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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
Yigiter, Mahmut Sami; Dogan, Nuri – Measurement: Interdisciplinary Research and Perspectives, 2023
In recent years, Computerized Multistage Testing (MST), with their versatile benefits, have found themselves a wide application in large scale assessments and have increased their popularity. The fact that forms can be made ready before the exam application, such as a linear test, and that they can be adapted according to the test taker's ability…
Descriptors: Programming Languages, Monte Carlo Methods, Computer Assisted Testing, Test Format
Kilic, Abdullah Faruk; Uysal, Ibrahim – International Journal of Assessment Tools in Education, 2022
Most researchers investigate the corrected item-total correlation of items when analyzing item discrimination in multi-dimensional structures under the Classical Test Theory, which might lead to underestimating item discrimination, thereby removing items from the test. Researchers might investigate the corrected item-total correlation with the…
Descriptors: Item Analysis, Correlation, Item Response Theory, Test Items
Erdem-Kara, Basak – International Journal of Assessment Tools in Education, 2019
Computer adaptive testing is an important research field in educational measurement, and simulation studies play a critically important role in CAT development and evaluation. Both Monte Carlo and Post Hoc simulations are frequently used in CAT studies in order to investigate the effects of different factors on test efficiency and to compare…
Descriptors: Computer Assisted Testing, Adaptive Testing, Programming Languages, Monte Carlo Methods
Seebut, Supot; Wongsason, Patcharee; Kim, Dojin; Putjuso, Thanin; Boonpok, Chawalit – EURASIA Journal of Mathematics, Science and Technology Education, 2022
Simulation modeling is an effective tool for solving problems that cannot be explained analytically or when data cannot be collected. This is done by simulating the observed behavior of a problem under study using a computer program. In math education, this can develop knowledge and fundamental competencies of simulation modeling at a higher level…
Descriptors: Programming Languages, Mathematics Instruction, Grade 12, Secondary School Students
Lockwood, J. R.; Castellano, Katherine E.; McCaffrey, Daniel F. – Journal of Educational and Behavioral Statistics, 2022
Many states and school districts in the United States use standardized test scores to compute annual measures of student achievement progress and then use school-level averages of these growth measures for various reporting and diagnostic purposes. These aggregate growth measures can vary consequentially from year to year for the same school,…
Descriptors: Accuracy, Prediction, Programming Languages, Standardized Tests
Silva, R. M.; Guan, Y.; Swartz, T. B. – Journal on Efficiency and Responsibility in Education and Science, 2017
This paper attempts to bridge the gap between classical test theory and item response theory. It is demonstrated that the familiar and popular statistics used in classical test theory can be translated into a Bayesian framework where all of the advantages of the Bayesian paradigm can be realized. In particular, prior opinion can be introduced and…
Descriptors: Item Response Theory, Bayesian Statistics, Test Construction, Markov Processes
Sanou Gozalo, Eduard; Hernández-Fernández, Antoni; Arias, Marta; Ferrer-i-Cancho, Ramon – Journal of Technology and Science Education, 2017
In a course of the degree of computer science, the programming project has changed from individual to teamed work, tentatively in couples (pair programming). Students have full freedom to team up with minimum intervention from teachers. The analysis of the working groups made indicates that students do not tend to associate with students with a…
Descriptors: Group Activities, Group Dynamics, Computer Science, Programming
Andrew Gelman; Daniel Lee; Jiqiang Guo – Journal of Educational and Behavioral Statistics, 2015
Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users' and developers'…
Descriptors: Programming Languages, Bayesian Statistics, Inferences, Monte Carlo Methods
Benakli, Nadia; Kostadinov, Boyan; Satyanarayana, Ashwin; Singh, Satyanand – International Journal of Mathematical Education in Science and Technology, 2017
The goal of this paper is to promote computational thinking among mathematics, engineering, science and technology students, through hands-on computer experiments. These activities have the potential to empower students to learn, create and invent with technology, and they engage computational thinking through simulations, visualizations and data…
Descriptors: Calculus, Probability, Data Analysis, Computation
Monaghan, Conal; Bizumic, Boris; Reynolds, Katherine; Smithson, Michael; Johns-Boast, Lynette; van Rooy, Dirk – European Journal of Engineering Education, 2015
One prominent approach in the exploration of the variations in project team performance has been to study two components of the aggregate personalities of the team members: conscientiousness and agreeableness. A second line of research, known as self-categorisation theory, argues that identifying as team members and the team's performance norms…
Descriptors: Performance Based Assessment, Student Projects, Computer Software, Programming