Intelligent Agent-Based e-Learning System for Adaptive Learning

To better respond to people’s demands for multimedia learning, appropriate learn-ing paths should be offered based on their actual learning demands and different knowledge levels. Adaptive online learning model integrates and improves exist-ing learning frameworks to offer a set of knowledge paths that can cater to dif-fer7ent preferences, tastes, and knowledge levels of learners, no need for them to be aware of this. Based on the improved ant colony algorithm, an adaptive learn-ing system model that can satisfy learners’ demands is built herein with reference to the foraging approach of ants to traverse the paths, thereby to find the best learning path, while the classification method for some learning objects can de-termine the search parameters. This innovative approach proposed hereof can help improve learners' academic performance and learning efficiency.

A feature-free method for testing adaptive network learning models is presented. The test is based on a property called the mean matching law, a. property shared by many adaptive network models of learning. As an application of this method, we prove that cue competition effects obtained with statistically independent cues cannot be explained by many previous adaptive network learning models, including those based on the delta learning rule. These results point to the need to incorporate competitive learning properties into adaptive network learning models.

The impact of general and specific performance and self-efficacy on learning with computer-based concept mapping

A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors

Abstract. Problem. The rapid advancement of artificial intelligence (AI) has significantly impacted various domains, particularly education. AI-driven tools are increasingly being integrated into teaching methodologies, assessment frameworks, and curriculum design. However, despite the growing research interest in Artificial Intelligence in Education (AIED), the field remains fragmented, lacking a comprehensive analysis of dominant research themes and emerging trends. Understanding these trends is crucial for optimizing AI applications in education and shaping future developments in the field. Goal. The primary goal of this study is to analyze the current landscape of AIED research by identifying key thematic clusters, evaluating dominant research directions, and predicting future trajectories in AI-driven education. Methodology. This study employs network analysis, topic modeling, and global research trend evaluation to analyze scientific publications in AIED. The Latent Dirichlet Allocation (LDA) algorithm is applied to classify research articles into thematic categories, enabling the identification of primary topics shaping the field. Data is sourced from the Web of Science database, and hierarchical clustering techniques are used to determine semantic similarities between thematic models. Results. The analysis reveals five major research clusters in AIED, including AI applications in medical education, machine learning in education, adaptive learning technologies, large language models in learning, and student-centered AI-driven educational strategies. The study identifies increasing trends in the adoption of generative AI and chatbot-based learning support, while traditional AI-driven assessment methodologies show moderate growth. The results also highlight strong interconnections between AI-driven personalization and intelligent tutoring systems, emphasizing the shift toward adaptive and student-centric learning environments. Originality. This research provides a structured and systematic analysis of AIED trends, leveraging advanced topic modeling techniques. Unlike previous studies focusing on isolated AI applications in education, this study presents a holistic view of thematic structures in AIED research, offering a quantitative and data-driven approach to understanding the evolution of the field. Practical Value. The findings can serve as a strategic reference for educators, policymakers, and AI developers in shaping future educational technologies. By identifying key thematic directions, this research supports evidence-based decision-making for integrating AI into educational systems. The results can also guide further research in AIED, helping scholars explore emerging AI-driven teaching methodologies, adaptive learning models, and ethical considerations in AI-powered education.

This writing aims to determine the various forms of student problems and alternative solutions that can be given to the problems experienced by students in the learning process in class V SDN 101888 Sei Merah. This study uses a qualitative descriptive research type to present a complete picture of the problems in a system of thought or current event class using a pedagogical and sociological approach. The study was conducted at SDN 101888 Sei Merah with the research subjects being 34 class V students. The data collection method used in the study is based on data obtained from interviews and processing and describing meaningful and easily understood data, namely by conducting observations by observing class 5 students. The results of the study indicate that the form of student problems in learning experienced by class V students of SDN 101888 Sei Merah is difficulty in student development and individual differences in students as well as alternative solutions for educators according to researchers. There are two factors that cause learning difficulties, namely internal factors and external factors. Educational problems found by researchers based on observations of elementary schools in grade 5 include student development, individual differences between students, learning processes, memory, knowledge, learning theories, learning motivation, and measurement and evaluation in learning.

This study investigates the long-term impact of adaptive digital learning ecosystems on students' self-regulated learning (SRL) behaviors and academic persistence. Adaptive learning systems personalize the learning experience by adjusting content and feedback to meet individual students' needs, preferences, and performance. These systems enhance engagement, motivation, and learning outcomes through real-time adjustments and continuous feedback. The research aims to explore how adaptive learning systems influence SRL and academic persistence in university courses over time. Using a longitudinal quantitative design, the study tracks SRL behaviors and academic persistence at multiple points during the semester. Results show significant improvements in SRL behaviors such as goal setting, planning, self-monitoring, and reflection among students engaged with adaptive learning environments. These students exhibited greater autonomy, improved metacognitive awareness, and higher motivation. Additionally, students in adaptive systems demonstrated greater academic persistence, as indicated by more time spent on tasks, higher assignment completion rates, and sustained engagement. The findings suggest that adaptive learning platforms promote SRL and academic persistence by offering personalized, responsive learning experiences. Unlike static, non-adaptive environments, adaptive systems provide dynamic support, enhancing students' ability to regulate their learning and remain engaged despite challenges. The study concludes that adaptive learning systems are vital for long-term academic success, though further research is needed to assess the sustainability of these effects in various educational settings and among diverse student populations.

This study aims to examine the effect of using Canva-based learning media on learning outcomes of Islamic Religious Education (PAI) in class III SDIT Al-Insan Pinrang, as well as how it relates to individual learning styles of students. The problem raised is the extent to which the use of Canva media can improve students' learning outcomes compared to PowerPoint media, taking into account visual, auditory, and kinesthetic learning styles. This study used a quantitative approach with a quasi-experimental non-equivalent control group design, where learners were divided into two groups, namely the experimental group using Canva and the control group using PowerPoint. The research design also involved a 2x3 factorial analysis to see the interaction between learning media and learning styles. Data was collected through pre-test and post-test to measure learning outcomes, as well as a questionnaire to identify learners' learning styles. Hypothesis testing was conducted using two independent samples t-test, one-way ANOVA, and Kruskal-Wallis to see the differences between experimental and control groups, as well as differences based on learning styles. The results showed that the use of Canva had a significant effect on PAI learning outcomes, especially for students with visual and kinesthetic learning styles. However, there was no significant difference between the auditory learning style groups using Canva and PowerPoint. This finding reinforces the importance of considering learning styles in choosing learning media, and shows that visual design-based learning media, such as Canva, can increase learners' motivation and active participation, especially in learning that requires greater visual engagement. This research is beneficial for the development of more adaptive and effective learning methods in primary schools.

New technological advances in user mobility and context immersion are enabling novel adaptive and pervasive learning models in ambient environments. In this paper, we aim at developing a novel ubiquitous learning model within a pervasive smart campus environment. We define a model of a smart campus, and advocate learning practices in the light of new paradigms such as context-awareness, ubiquitous learning, pervasive environment, resource virtualization, autnomic computing and adaptive learning. In this environment, we profile learners and augment physical campus structures to advocate contextaware learning processes. We also suggest a social community platform for knowledge sharing which involves peer learners, domain experts as well as campus physical resources.

This study was conducted because many students still face difficulties in learning, as the instructional methods used have not yet been implemented in ways that facilitate student understanding. Therefore, the Means-Ends Analysis (MEA) learning model was employed to guide students through a step-by-step thinking process, aiming to make it easier for them to achieve their learning goals and improve their academic performance. The purpose of this study was to examine the effect of the Means-Ends Analysis (MEA) learning model on students' learning outcomes in Islamic Religious Education (PAI) at SMA Negeri 10 Bandar Lampung. The research employed a quantitative method with a quasi-experimental design. The sample was selected randomly, with class X.5 assigned as the experimental group and class X.1 as the control group, each consisting of 30 students. The instrument used was a multiple-choice test. The data analysis began with tests for normality and homogeneity, followed by hypothesis testing using the Independent Samples T-Test if the data were normally distributed and homogeneous, or the Mann-Whitney U Test if the data were not normally distributed, to determine the effectiveness of the MEA learning model on student learning outcomes. The normality test results indicated that the data were not normally distributed (sig. 0.000 < 0.05), so the Mann-Whitney U Test was used, yielding a value of 347.500. The homogeneity test showed that the data were homogeneous (sig. 0.309 > 0.05). The hypothesis test results revealed a significance value of 0.757 (> 0.05), indicating that there was no significant difference between the experimental and control groups. Therefore, the MEA model has not been proven effective in improving learning outcomes in Islamic Religious Education. Other contributing factors, such as student motivation and the teacher’s instructional approach, also need to be considered. The research findings suggest that the implementation of the Means-Ends Analysis (MEA) learning model did not significantly influence the improvement of students’ academic performance. This may be due to several factors, including students’ limited understanding of the steps involved in the MEA model, restricted instructional time, or suboptimal implementation by the teacher. It is suspected that the application was either not optimal or not well aligned with students’ characteristics. Therefore, it is recommended that teachers adopt instructional models that are better suited to students' needs and engage in evaluation and training efforts to enhance the quality of instruction.

Currently, the online learners are increasingly demanding more personalized learning since the web technology, and the learners have individual features of characteristics such as learning goals, experiences, interests, personality traits, learning styles, learning activities, and prior knowledge. A personalized learning process requires an adaptive learning system (ALS). In order to adapt, a learner model is required. Thus, modelling the learner model in an adaptive system environment is a key point to success in recommending the learner. The ontology-based approach was used to model the adaptive learning model in this research. Ontology is a graph structure that consists of a collection of contexts, relationships, and models which related to contexts. The ontology of the learner model enables to produce a description of learner’s properties which contains important information about domain knowledge, learning performance, interests, preference, goal, tasks, and personal traits. Keywords - Personalized Learning, Adaptive Learning System, Ontology, Learner Model

This study introduces an innovative approach for detecting Distributed Denial-of-Service (DDoS) attacks in Software-Defined Networks (SDNs) with a multi-controller setup, specifically tailored to secure Internet of Things (IoT) devices.Traditional DDoS detection techniques struggle with the dynamic and sophisticated nature of modern network threats, particularly in complex and scalable environments like SDNs.To address these challenges, our objective is to develop a methodology that integrates adaptive learning and feedback mechanisms to continuously evolve and improve detection accuracy.Our approach utilizes adaptive learning models that dynamically adjust to new data and emerging attack patterns, learning continuously from network behaviour to detect subtle and novel attack strategies effectively.By implementing a system of feedback loops, our detection algorithms receive real-time insights from the operational environment, allowing immediate adaptation to changing network conditions and attack techniques.Advanced feature selection methods are employed to identify the most relevant data points from network traffic.Through ROC curve analysis of various machine learning models-RNN, LSTM, GAN, and Auto Encoderusing Chi-Square, Decision Tree, and PCA feature selection methods, PCA consistently demonstrated superior performance.It achieved higher true positive rates across various false positive rates, indicating better classification accuracy.The Auto Encoder model, combined with PCA, exhibited the best results, highlighting its efficacy in balancing sensitivity and specificity for anomaly detection.Experimental evaluations conducted using the CICDDoS2019 dataset highlight the effectiveness of our approach.The proposed Auto Encoder model with PCA feature selection achieved the highest accuracy at 99.56%, surpassing other models such as LSTM with feature selection, which achieved 99.42%, and RNN LSTM, which had the lowest accuracy at 89.63%.These results demonstrate the effectiveness of our approach in enhancing the accuracy and reliability of network traffic anomaly detection, particularly for DDoS attacks involving IoT devices.Overall, our research combines adaptive learning and feedback loops to create a dynamic, intelligent DDoS detection system for SDNs, advancing network security in handling evolving threats.

This study aims to find out how teachers diagnose to determine students’ learning difficulties, especially in Mathematics in Elementary Schools. There are four aspects discussed in this study, including the teachers’ perception of students’ learning difficulties, teachers’ practice in connection with diagnosing students’ learning difficulties, teachers’ actions in response to the results of the diagnosis, parties involved regarding students’ learning difficulties. The research method used was descriptive qualitative with 13 teachers as the subject selected by purposive sampling. The data were obtained through interviews with classroom teachers, both from private and public schools, especially in North Borneo. The findings in this study show that the teachers use various ways to diagnose and overcome students’ learning difficulties. There are 3 ways that teachers do to diagnose learning difficulties in Mathematics, including tests, observations and interviews. While the ways to overcome them are to do remedial, provide tutoring individually, provide additional hours for students who have learning difficulties in Mathematics and involve students in learning. The method used has been effective, proofed by changes in students’ understanding and improvement in students’ learning outcomes. This study offers elementary school teachers ways that can be carried out to diagnose and overcome students’ learning difficulties in Mathematics.

In this article, we will look at why people react differently to stress. We will analyze the results of a number of empirical studies of individual differences in students' reactions to stress during study and outside study time. We will explore the factors that influence individual differences in the emergence of a stress response. We compare genetic, acquired, and dispositional individual differences and give examples of each of these categories to show how one or another difference affects the occurrence of stress. Traditionally, psychologists have paid much attention to how people cope with stress and how they react to stressors.
 Much of this interest is based on the general belief that some people are more prone to stress and more vulnerable than "others".
 Therefore, people are distributed within some continuum of stress resistance. It turned out that some people are prone to stress, while others rarely experience stress even under difficult conditions. This idea has attracted the interest of universities because this approach makes it possible to identify stress factors that determine the contingent with a stable psyche. In the field of education, this idea is attractive because it implies the possibility of identifying people who are prone to stress, with the help of which appropriate stress management programs can be developed. The analysis of scientific literature shows that the value of self- assessment of the complex of stress manifestations will be the higher, the worse the subject feels, the less he believes in his strength, abilities, energy, independence, the lower the assessment of his ability to control his own life and be independent [1].

Abstract. This study examines the relationship between students' personality and intelligence scores with their preferences for the personality profile of their lecturers. Student ratings (N = 136) of 30 lecturer trait characteristics were coded into an internally reliable Big Five taxonomy ( Costa & McCrae, 1992 ). Descriptive statistics showed that, overall, students tended to prefer conscientious, open, and stable lecturers, though correlations revealed that these preferences were largely a function of students' own personality traits. Thus, open students preferred open lecturers, while agreeable students preferred agreeable lecturers. There was evidence of a similarity effect for both Agreeableness and Openness. In addition, less intelligent students were more likely to prefer agreeable lecturers than their more intelligent counterparts were. A series of regressions showed that individual differences are particularly good predictors of preferences for agreeable lecturers, and modest, albeit significant, predictors of preferences for open and neurotic lecturers. Educational and vocational implications are considered.