This study reviews existing research on the use of large language models (LLMs) for automatic item generation (AIG). We performed a comprehensive literature search across seven research databases, selected studies based on predefined criteria, and summarized 60 relevant studies that employed LLMs in the AIG process. We identified the most commonly used LLMs in current AIG literature, their specific applications in the AIG process, and the characteristics of the generated items. We found that LLMs are flexible and effective in generating various types of items across different languages and subject domains. However, many studies have overlooked the quality of the generated items, indicating a lack of a solid educational foundation. Therefore, we share two suggestions to enhance the educational foundation for leveraging LLMs in AIG, advocating for interdisciplinary collaborations to exploit the utility and potential of LLMs.

Learning analytics (LA) has the potential to significantly improve teaching and learning, but there are still many areas for improvement in LA research and practice. The literature highlights limitations in every stage of the LA life cycle, including scarce pedagogical grounding and poor design choices in the development of LA, challenges in the implementation of LA with respect to the interpretability of insights, prediction, and actionability of feedback, and lack of generalizability and strong practices in LA evaluation. In this position paper, we advocate for empowering teachers in developing LA solutions. We argue that this would enhance the theoretical basis of LA tools and make them more understandable and practical. We present some instances where process data can be utilized to comprehend learning processes and generate more interpretable LA insights. Additionally, we investigate the potential implementation of large language models (LLMs) in LA to produce comprehensible insights, provide timely and actionable feedback, enhance personalization, and support teachers’ tasks more extensively.

Originally developed as an effective feature selection method in healthcare predictive analytics, Recursive Feature Elimination (RFE) has gained increasing popularity in Educational Data Mining (EDM) due to its ability to handle high-dimensional data and support interpretable modeling. Over time, various RFE variants have emerged, each introducing methodological enhancements. To help researchers better understand and apply RFE more effectively, this study organizes existing variants into four methodological categories: (1) integration with different machine learning models, (2) combinations of multiple feature importance metrics, (3) modifications to the original RFE process, and (4) hybridization with other feature selection or dimensionality reduction techniques. Rather than conducting a systematic review, we present a narrative synthesis supported by illustrative studies from EDM to demonstrate how different variants have been applied in practice. We also conduct an empirical evaluation of five representative RFE variants across two domains: a regression task using a large-scale educational dataset and a classification task using a clinical dataset on chronic heart failure. Our evaluation benchmarks predictive accuracy, feature selection stability, and runtime efficiency. Results show that the evaluation metrics vary significantly across RFE variants. For example, while RFE wrapped with tree-based models such as Random Forest and Extreme Gradient Boosting (XGBoost) yields strong predictive performance, these methods tend to retain large feature sets and incur high computational costs. In contrast, a variant known as Enhanced RFE achieves substantial feature reduction with only marginal accuracy loss, offering a favorable balance between efficiency and performance. These findings underscore the trade-offs among accuracy, interpretability, and computational cost across RFE variants, providing practical guidance for selecting the most appropriate algorithm based on domain-specific needs and constraints.

Machine learning (ML) has become integral in educational decision-making through technologies such as learning analytics and educational data mining. However, the adoption of machine learning-driven tools without scrutiny risks perpetuating biases. Despite ongoing efforts to tackle fairness issues, their application to educational datasets remains limited. To address the mentioned gap in the literature, this research evaluates the effectiveness of four bias mitigation techniques in an educational dataset aiming at predicting students’ dropout rate. The overarching research question is: “How effective are the techniques of reweighting, resampling, and Reject Option-based Classification (ROC) pivoting in mitigating the predictive bias associated with high school dropout rates in the HSLS:09 dataset?" The effectiveness of these techniques was assessed based on performance metrics including false positive rate (FPR), accuracy, and F1 score. The study focused on the biological sex of students as the protected attribute. The reweighting technique was found to be ineffective, showing results identical to the baseline condition. Both uniform and preferential resampling techniques significantly reduced predictive bias, especially in the FPR metric but at the cost of reduced accuracy and F1 scores. The ROC pivot technique marginally reduced predictive bias while maintaining the original performance of the classifier, emerging as the optimal method for the HSLS:09 dataset. This research extends the understanding of bias mitigation in educational contexts, demonstrating practical applications of various techniques and providing insights for educators and policymakers. By focusing on an educational dataset, it contributes novel insights beyond the commonly studied datasets, highlighting the importance of context-specific approaches in bias mitigation.

Over a decade ago, automatic item generation (AIG) was introduced to meet the increasing need for high-quality items in educational measurement. Around the same time, a new area of research in computer science began to develop questions for educational use. Historically, researchers from these two domains had little knowledge or communication with one another. However, the development of pre-trained large language models (LLMs) has sparked the interest of researchers from both domains in applying these models for automatically creating items. With similar objectives and methodologies, these two research domains appear to be converging on how to address the problems in this field. The purpose of this study is to provide a review of the current state of research by synthesizing existing studies on the use of LLMs for AIG. By combining research from both domains, we examine the utility and potential of LLMs for AIG. We performed a comprehensive literature review in seven research databases, selected studies based on predefined criteria, and summarized 60 relevant studies that employed LLMs in the AIG process. We identified the most commonly used LLMs in current AIG literature, their specific applications in the AIG process, and the characteristics of the generated items. We found that LLMs are flexible and effective in generating various types of items based on different languages and subject domains. However, many studies have overlooked the quality of the generated items, indicating a lack of a solid educational foundation. This review emphasizes the urgent need for greater integration of learning and measurement theories in future AIG research. We share two suggestions to enhance the educational foundation for leveraging LLMs in AIG, advocating for interdisciplinary collaborations to exploit the utility and potential of LLMs.

This study systematically explores Recursive Feature Elimination (RFE) and its variants as effective methods for feature selection within educational data mining (EDM) and healthcare analytics. Feature selection is crucial in predictive modeling due to challenges arising from high dimensionality and data complexity. The paper first reviews foundational principles of the RFE algorithm, highlighting its iterative mechanism for ranking and removing less informative features. Next, it provides a comprehensive narrative review of various RFE modifications, including integration with different machine learning models, utilization of multiple feature importance metrics, algorithmic enhancements through cross-validation and local search techniques, and hybrid approaches combining RFE with other dimensionality reduction methods. Subsequently, an empirical evaluation compares four RFE variants—standard RFE, RF-RFE, Enhanced RFE, and RFE with local search—across two distinct predictive tasks: regression for predicting students’ mathematics achievement using a digital assessment dataset, and classification for predicting chronic heart failure in a clinical dataset. Results indicate that while RF-RFE captured complex feature interactions and improved predictive performance slightly, Enhanced RFE provided substantial dimensionality reduction with minimal loss in accuracy, offering a balanced approach beneficial for practical applications. The study concludes by discussing implications, limitations, and recommendations for effectively applying RFE in educational and clinical contexts.