Data extraction is a crucial, yet labor-intensive and error-prone part of evidence synthesis. To date, efforts to harness machine learning for enhancing efficiency of the data extraction process have fallen short of achieving sufficient accuracy and usability. With the release of large language models (LLMs), new possibilities have emerged to…

Social sciences expose many cognitively complex, highly qualified, or fuzzy problems, whose resolution relies primarily on expert judgement rather than automated systems. One of such instances that we study in this work is a reflection analysis in the writings of student teachers. We share a hands-on experience on how these challenges can be…

This article provides a conceptual framework for considering both the theoretical and methodological factors that underpin the successful validation of a competency framework. Drawing on educational assessment literature, this article argues that a valid competency framework relates to an interpretive judgement of the credibility of the claims…

Governments and educational authorities around the world are emphasizing performance evaluation of educational systems. Opinion Mining (OM) has gained acceptance among experts in various regions, including the preparation space. The proposed model involves Two modules: the data preprocessing module and the opinion mining module. The main objective…

Feedback on how students progress through completing subgoals can improve students' learning and motivation in programming. Detecting subgoal completion is a challenging task, and most learning environments do so either with "expert-authored" models or with "data-driven" models. Both models have advantages that are…

Forecasting the enrollments of new students in bachelor's systems became an urgent desire in the majority of higher education institutions. It represents an important stage in the process of making strategic decisions for new course's accreditation and optimization of resources. To gain a deep view of the educational forecasting context, the most…

Institutional staff such as enrollment managers, business officers, and institutional researchers are often asked to predict enrollments. Developing any predictive model can be intimidating, particularly when there is no textbook to follow. This paper provides a practical framework for generating enrollment projection options and for evaluating…

Information is everywhere in a hidden and scattered way. It becomes useful when we apply Data mining to extracts the hidden, meaningful, and potentially useful patterns from these vast data resources. Educational data mining ensures a quality education by analyzing educational data based on various aspects. In this paper, we have analyzed the…

The educational recommendation system to provide support for academic guidance and adaptive learning has always been an important issue of research for smart education. A bad guidance can give rise to difficulties in further studies and can be extended to school dropout. This paper explores the potential of Educational Data Mining for academic…

Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to persist. The other class must identify the…

The development in information science has enabled an explosive growth of data, which attracts more and more researchers to engage in the field of big data analytics. Noticeably, in many real-world applications, large amounts of data are imbalanced data since the events of interests occur infrequently. Classification of imbalanced data is an…

The high rate of students taking developmental education courses suggests that many students graduate from high school unready to meet college expectations. A college readiness screener can help colleges and school districts better identify students who are not ready for college credit courses. The primary audience for this guide is leaders and…

User clustering algorithms have been introduced to analyze users' learning behaviors and help to provide personalized learning guides in traditional Web-based learning systems. However, the explicit and implicit coupled interactions, which means the correlations between user attributes generated from learning actions, are not considered in these…

This commentary to the recent article by Musso et al. (2013) discusses issues related to model fitting, comparison of classification accuracy of generative and discriminative models, and two (or more) cultures of data modeling. We start by questioning the extremely high classification accuracy with an empirical data from a complex domain. There is…

The usage of data mining has dramatically increased over the past few years and the education sector is leveraging this field in order to analyze and gain intuitive knowledge in terms of the vast accumulated data within its confines. The primary objective of this study is to compare the results of different classification techniques such as Naïve…