Adaptive Learning Technology Research Articles

Understanding gap between perception and expectations for artificial intelligence: Implications for sustainable development goals 4 and 9

This research is focused on the gap created in the perception and expectations of Kosovar students in the implementation of artificial intelligence during their learning and education. The research was conducted with students who had attended training on Artificial Intelligence that to identify the gap between their perception and expectations regarding artificial intelligence. The aim of this study is to investigate the understanding gap between perceptions and expectations of artificial intelligence and to provide answers to the research questions posed in this paper. Obejectiv of the study is to shed light on the need for Sustainable Development Goal 4, Quality Education, by emphasizing the importance of inclusive and equitable AI literacy, and also for Development Goal 9, Industry, Innovation, and Infrastructure, by fostering innovation and supporting the development of resilient and sustainable AI infrastructure. During the research we used purposive sampling since we already had access to the database showing which students had attended the training and were certified for artificial intelligence by Microsoft. Bazed on the use of adaptive learning technology, wich is one of the ways Artificial Intelligence can have an impact on student psychology, and based on each student's performance, interests and learning style, we concluded this technology tailors the learning experience to them, and learning is more effective and efficient, and motivation and engagement also increase more.

Assessing cognitive flexibility in adaptive learning technologies for special education

This study used quantitative methods to examine the correlation between adaptive learning technology and cognitive flexibility in kids receiving special education. The study included a cohort of 120 kids, ages 8–12, who were diagnosed with particular learning difficulties, ADHD, or autism spectrum disorder. Cognitive flexibility was evaluated using the Wisconsin Card Sorting Test (WCST), while the utilization of adaptive learning technologies was quantified using self–report questionnaires. The data was analyzed using several statistical methods, such as independent samples t-tests, regression, Pearson correlation coefficients, ANOVA, and ANCOVA. The findings revealed a noteworthy and favorable correlation between the utilization of adaptive technology and the scores of cognitive flexibilities. This correlation remained significant even after accounting for demographic characteristics. Moreover, it was shown that the diagnostic status had a moderating effect on the correlation between the utilization of adaptive technology and cognitive flexibility. The results emphasize the capacity of adaptive learning technologies to improve cognitive flexibility abilities in kids with special needs, offering significant knowledge for educators, legislators, and technology developers.

Detection of Ginkgo biloba seed defects based on feature adaptive learning and nuclear magnetic resonance technology

Detection of Ginkgo biloba seed defects based on feature adaptive learning and nuclear magnetic resonance technology

Domain Transfer-Based Hypergraph Convolutional Network for Posture Anomaly Detection in Physical Education Teaching

Human skeleton-based posture anomaly detection has been widely applied in the field of physical education teaching. The existing spatio-temporal graph convolutional networks (ST-GCN) can fully utilize the local and global information of the human skeleton for action recognition, but the entire model requires a large amount of computation and the modeling of high-order relationships between joint points of the human skeleton is insufficient. To this end, this paper proposes a novel domain adaptive hypergraph convolutional network for basketball posture anomaly analysis by exploiting 2D skeleton information. First, we designed an effective hypergraph convolution feature extraction network to improve the high-order dependency modeling. To further improve the performance of the hypergraph convolutional network, we introduce domain adaptive learning technology to supervise the feature extraction learning of the target domain (2D skeleton) through the source domain (3D skeleton). At last, we construct a basketball action teaching analysis dataset for model evaluation. We conducted a large number of comparative experiments on the public dataset NTU RGB+D and our self-built dataset. All the results showed that our proposed hypergraph convolutional model effectively extracts features of 2D human skeletons, and by introducing domain adaptive learning, the performance of basketball anomaly detection is further improved.

Tailoring Education: A Comprehensive Review of Personalized Learning Approaches Based on Individual Strengths, Needs, Skills, and Interests

Background and Aims: Personalized learning is important because it tailors educational experiences to each student's needs and strengths, resulting in more effective and engaging learning. This approach not only improves academic performance but also accommodates students' learning styles and interests, resulting in increased motivation and success. This paper investigates Tailoring Education: A Comprehensive Review of Personalized Learning Approaches Based on Individual Strengths, Needs, Skills, and Interests. Methodology: This paper conducted a thorough analysis of various personalized learning approaches, such as adaptive learning technologies, differentiated instruction, and project-based learning. It combined findings from empirical studies, case studies, and theoretical frameworks to assess the efficacy and impact of these approaches on individual student needs and academic outcomes. Results: The finding found that personalized learning approaches demonstrate their significant potential for meeting individual student needs and improving educational outcomes. Adaptive learning technologies and differentiated instruction have successfully tailored learning experiences to students' strengths, whereas competency-based and project-based learning provides flexible, student-centered pathways for meaningful engagement and academic progress. Collectively, these methodologies help to create a more individualized and responsive educational environment. Conclusion: The findings show that personalized learning approaches effectively meet individual student needs while improving educational outcomes. These methodologies foster a more responsive and personalized learning environment by tailoring experiences to students' strengths and providing flexible, student-centered pathways.

Cloud-Enabled Personalization: Transforming Educational Paradigms through Adaptive Learning Technologies

This article examines the transformative impact of cloud computing on personalized learning in education, exploring how cloud-based technologies are reshaping educational paradigms. Through a comprehensive analysis of adaptive learning platforms, real-time feedback systems, and learning analytics, we demonstrate how cloud computing facilitates tailored educational experiences. The article addresses key challenges in education, including accessibility, resource allocation, and costeffectiveness, showcasing cloud-based solutions that enhance collaboration and inclusivity. We explore the evolution of teaching and learning tools, from virtual classrooms to interactive content delivery, and their role in improving student outcomes through early intervention systems and engagement-enhancing techniques. The global reach and scalability of cloud-based education are examined, highlighting its potential for cross-border learning and ability to meet fluctuating demands. Our findings indicate that cloud computing is not merely a technological advancement but a fundamental shift in educational methodology, offering unprecedented opportunities for personalized, efficient, and globally accessible learning experiences. This article contributes to the growing body of knowledge on educational technology and provides insights for educators, institutions, and policymakers seeking to leverage cloud computing for educational excellence.

Co-design: From Understanding to Prototyping an Adaptive Learning Technology to Enhance Self-regulated Learning

Co-design: From Understanding to Prototyping an Adaptive Learning Technology to Enhance Self-regulated Learning

EFFECTIVENESS OF DUOLINGO IN FOREIGN LANGUAGE LEARNING

This study aims to explore the efficiency of Duolingo as a language learning tool, with a particular focus on its gamified nature, adaptive learning technology and strategic use of notifications. In this regard, the study employed qualitative research design wherein two semi-structured interviews were held. Specifically, these participants were housewife who had been using Duolingo daily for almost 37 days to learn English and a student who was intermittently using it to learn Turkish. In conclusion, these findings show that apart from being interactive and engaging, Duolingo significantly improves vocabulary and grammar skills. Furthermore, such game-like features as rewards, competitions and streak counts act as strong incentives for continued usage. Additionally, customized messages delivered in real time can help in bringing back users into the application and ensuring daily routine learning is maintained. Nevertheless, despite its pros there are several cons that have been found; they include restriction of lives in free version and lack of practice realistic communication scenarios. Accordingly, researchers recommend introducing artificial intelligence for improved speaking practice and enhancing the number of lives to further improve the functionality of DuoLingo.com. These facts help give more insights on how digital tools can support language acquisition across diverse contexts. Key words: Duolingo, Language Learning, Gamification, Vocabulary, Grammar and Motivation.

Adaptive Learning Models for Diverse Classrooms: Enhancing Educational Equity

This review paper explores the transformative potential of adaptive learning models in promoting educational equity. Adaptive learning systems leverage advanced algorithms and digital tools to provide personalized instruction tailored to individual student needs, enhancing academic outcomes and fostering greater engagement and confidence. The theoretical foundation of adaptive learning aligns with constructivist and socio-cultural theories, emphasizing the importance of personalized and differentiated instruction. Evidence suggests adaptive learning improves academic performance among diverse and underserved student populations. However, significant barriers to implementation persist, including technological, financial, and pedagogical challenges, as well as ethical considerations related to data privacy and algorithmic fairness. The paper highlights the implications of these findings for educational policy and classroom practice, advocating for equitable access to adaptive learning technologies and comprehensive professional development for educators. The evolution of adaptive learning, driven by advancements in artificial intelligence and machine learning, promises further educational equity and inclusivity advancements. Keywords: Adaptive Learning, Educational Equity, Personalized Instruction, Digital Tools

EFFECTIVE PEDAGOGICAL STRATEGIES FOR DIGITAL LEARNERS

This study investigated the effectiveness of various pedagogical strategies in digital learning environments, focusing on blended learning, gamification, adaptive learning technologies, interactive multimedia content, and frequent feedback. Employing a mixed-methods approach, the research combined quantitative surveys and qualitative interviews with students and educators to assess the impact of these strategies on engagement, motivation, and academic performance. The results revealed that blended learning enhances flexibility and personalization, gamification increases motivation through interactive elements, and adaptive technologies tailor content to individual needs. Interactive multimedia content improves retention and understanding of complex concepts, while frequent feedback supports continuous learner development. The findings emphasized integrating diverse pedagogical strategies to optimize digital education and address varying learner needs. Future research should explore the long-term impacts of these strategies, emerging technologies, and their effectiveness across different educational contexts to ensure equitable and compelling learning experiences. KEYWORDS: blended learning, gamification, adaptive learning technologies, multimedia content, frequent feedback, digital learning, educational technology

Application of Adaptive Machine Learning in Non-Stationary Environments

In the current data-driven era, the application of adaptive machine learning in non-stationary environments has become particularly important. This paper explores the basic concepts of adaptive machine learning and its applications in financial market forecasting and industrial equipment monitoring, demonstrating its superior performance in high-noise, dynamic environments. The research results indicate that adaptive machine learning models significantly improve prediction accuracy, response speed, and robustness through strategies such as online learning and incremental learning. In financial market forecasting, the mean squared error (MSE) was reduced to 0.015, and the fault detection accuracy in industrial equipment monitoring increased to 95%. The paper also proposes future research directions, including multi-source data fusion, anomaly detection, computational efficiency enhancement, and model interpretability. Adaptive machine learning technology not only enriches the theoretical framework of machine learning but also provides new solutions for practical applications in various fields, paving the way for further development of intelligent systems.

ROLE OF ARTIFICIAL INTELLIGENCE FOR ADAPTIVE LEARNING ENVIRONMENTS IN HIGHER EDUCATION BY 2030

This study focuses on the ability of Artificial Intelligence (AI) to redesign learning experience of higher education by making learning adaptable by the year 2030. Machine learning and natural language processing afford the possibility of developing adaptive learning environment for students. The research also focuses on AI's present and future uses in the learning & issues of realizing those uses. The purposive sampling technique selected 5 faculty members from different universities. A semi-structured interview guide was developed to get data from the participants. Data was analyzed thematically by facilitation of NVivo 14. The potential of AI for enhancing personalized tasks, automated tasks related to administration, and creating interactive learning experiences. The concerns of data confidentiality and ethical considerations were also addressed. By analyzing the improvement of adaptive learning technologies, the study presents views of how AI can improve educational outcomes. Therefore, the findings also emphasize the diverse implications of equalizing technological innovation with keeping important human fundamentals in education while highlighting the justice and inclusivity.

Utilizing Technology to Assess English Learning Outcomes of Students Based on a Competency-based Approach

This study addresses the research gap in the integration of technology in evaluating English language learning outcomes, aiming to improve efficiency, accuracy, and flexibility in competency-based assessments. The research employs a mixed-methods approach, using quantitative data from online assessments. Participants included educators and learners engaged in English language programs across various educational institutions. The findings indicate that adaptive learning technologies significantly enhance accessibility, scalability, and student engagement in English language assessments. However, challenges such as technological limitations, data privacy issues, and the need for comprehensive teacher training are highlighted. This research underscores the potential of technology to transform competency-based English language proficiency examinations while offering practical insights for educational institutions, policymakers, and ed-tech companies to optimize these advancements effectively.

Revolutionizing Autism Education: Harnessing AI for Tailored Skill Development in Social, Emotional, and Independent Learning Domains

This review explores the potential of artificial intelligence (AI) to address the special needs of children with autism. It examines the issues of specific skill development in the course of this review through some features in areas such as Social Skills Training, Emotional Regulation Support, and Independent Learning Skills. This study incorporates input from psychology, education, and technology. Although the article lacks presentation of the original empirical data, it nonetheless synthesizes existing information to provide a comprehensive overview of the current state of the field. The paper firmly recommends the use of individually adapted interventions, leveraging virtual reality, natural language processing, and adaptive learning technologies. This detailed and futuristic approach will help to augment the speed of autism education, via personalizing and enhancing social, emotional, and independent learning for individuals with ASD to address current challenges and ethical considerations. It contributes by proposing AI-driven solutions to improve educational outcomes and promote greater inclusivity and adaptability in autism education.

Adaptive Learning Technologies: Personalizing Education for Students with Special Needs

Adaptive Learning Technologies: Personalizing Education for Students with Special Needs

Implementing adaptive learning technologies: Practical strategies for enhancing cognition in mathematics education

Recent studies have shown that adaptive learning technology can significantly change mathematics teaching. This research used a combination of methods to explore how adaptive learning technologies (ALTs) can improve cognitive abilities in math instruction. The study involved 300 secondary school students. Quantitative data was collected through pre-tests and post-tests to evaluate problem-solving, critical thinking, and logical reasoning skills, as well as a survey on students' opinions about ALTs. Qualitative data was gathered by analyzing participant responses in depth. The quantitative data was analyzed using descriptive statistics, paired samples t-tests, ANCOVA, correlation analyses, and regression analyses. The qualitative data was examined using thematic analysis. The results showed significant improvements in cognitive abilities with the use of ALTs, supported by both quantitative and qualitative data. Additionally, using ALTs was positively linked to the development of cognitive skills. These findings enhance our understanding of the importance of ALTs in mathematics education and provide useful insights for teachers, curriculum developers, and policymakers.

The Impact of Adaptive Learning Technology on Improving Students’ Concept Understanding

Adaptive learning technology is an educational method that uses artificial intelligence and computer algorithms. This learning system can manage students’ interaction pattern during learning activities. The use of adaptive learning technology is able to change students from just receiving information to an active and collaborative part in the learning process. This research was conducted with the aim of improving the quality of education in Indonesia by encouraging teachers to use this technology. This research also aims to provide a better understanding of the potential and weaknesses of adaptive learning technology in improving students’ concept understanding as well as providing stronger guidance for curriculum development and better educational practices. The method used in this research is quantitative method. This method is a way of collecting numerical data that can be tested. Data is collected through the distribution of questionnaires addressed to students. Furthermore, the data that has been collected from the distribution of the questionnaire, will be accessible in Excel format which can then be processed with SPSS. From the results of the study, it can be seen that the impact of using adaptive learning technology shows that adaptive learning technology can improve the quality of education. Research shows that with the use of adaptive learning technology, it can change teaching methods, learning materials, and can find out the level of learning difficulties faced by these students. From this study, researchers can conclude that the impact of using adaptive learning technology, can improve student understanding and achievement and has the potential to improve the quality of education. with the existence of adaptive learning technology, it is able to increase student involvement and motivation in learning, so that student understanding in learning can be achieved well.

Teaching with an adaptive technology

In this paper we present the first findings from a 4 year longitudinal study that investigates the interplay between an adaptive learning technology and educators and students in a nurse education in Denmark. With inspiration from grounded theory we analyse data from 14 hours of classroom observations supplemented by educator interviews, student surveys and mini interviews with students. In the paper we focus on the role of a new technology in the classroom: we ask how data reports from an adaptive learning technology influence educators’ preparations for classroom activities, and we ask how students' technology acceptance evolves over time. Analyzing our data we see three main themes emerge that contributes to answer the research question: Interpretative inclinations: The adaptive technology contributes to planning the lessons with a recurring inclination in practice: wrong answers lead to more educator presentations, and correct answers lead to student-centred group work during class. Implicit comparisons: The use of data reveals the educator’s expectations and assumptions when she implicitly compares the data on the students with her intuition, and when she compares the technology with the physical book. The implicit comparisons make the use of data challenging for the educator. Reverse adoption: The students find it hard to grasp the intention of the adaptive technology, but they accept and learn to adapt and appreciate it over time. In contrast to e.g. Rogers’ technology acceptance theory we find that the adaptive learning technology cannot be evaluated per se: in order to describe how a learning technology is accepted and what role it comes to play in an educational context, the parameter of time is mandatory to include in the research design.

The future of mathematics education: Adaptive learning technologies and artificial intelligence

The future of mathematics education: Adaptive learning technologies and artificial intelligence

Exploring the Challenges and Future Directions of Big Data and AI in Education

The integration of Big Data and Artificial Intelligence (AI) in education holds transformative potential, promising enhanced personalized learning experiences, improved administrative efficiency, and advanced predictive analytics. However, the adoption of these technologies also presents significant challenges. This paper explores the current landscape of Big Data and AI in education, identifying key challenges such as data privacy concerns, the digital divide, the need for teacher training, and the integration of AI with existing educational frameworks. Additionally, it examines potential future directions, including the development of ethical guidelines, advancements in adaptive learning technologies, and the creation of more inclusive and equitable AI systems. By addressing these challenges and leveraging future opportunities, the educational sector can harness the full potential of Big Data and AI to improve learning outcomes and operational efficiencies.