to consider closely related cases. In practice, the code for these models frequently evolves without appropriate notational choices, largely as a result of the lengthy development time of, and the number of individuals contributing to, their…
Descriptors: Models, Best Practices, Mathematical Concepts, Energy
Yang Shi; Tiffany Barnes; Min Chi; Thomas Price – International Educational Data Mining Society, 2024
Knowledge tracing (KT) models have been a commonly used tool for tracking students' knowledge status. Recent advances in deep knowledge tracing (DKT) have demonstrated increased performance for knowledge tracing tasks in many datasets. However, interpreting students' states on single knowledge components (KCs) from DKT models could be challenging…
Descriptors: Algorithms, Artificial Intelligence, Models, Programming
Feng Hsu Wang – IEEE Transactions on Learning Technologies, 2024
Due to the development of deep learning technology, its application in education has received increasing attention from researchers. Intelligent agents based on deep learning technology can perform higher order intellectual tasks than ever. However, the high deployment cost of deep learning models has hindered their widespread application in…
Descriptors: Learning Processes, Models, Man Machine Systems, Cooperative Learning
Austin M. Shin; Ayaan M. Kazerouni – ACM Transactions on Computing Education, 2024
Background and Context: Students' programming projects are often assessed on the basis of their tests as well as their implementations, most commonly using test adequacy criteria like branch coverage, or, in some cases, mutation analysis. As a result, students are implicitly encouraged to use these tools during their development process (i.e., so…
Descriptors: Feedback (Response), Programming, Student Projects, Computer Software
Mark W. Isken – INFORMS Transactions on Education, 2025
A staple of many spreadsheet-based management science courses is the use of Excel for activities such as model building, sensitivity analysis, goal seeking, and Monte-Carlo simulation. What might those things look like if carried out using Python? We describe a teaching module in which Python is used to do typical Excel-based modeling and…
Descriptors: Spreadsheets, Models, Programming Languages, Monte Carlo Methods
Maciej Pankiewicz; Yang Shi; Ryan S. Baker – International Educational Data Mining Society, 2025
Knowledge Tracing (KT) models predicting student performance in intelligent tutoring systems have been successfully deployed in several educational domains. However, their usage in open-ended programming problems poses multiple challenges due to the complexity of the programming code and a complex interplay between syntax and logic requirements…
Descriptors: Algorithms, Artificial Intelligence, Models, Intelligent Tutoring Systems
Pere J. Ferrando; Ana Hernández-Dorado; Urbano Lorenzo-Seva – Structural Equation Modeling: A Multidisciplinary Journal, 2024
A frequent criticism of exploratory factor analysis (EFA) is that it does not allow correlated residuals to be modelled, while they can be routinely specified in the confirmatory (CFA) model. In this article, we propose an EFA approach in which both the common factor solution and the residual matrix are unrestricted (i.e., the correlated residuals…
Descriptors: Correlation, Factor Analysis, Models, Goodness of Fit
Ethan C. Brown; Mohammed A. A. Abulela – Practical Assessment, Research & Evaluation, 2025
Moderated multiple regression (MMR) has become a fundamental tool for applied researchers, since many effects are expected to vary based on other variables. However, the inherent complexity of MMR creates formidable challenges for adequately performing power analysis on interaction effects to ensure reliable and replicable research results. Prior…
Descriptors: Statistical Analysis, Multiple Regression Analysis, Models, Programming Languages
Sukan Saeliang; Pinanta Chatwattana – International Education Studies, 2025
The project-based learning model via generative artificial intelligence, or PjBL model via Gen-AI, is a research tool that was initiated based on the concept of project-based learning management focusing mainly on self-directed learning, in which learners are able to learn and practice through the projects they are interested in as to their…
Descriptors: Active Learning, Student Projects, Artificial Intelligence, Man Machine Systems
Ronit Shmallo; Adi Katz – Computer Science Education, 2024
Background and Context: Gender research shows that women are better at reading comprehension. Other studies indicate a lower tendency in women to choose STEM professions. Since data modeling requires reading skills and also belongs in the areas of information systems and computer science (STEM professions), these findings provoked our curiosity.…
Descriptors: Gender Differences, Transfer of Training, Databases, Models
Diana Kirk; Andrew Luxton-Reilly; Ewan Tempero – ACM Transactions on Computing Education, 2025
Objectives: Code style is an important aspect of text-based programming because programs written with good style are considered easier to understand and change and so improve the maintainability of the delivered software product. However teaching code style is complicated by the existence of many style guides and standards that contain…
Descriptors: Computer Science Education, Programming, Computer Software, Teaching Methods
Erik Forsberg; Anders Sjöberg – Measurement: Interdisciplinary Research and Perspectives, 2025
This paper reports a validation study based on descriptive multidimensional item response theory (DMIRT), implemented in the R package "D3mirt" by using the ERS-C, an extended version of the Relevance subscale from the Moral Foundations Questionnaire including two new items for collectivism (17 items in total). Two latent models are…
Descriptors: Evaluation Methods, Programming Languages, Altruism, Collectivism

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