Educational data mining is capable of producing useful data-driven applications (e.g., early warning systems in schools or the prediction of students’ academic achievement) based on predictive models. However, the class imbalance problem in educational datasets could hamper the accuracy of predictive models as many of these models are designed on the assumption that the predicted class is balanced. Although previous studies proposed several methods to deal with the imbalanced class problem, most of them focused on the technical details of how to improve each technique, while only a few focused on the application aspect, especially for the application of data with different imbalance ratios. In this study, we compared several sampling techniques to handle the different ratios of the class imbalance problem (i.e., moderately or extremely imbalanced classifications) using the High School Longitudinal Study of 2009 dataset. For our comparison, we used random oversampling (ROS), random undersampling (RUS), and the combination of the synthetic minority oversampling technique for nominal and continuous (SMOTE-NC) and RUS as a hybrid resampling technique. We used the Random Forest as our classification algorithm to evaluate the results of each sampling technique. Our results show that random oversampling for moderately imbalanced data and hybrid resampling for extremely imbalanced data seem to work best. The implications for educational data mining applications and suggestions for future research are discussed.

Multiple-choice testing is considered one of the most effective and enduring forms of educational assessment that remains in practice today. This study presents a comprehensive review of the literature on multiple-choice testing in education focused, specifically, on the development, analysis, and use of the incorrect options, which are also called the distractors. Despite a vast body of literature on multiple-choice testing, the task of creating distractors has received much less attention. In this study, we provide an overview of what is known about developing distractors for multiple-choice items and evaluating their quality. Next, we synthesize the existing guidelines on how to use distractors and summarize earlier research on the optimal number of distractors and the optimal ordering of distractors. Finally, we use this comprehensive review to provide the most up-to-date recommendations regarding distractor development, analysis, and use, and in the process, we highlight important areas where further research is needed.

The integration of artificial intelligence (AI) in educational measurement has transformed assessment methods, allowing for automated scoring, swift content analysis, and personalized feedback through machine learning and natural language processing. These advancements provide valuable insights into student performance while also enhancing the overall assessment experience. However, the implementation of AI in education also raises significant ethical concerns regarding validity, reliability, transparency, fairness, and equity. Issues such as algorithmic bias and the opacity of AI decision-making processes risk perpetuating inequalities and affecting assessment outcomes. In response, various stakeholders, including educators, policymakers, and testing organizations, have developed guidelines to ensure the ethical use of AI in education. The National Council of Measurement in Education’s Special Interest Group on AI in Measurement and Education (AIME) is dedicated to establishing ethical standards and advancing research in this area. In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement, explores significant challenges such as automation bias and environmental impact, and proposes solutions to ensure AI’s responsible and effective use in education.

Large amounts of missing data could distort item parameter estimation and lead to biased ability estimates in educational assessments. Therefore, missing responses should be handled properly before estimating any parameters. In this study, two Monte Carlo simulation studies were conducted to compare the performance of four methods in handling missing data when estimating ability parameters. The methods were full-information maximum likelihood (FIML), zero replacement, and multiple imputation with chain equations utilizing classification and regression trees (MICE-CART) and random forest imputation (MICE-RFI). For the two imputation methods, missing responses were considered as a valid response category to enhance the accuracy of imputations. Bias, root mean square error, and the correlation between true ability parameters and estimated ability parameters were used to evaluate the accuracy of ability estimates for each method. Results indicated that FIML outperformed the other methods under most conditions. Zero replacement yielded accurate ability estimates when missing proportions were very high. The performances of MICE-CART and MICE-RFI were quite similar but these two methods appeared to be affected differently by the missing data mechanism. As the number of items increased and missing proportions decreased, all the methods performed better. In addition, the information on missing data could improve the performance of MICE-RFI and MICE-CART when the data set is sparse and the missing data mechanism is missing at random.

Play is an integrative process, and the skills acquired in it—overcoming impulses, behavior control, exploration and discovery, problem-solving, reasoning, drawing conclusions, and attention to processes and outcomes are foundational cognitive structures that drive learning and motivation. Loose parts play is a prominent form of play that many scholars and educators explicitly endorse for cognitive development (e.g., divergent thinking, problem-solving). It is unique among play types because children can combine different play types and natural or manufactured materials in one occurrence. While educators and policymakers promote the benefits of loose parts play, no previous research has explored the direct relationship between preschool-age children’s indoor loose parts play experiences and cognitive development. We address this gap by bringing together the relevant literature and synthesizing the empirical studies on common play types with loose parts, namely object and exploratory, symbolic and pretend, and constructive play. We also focus on studies that examine children’s experiences through loose parts, highlighting the impact of different play types on learning through the reinforcement of cognitive skills, such as executive function, cognitive self-regulation, reasoning, and problem-solving. By examining the existing literature and synthesizing empirical evidence, we aim to deepen our understanding of the relationship between children’s play with loose parts and its impact on cognitive development. Ultimately, pointing out the gaps in the literature that would add to the body of knowledge surrounding the benefits of play for cognitive development and inform educators, policymakers, and researchers about the significance of incorporating loose parts play into early childhood education.

Developing a diagnostic tool within the diagnostic measurement framework is the optimal approach to obtain multidimensional and classification-based feedback on examinees. However, end users may seek to obtain diagnostic feedback from existing item responses to assessments that have been designed under either the classical test theory or item response theory frameworks. Retrofitting diagnostic classification models to existing assessments designed under other psychometric frameworks could be a plausible approach to obtain more actionable scores or understand more about the constructs themselves. This study (a) discusses the possibility and problems of retrofitting, (b) proposes a step-by-step retrofitting framework, and (c) explores the information one can gain from retrofitting through an empirical application example. While retrofitting may not always be an ideal approach to diagnostic measurement, this article aims to invite discussions through presenting the possibility, challenges, process, and product of retrofitting.