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Bilal Ghanem; Alona Fyshe – International Educational Data Mining Society, 2024
Multiple choice questions (MCQs) are a common way to assess reading comprehension. Every MCQ needs a set of distractor answers that are incorrect, but plausible enough to test student knowledge. However, good distractors are hard to create. Distractor generation (DG) models have been proposed, and their performance is typically evaluated using…
Descriptors: Multiple Choice Tests, Reading Comprehension, Test Items, Testing
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Yi, Zhiyao; Chen, Yi-Hsin; Yin, Yue; Cheng, Ke; Wang, Yan; Nguyen, Diep; Pham, Thanh; Kim, EunSook – Journal of Experimental Education, 2022
A simulation study was conducted to examine the efficacy of nine frequently-used HOV tests, including Levene's tests with squared residuals and with absolute residuals, Brown and Forsythe (BF) test, Bootstrap BF test, O'Brien test, Z-variance test, Box-Scheffé (BS) test, Bartlett test, and Pseudo jackknife test under comprehensive simulation…
Descriptors: Statistical Analysis, Robustness (Statistics), Sampling, Statistical Inference
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Sean McGrath; XiaoFei Zhao; Omer Ozturk; Stephan Katzenschlager; Russell Steele; Andrea Benedetti – Research Synthesis Methods, 2024
When performing an aggregate data meta-analysis of a continuous outcome, researchers often come across primary studies that report the sample median of the outcome. However, standard meta-analytic methods typically cannot be directly applied in this setting. In recent years, there has been substantial development in statistical methods to…
Descriptors: Statistical Analysis, Meta Analysis, Data Analysis, Sampling
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Hayat Sahlaoui; El Arbi Abdellaoui Alaoui; Said Agoujil; Anand Nayyar – Education and Information Technologies, 2024
Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…
Descriptors: Sampling, Classification, Algorithms, Prediction
Claire Miller – ProQuest LLC, 2024
Data are everywhere. Data collected from samples are often reported in the form of polls, medical studies, and advertisement information and an understanding of sampling distributions and statistical inference is important for evaluating data-based claims (Bargagliotti et al., 2020). Despite the importance of understanding statistical inference…
Descriptors: Novices, Thinking Skills, Sampling, Statistical Distributions
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Gonzalez, Oscar – Educational and Psychological Measurement, 2023
When scores are used to make decisions about respondents, it is of interest to estimate classification accuracy (CA), the probability of making a correct decision, and classification consistency (CC), the probability of making the same decision across two parallel administrations of the measure. Model-based estimates of CA and CC computed from the…
Descriptors: Classification, Accuracy, Intervals, Probability
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van Aert, Robbie C. M.; Goos, Cas – Research Synthesis Methods, 2023
The partial correlation coefficient quantifies the relationship between two variables while taking into account the effect of one or multiple control variables. Researchers often want to synthesize partial correlation coefficients in a meta-analysis since these can be readily computed based on the reported results of a linear regression analysis.…
Descriptors: Computation, Sampling, Correlation, Meta Analysis
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Raden Bambang Sumarsono; Ali Imron; Bambang Budi Wiyono; Imron Arifin; Athalla Nauval Bhayangkara; Mufti Nawang Prastiko – Open Education Studies, 2025
This article aims to describe the influence of parental involvement on the success of achieving the Pancasila Student Profile through school effectiveness. This research was conducted in elementary schools throughout East Java. The stages include the following: (1) testing validity and reliability; (2) testing the feasibility of the model which is…
Descriptors: Parent Participation, Student Characteristics, School Effectiveness, Elementary School Students
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Tian Fan; Luotong Hui; Liang Luo; Anique B. H. de Bruin – Educational Psychology Review, 2024
Recent research has suggested that students prefer restudying over retrieval practice when learning difficult materials, despite the latter being a more effective learning strategy. The current study investigated whether an instructional intervention can improve the use of retrieval practice for both easy and difficult materials. In Experiment 1,…
Descriptors: Information Retrieval, Intervention, Difficulty Level, Learning Strategies
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Sebahat Gok; Robert L. Goldstone – Cognitive Research: Principles and Implications, 2024
Interactive computer simulations are commonly used as pedagogical tools to support students' statistical reasoning. This paper examines whether and how these simulations enable their intended effects. We begin by contrasting two theoretical frameworks--"dual processes" and "grounded cognition"--in the context of people's…
Descriptors: Computer Simulation, Thinking Skills, Teaching Methods, Interaction
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Ransom, Keith J.; Perfors, Andrew; Hayes, Brett K.; Connor Desai, Saoirse – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
In describing how people generalize from observed samples of data to novel cases, theories of inductive inference have emphasized the learner's reliance on the contents of the sample. More recently, a growing body of literature suggests that different assumptions about how a data sample was generated can lead the learner to draw qualitatively…
Descriptors: Sampling, Generalization, Inferences, Logical Thinking
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Sperandei, Sandro; Bastos, Leonardo Soares; Ribeiro-Alves, Marcelo; Reis, Arianne; Bastos, Francisco Inácio – International Journal of Social Research Methodology, 2023
The aim of this study is to investigate the impact of different logistic regression estimators applied to RDS studies via simulation and the analysis of empirical data. Four simulated populations were created with different connectivity characteristics. Each simulated individual received two attributes, one of them associated to the infection…
Descriptors: Regression (Statistics), Recruitment, Sampling, Simulation
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Groth, Randall; Rickards, Megan; Roehm, Elizabeth – Statistics Education Research Journal, 2023
In this report, we analyze students' learning of compound probability by describing connections they generated while engaged with tasks involving two independent events. Several of their connections were compatible with the development of expertise, such as recognizing the need to determine sample spaces across a variety of situations and noting…
Descriptors: Statistics Education, Probability, Concept Formation, Sampling
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Cheng, Siwei – Sociological Methods & Research, 2023
One of the most important developments in the current era of social sciences is the growing availability and diversity of data, big and small. Social scientists increasingly combine information from multiple data sets in their research. While conducting statistical analyses with linked data is relatively straightforward, borrowing information…
Descriptors: Social Science Research, Statistical Analysis, Statistical Distributions, Statistical Bias
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Wendy Chan; Jimin Oh; Chen Li; Jiexuan Huang; Yeran Tong – Society for Research on Educational Effectiveness, 2023
Background: The generalizability of a study's results continues to be at the forefront of concerns in evaluation research in education (Tipton & Olsen, 2018). Over the past decade, statisticians have developed methods, mainly based on propensity scores, to improve generalizations in the absence of random sampling (Stuart et al., 2011; Tipton,…
Descriptors: Generalizability Theory, Probability, Scores, Sampling
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