Augmented reality (AR) and virtual reality (VR) have attracted increasing interest in the field of education, largely because of their potential to create immersive and stimulating learning environments. These technologies appear to offer alternative approaches to traditional instruction, enabling interaction between teachers and learners in virtual spaces. Several studies have suggested that AR and VR tools can increase student engagement across a range of subjects and provide meaningful support in classroom settings. This systematic narrative review investigates the benefits, challenges, and application domains of AR and VR in education by analyzing studies published over the past ten years. The literature was gathered from six major academic databases: Scopus, Web of Science, ScienceDirect, ERIC, IEEE Xplore, and the ACM Digital Library. Following the preferred reporting items for systematic reviews and meta-analyses 2020 guidelines, 53 relevant articles were identified and analyzed. The findings indicated mostly positive outcomes tied to the adoption of AR and VR in education, especially in improving learning. Simultaneously, several recurring challenges have been reported, posing barriers to widespread implementation. The review concludes by highlighting areas where further research is necessary to integrate these technologies into educational practices more effectively.

Educational data mining (EDM) is a specialized field within data mining that focuses on extracting valuable insights from academic data across high school and university levels. A common practice in EDM involves predicting students’ grades to identify at-risk individuals and improve the efficiency of academic tasks. This knowledge benefits students, parents, and institutions equally. Early detection enables interventions that improve student performance. The literature presents various prediction strategies, each with its own unique advantages and disadvantages. This study aims to comprehensively evaluate the methods, tools, and applications of machine learning (ML) and data mining (DM) in education. The main goal is to improve the accuracy of predicting academic achievements by employing eight widely recognized ML algorithms: naïve bayes (NB), k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), logistic regression (LR), extreme gradient boost (XGBOOST), and ensemble voting classifier (EVC). The focus is on improving data quality by eliminating instances of noise. Performance evaluation involves assessing parameters such as accuracy, precision, F-measure, and recall. Incorporating cross-validation and hyperparameter tuning improves classification accuracy. The ML models outperform other ensemble approaches, providing a valuable tool for predicting student performance and assisting educators in making proactive decisions through timely alerts.

The probabilistic cone b-metric space is a novel concept that we describe in this study along with some of its fundamental topological properties and instances. We also established the fixed point theorem for the probabilistic nonlinear Banach contraction mapping on this kind of spaces. Many prior findings in the literature are generalized and unified by our findings. In order to illustrate the basic theorem in ordinary cone b-metric spaces, some related findings are also provided with an application to integral equation.

In the realm of competency-based education, the integration of adaptive AI-driven assessment strategies brings forth a paradigm shift in evaluating learner mastery. This chapter delves into the intricacies of designing learning scenarios that seamlessly blend pedagogy with AI algorithms to offer personalized, data-informed assessments. By meticulously selecting objectives, designing pedagogical approaches, and orchestrating learner activities, educators create a foundation for adaptive assessment. The integration of AI algorithms enhances evaluation precision, enabling real-time identification of learning gaps and strengths. This chapter delves into the application of machine learning algorithms for tailored feedback, remediation, and ongoing supervision, fostering a learner-centric environment. Through real-world cases and innovative practices, educators gain insights into crafting assessment systems that empower learners to excel in a competency-driven landscape. Request access from your librarian to read this chapter's full text.

This chapter explores the transformative impact of artificial intelligence (AI) in shaping adaptive, personalized learning experiences that cater to students' unique needs. By employing AI-driven platforms, educators can create learning pathways that dynamically align with students' strengths, interests, and preferences. The benefits are substantial, as AI enhances engagement, motivation, and achievement by adjusting content in real-time to match individual learning styles. Alongside these advantages, the chapter addresses critical challenges such as safeguarding data privacy, mitigating algorithmic bias, and ensuring inclusivity across diverse learner backgrounds. Ultimately, this chapter underscores AI's potential to redefine education, while emphasizing the ethical considerations crucial for its equitable and responsible integration into classrooms.

Augmented reality (AR) has attracted significant attention and has become an important topic for many professionals and researchers. This is due to the capability of AR to offer a multitude of advantages that can enrich various domains of life. In education, AR technology has been increasingly used to create new learning experiences and transform real 3D (three-dimensional) or 2D (two-dimensional) objects into virtual elements, capturing users’ interests and motivation while fostering the growth of various human abilities and attitudes. The integration and use of educational games and applications based on AR in mathematics education have historically grown, and their impact has been beneficial for teaching mathematics and creating effective pedagogical scenarios. This paper presents an experiment we will undertake with our educational mobile application, GeoFormeAR, to animate a lesson on “Cylinder and Prism” and then compare it with a similar lesson delivered using the traditional approach in the second class. A post-test was administered to measure the students’ understanding and application of geometric concepts. Additionally, a questionnaire was distributed to collect the students’ perceptions of usability and engagement. The results of this study indicate a significant difference in performance scores between students using the GeoFormeAR mobile application and those learning geometry using traditional methods. Moreover, the questionnaire results reveal positive student perceptions regarding the usability and engagement of the GeoFormeAR mobile application. In conclusion, this study aims to broaden the scope of future vision on the conceptions and adoption of AR in mathematics teaching and learning.