Personal Learning Research Articles

Enhancing Education and Teaching Management Through Data Mining and Support Vector Machine Algorithm

This study investigates the effectiveness of the SVM+TLBO approach for predicting student performance and evaluating learning abilities in educational settings. By integrating Support Vector Machines (SVM) with Teaching-Learning-Based Optimization (TLBO), the research aims to enhance predictive accuracy and efficiency compared to traditional methods, including Decision Trees, Ant Colony Algorithms, Clustering Algorithms, Convolutional Neural Networks (CNN), Neural Networks, Support Vector Machine (SVM) without Optimization. Results indicate that the SVM+TLBO model significantly outperforms these methods, providing robust predictions and valuable insights into student learning patterns. The findings underscore the importance of data-informed decision-making in education, enabling educators to identify at-risk students and tailor instructional strategies effectively. This research contributes to the growing field of educational data mining, highlighting the potential of advanced machine learning techniques to optimize educational outcomes and foster personalized learning experiences.

The Impact of AI-Powered Personalized Learning on Student Performance in Udaipur Colleges

The integration of Artificial Intelligence (AI) into educational settings has garnered increasing attention for its potential to enhance personalized learning and improve student outcomes. This study investigates the impact of an AI-powered personalized learning tool implemented at colleges in Udaipur. Using a mixed-methods approach, we collected quantitative data through pre-test and post-test scores and qualitative feedback via surveys and interviews with students and teachers. The results revealed significant improvements in student performance and engagement, with the AI tool providing tailored learning experiences that addressed individual needs. Statistical analyses demonstrated a substantial positive effect on academic outcomes, while qualitative feedback highlighted the tool's usability and effectiveness in identifying students' weaknesses. The findings suggest that AI-powered personalized learning can be a valuable asset in education, offering recommendations for broader adoption and further research in this field.

Adopting Digital Tools in Higher Education: Opportunities, Challenges and Theoretical Insights

ABSTRACTThe digital transformation of higher education, driven by technological advancements and evolving societal needs, presents both significant opportunities and critical challenges. This study integrates the technology acceptance model (TAM), diffusion of innovations (DOI), comprehensive literature review, in‐depth case studies and empirical survey data to explore these dynamics. The findings reveal the potential of digital technologies—such as online platforms, adaptive learning tools and immersive technologies—to expand access, personalise learning and foster collaboration. However, the research also identifies major barriers, including the digital divide, faculty resistance and data privacy concerns. By combining qualitative thematic analysis and quantitative statistical analysis, this study underscores the importance of strategic planning, inclusive policies and ongoing professional development in maximising the benefits of digital education. The paper contributes to the field by offering actionable insights, theoretical implications and practical recommendations for institutions to implement adaptive pedagogical strategies, ensure equitable access to technology and build robust, secure digital learning environments.

Analysis of Studies Based on Türkiye Examining the Relationship between Artificial Intelligence and Education: A Meta Synthesis Study

Artificial intelligence is defined as human-made systems that mimic the thinking, perception, and learning abilities of the human brain, using the information they gather and having the ability to improve themselves. With the changes and developments in the field of technology, the use of artificial intelligence has also expanded. The increasing popularity of artificial intelligence in the field of education has led to a rise in research on this topic. This paper aims to synthesize the existing literature on the applications of artificial intelligence in education within a specific time frame and to develop a holistic perspective. In the paper, 20 studies written in the last five years on artificial intelligence and education were examined using the metasynthesis method. The research question is defined as: "What trends exist in academic research on artificial intelligence and education conducted in Turkey over the past five years?" The data source for the research was determined using the criterion sampling method. For this purpose, a detailed search was conducted in databases such as Google Scholar, Academia, TÜBİTAK ULAKBİM, and Dergipark using the keyword "artificial intelligence and education." As a result of the analyses, it was found that while the study emphasizes the contributions of artificial intelligence to the field of education, there are also some concerns on the subject. The contributions of artificial intelligence, particularly in terms of providing personalized learning experiences, saving time, and improving student performance, were highlighted, but concerns were raised regarding data privacy and the ethical principles in the use of artificial intelligence. Additionally, it was found that there are educational and developmental needs for artificial intelligence users.

The Role of Artificial Intelligence (AI) in Personalized Learning: A Case Study in K-12 Education

This study investigated the mediating role of artificial intelligence (AI) in intensifying personalized learning in K-12 education by employing qualitative case research. The study examines how an AI deployed within a K-12 school changes the engagement, academic performance, and role of teachers through its implementation. Results show that AI tools lead to large improvements in student achievement through personalized learning paths, increased engagement, and the immediacy of feedback. Nevertheless, challenges including data privacy ponderings and the digital gap are significant insights for educators to consider as well. The report suggests guidelines for policymakers and practitioners to ensure the productive application of AI in personalized learning, including equal access to technology and continuous teacher upskilling.

ChatGPT Affordances and Indonesian EFL Students’ Perceptions in L2 Writing: A Collaborative Reflexive Thematic Analysis

ABSTRACT This study explores ChatGPT affordances, students’ use of prompts, and their perceptions of using the tool in EFL writing. With screen capture technology to observe students’ interactions with ChatGPT during writing process and semi-structured interviews, the data were analysed using a collaborative reflective thematic analysis. The results revealed that students’ use of ChatGPT has the potential to improve their essay quality in terms of lexical resources, grammatical range and accuracy, and coherence and cohesion. ChatGPT also provides such affordances as brainstorming, essay outlining, idea development, and feedback on hedging devices. Overall, students considered ChatGPT a valuable personal learning tutor that may stimulate student autonomy by offering immediate feedback in a less anxiety-provoking learning environment. However, some experienced challenges in producing effective prompts and understanding ChatGPT’s feedback. The study concludes that while the use of ChatGPT can enhance L2 writing development, teachers’ guidance is necessary during students’ interaction with the tool.

Applications of Big Data and Blockchain Technology in Food Testing and Their Exploration on Educational Reform

This study reviews the applications of big data (BD) and blockchain technology in modern food testing and explores their impact on educational reform. The first part highlights the critical role of BD in ensuring food safety across the supply chain, discussing various data collection methods, such as national and international food safety databases, while addressing the challenges related to data storage and real-time information retrieval. Additionally, blockchain technology has been explored for its ability to enhance transparency, traceability, and security in the food-testing process by creating immutable records of testing data, ensuring data integrity, and reducing the risk of tampering or fraud. The second part focuses on the influence of BD and blockchain on educational reform, particularly within food science curricula. BD enables data-driven curriculum design, supporting personalized learning and more effective educational outcomes, while blockchain ensures transparency in course management and credentials. This study advocates integrating these technologies into curriculum reform to enhance both the efficiency and quality of education.

Digital Platforms in Higher Education: Opportunities, Challenges, and Strategies

Abstract: Higher education institutions' adoption of digital platforms has grown rapidly in recent years. These platforms have transformed teaching and learning, providing innovative access, engagement and collaboration avenues. The emergence of digital platforms owes much to technological advances and the spread of Internet connectivity, making it easier for universities to utilize digital platforms for education. At the same time, the growing demand for flexible and personalized learning experiences has driven the adoption of digital platforms, which provide anytime, anywhere access to educational resources. However, adopting digital platforms also poses several challenges and important issues. This study aims to explore the role of digital platforms in higher education, focusing on identifying the opportunities they offer and the challenges they face for universities. This research is relevant to a wide range of stakeholders in the higher education sector, providing valuable insights for universities considering or implementing digital platforms and being beneficial to policymakers and educational organizations shaping the future of higher education.

Enhancing Human Knowledge and Capabilities with Artificial Intelligence Tools for Education

This study explores the different ways Artificial Intelligence (AI) tools bolster human knowledge and boost intellectual and creative efforts within the educational sector. It provides a broad examination of AI's potential to enhance educational processes and outcomes while also assessing the ramifications of AI-driven creative destruction on the job market encompassing both job displacement and the emerging scope for new knowledge creation. Purpose. The study aims to explore the different ways in which AI tools enhance human knowledge and capabilities and their role in augmenting human intellectual and creative endeavours in education. By examining how AI can improve educational processes and outcomes, it highlights the potential for significant advancements. Additionally, the study critically examines the implications of AI-driven creative destruction on the job market, focusing on the education sector. This includes understanding both job displacement and the creation of new opportunities that require advanced skills. Methods. This research involves a detailed analysis of various AI applications across the education domain. It employs virtual interviews with students and educators to provide detailed examples of AI's impact and reviews existing literature to contextualise these findings within broader economic implications. By focusing on the education sector, the study provides a comprehensive overview of how AI is being implemented and the outcomes of these implementations. This approach allows for a thorough understanding of both the benefits and challenges connected with AI integration. Results. The study suggests that AI significantly improves data processing capabilities, leading to notable advancements in educational research and personalised learning. These improvements can facilitate decision-making and innovation. However, AI also disrupts traditional employment patterns, displacing routine jobs that are easily automated. Conversely, it creates new roles that demand advanced technical skills and continuous education, highlighting a shift in the job market toward more specialised and high-skill positions. Conclusion. While AI can present substantial benefits in terms of efficiency and innovation, it poses significant challenges in the form of job displacement. To manage these transitions effectively, strategic responses from policymakers and educational institutions are essential. These strategies should aim to ensure equitable access to AI’s benefits and support workforce adaptation. By fostering a balanced integration of technological advancements and human well-being, it is possible to mitigate the negative impacts while maximising the positive outcomes of AI.

A Probability-Based Model for Course Completion Prediction in Online Asynchronous Learning

The sheer scale of Massive Open Online Courses (MOOCs), presents a significant challenge in delivering personalized learning experiences and effective student support. With vast participation numbers, it becomes difficult for instructors to track individual progress, pinpoint specific areas where students struggle, and understand the underlying reasons for failure to complete the course. This lack of individualized attention can lead to disengagement and higher dropout rates. A probability-based analysis offers a solution by generating student-specific predictions about their likelihood of completion. This empowers educators to proactively identify those at risk, tailor interventions, optimize resource allocation, and potentially improve the overall learning experience within the MOOC environment. This study focuses on developing a robust predictive framework to accurately estimate the probability of students completing an introductory programming course offered on the Open Learning Platform of the University of Moratuwa, Sri Lanka. The approach began with a classification model to determine the likelihood of course completion. Building upon this, a regression model was developed to generate a specific probability percentage representing the chance of a student successfully completing the course within a designated time frame. Initial findings suggested that predictions from the classification model achieved the highest accuracy when students have completed approximately 42.8% of the course materials. It is anticipated that further refinements to the methodology will improve the reliability of the predictions. A crucial aspect of this research involves determining the optimal percentage of course progress needed to yield reliable probability predictions. This is investigated through systematic analysis and experimentations including incremental model testing. The dataset encompasses diverse demographic and educational variables, enabling the identification of influential factors affecting course completion. This study provides insights on developing personalized learning strategies, intervention tactics and academic planning within online asynchronous education.

Pathways to Enhance New Quality Productivity in New Liberal Arts Education Through Artificial Intelligence

This paper explores the pathways through which artificial intelligence (AI) enhances new quality productivity in new liberal arts education. By analyzing the role of AI in personalized learning, interdisciplinary integration, and the application of virtual reality/augmented reality technologies, it reveals how AI technology promotes the development of students’ innovative capabilities and productivity in the context of new liberal arts education. The study shows that AI is not only a technical tool but also a driving force for transforming educational models and fostering knowledge innovation. Further exploration of the deep integration of AI and new liberal arts education is necessary to promote comprehensive social progress.

Can ChatGPT Revolutionize Language Learning? Unveiling the Power of AI in Multilingual Education Through User Insights and Pedagogical Impact

ABSTRACTIn the era of accelerating globalization, the necessity for multilingual education is increasingly prominent. This study investigates the effectiveness of the AI‐based chatbot ChatGPT in multilingual teaching applications. This study employs a quasi‐experimental research methodology to examine the experiences of 100 international students at a university in Western China. The investigation delves into multiple dimensions of ChatGPT's effectiveness, including user interface design, operational experience, educational interaction, student engagement, personalized learning recommendations, and the enhancement of cultural understanding. The findings indicate that ChatGPT demonstrates significant potential in multilingual teaching, particularly in providing personalized learning support and facilitating cultural comprehension. However, improvements are needed in aspects of user interface friendliness, interaction naturalness, and depth. This research not only provides empirical support for the use of ChatGPT in multilingual education but also introduces a fresh perspective on integrating AI technology into educational practices, thereby advancing its role in multilingual teaching.

Exploring the Perception of the Impact of Personal Learning Environments (PLEs) in the Generative AI Era in Engineering Education

Objective: This study explores students' perceptions of using Personal Learning Environments (PLEs) in the era of generative artificial intelligence as a learning resource in higher education. The specific objectives are: 1) to determine the current tools that students incorporate into their PLEs and 2) to identify students' perceptions of the use of PLEs for lifelong learning. Theoretical Framework: UNESCO and other institutions recognize lifelong learning as an essential competency for personal and professional development. Personal Learning Environments (PLEs) are considered effective tools for promoting self-directed learning, allowing students to manage their learning processes, especially with the advent of generative artificial intelligence technologies. Method: A qualitative study was conducted with 207 first-semester engineering students from a public university in Peru. Participants were introduced to a training program to develop their PLEs using various digital tools, including generative AI tools such as ChatGPT DALL-E. The instrument used to evaluate students' perceptions was the MUSIC model (Jones, 2009), which focuses on empowerment and usefulness. Results: The results indicate that more than 80% of the students positively perceive PLEs, considering them valuable and empowering tools for achieving their learning objectives. Integrating generative AI tools into PLEs is notable, as it provides a dynamic and personalized learning experience. Most students found PLEs helpful for their future and relevant for promoting lifelong learning skills. Research Implications: The research suggests that PLEs, especially when integrated with generative AI tools, positively impact self-directed learning and promote collaborative and social learning. These findings can inform the development of educational programs and policies that encourage using PLEs as a student-centered learning strategy. Originality/Value: This study contributes to the understanding of the role of PLEs in the context of higher education and generative artificial intelligence. It is one of the first studies to examine students' perceptions of using PLEs with generative AI tools, highlighting their potential to enhance learning autonomy and personalization.

Digital Echoes: Crafting Chinese EFL Teacher Identity in the Era of AI-Enhanced Instruction—A Qualitative Exploration

This study examines the impact of artificial intelligence (AI) integration on the professional identities of Chinese tertiary English as a Foreign Language (EFL) teachers. It explores how these educators perceive and adapt to the incorporation of AI tools into their teaching practices, focusing on both the opportunities and challenges encountered. Through qualitative analysis of semi-structured interviews with 16 tertiary EFL teachers, the study identifies key factors influencing their professional identities, including institutional support, professional development, peer influence, personal attitudes towards technology, and ethical considerations. The findings indicate that AI integration enhances personalized learning and reduces the burden of administrative tasks. However, it also challenges teachers to reshape their roles and manage both technological and ethical complexities effectively. The study highlights the necessity for ongoing professional development and robust institutional support to effectively integrate AI in educational settings, ensuring it enhances rather than undermines the teaching and learning process.

Towards Responsible Use: Student Perspectives on ChatGPT in Higher Education

The advent of generative artificial intelligence (AI) tools, particularly Large Language Models (LLMs) like ChatGPT, presents a dichotomy of opportunities and challenges within the educational sector. These tools offer the promise of personalized learning support, accessible at any time, thereby enhancing educational engagement and resource availability. However, they also introduce significant concerns, notably the risk of plagiarism, complicating their integration into academic settings. Despite the widespread adoption of ChatGPT by students, there remains a gap in understanding their experiences with these tools and the need for frameworks to guide responsible use. To address this knowledge gap, this study investigates the utilisation of ChatGPT for academic tasks and students' perceptions of its benefits and drawbacks in a university context. The findings reveal that half of the students used ChatGPT either regularly or occasionally for academic tasks. However, only slightly more than one-third of the participants expressed confidence in maintaining the authenticity and originality of their outputs when using ChatGPT. In terms of utility, respondents' perceptions varied, with 25.0% considering it moderately useful, 39.3% very useful, and 10.7% extremely useful for their assignments. Notably, 42.8% observed a significant enhancement in the quality of their work with ChatGPT, compared to assignments completed independently. Despite these advantages, 71.4% of students encountered errors or inaccuracies in ChatGPT's outputs, underscoring the tool's limitations. A significant majority (89.7%) advocate for the establishment of specific guidelines or training to foster effective and responsible usage of ChatGPT in academic contexts. The Chi-square test indicated a significant difference in ChatGPT usage between White and BAME students (p=0.031). These findings underscore the broad utilisation of ChatGPT in student learning and general satisfaction with its application. Nonetheless, students acknowledge the tool's potential for errors and inaccuracies. In light of these challenges, there is a pronounced demand for clear guidelines and proper training to navigate the responsible use of ChatGPT in educational settings. This study illuminates the imperative for educators and institutions to develop strategies that not only leverage the benefits of generative AI tools but also mitigate their risks and promote meaningful learning outcomes.

Goal-Setting, Active Practice, Self-Monitoring: a Web-Based System to Improve Programming Proficiency

Mastering programming fundamentals poses a significant challenge for students, especially in demanding higher education environments. This pilot study investigates the effectiveness of a web-based system, SoftwareSkills, designed to enhance programming proficiency among second-year bachelor students. SoftwareSkills leverages, active practice, and self-monitoring to support programming practice. Thirty-six students (n=36) participated in a quasi-experimental design, divided into independent exploration and instructional session groups. User interaction data, exercise performance, and behavioural patterns were analysed. The study revealed four distinct learning strategies: reviewing skill masteries, dedicating sufficient time to each question, striving for mastery, and adapting spaced practice. However, some students exhibited rapid responses and minimal practice, suggesting a need for deeper learning strategies. These findings highlight the suitability of self-regulated learning and deliberate practice in programming education. Future work will involve first and second-year programming students and integrate AI-driven personalised learning features to optimise the learning experience. This research contributes insights into effective practices and the potential of technology-aided learning to support student success in programming education.

Pedagogical Interaction and Student Engagement at Selected University

This quantitative study examined pedagogical interaction and student engagement in a university in Guangdong, China, involving 364 students and 132 teachers. Surveys evaluated pedagogical strategies, including reflective, inquiry-based, and collaborative learning, alongside cognitive, emotional, and behavioral aspects of student engagement. Professional development was measured by the establishment of training systems aimed at enhancing students' capacity to engage in classroom interactions, ensuring that teachers progressively cultivate active participation.The findings show that teachers frequently facilitate teamwork, reflect on their teaching practices, and set clear expectations for group activities. They also promote inquiry-based learning by fostering interactive environments where students exchange ideas and engage in discussions. In problem-based learning settings, teachers guide students through research, data analysis, and presentations. However, Spearman’s rho correlation analysis found no significant relationship between teaching strategies and student engagement, suggesting that changes in one do not necessarily affect the other. This indicates that factors beyond pedagogy, such as student motivation or external influences, may impact engagement. The study highlights the need for more personalized learning approaches and enhanced support systems to encourage participation. And, continuous professional development remains crucial, ensuring educators can create environments that nurture meaningful student engagement, regardless of instructional strategies used.

Enhancing Higher Education in Portugal: Leveraging Generative Artificial Intelligence for Learning-Teaching Process

Recent advancements in Generative Artificial Intelligence (GAI) have revolutionized numerous fields, including higher education. This study focuses on the integration of GAI in Portuguese higher education institutions and explores its multifaceted implications and potential. Our research, conducted through a comprehensive survey between April and June 2023, engages with higher education faculty to understand their perceptions and utilization of GAI tools in teaching and learning processes. The potential of GAI to enhance personalized learning and interactive teaching methodologies is significant. It enables the creation of customized educational content, interactive simulations, and real-time feedback mechanisms, thus reducing the educators' workload and enhancing the learning experience. However, this integration is not without challenges. The study identifies critical ethical concerns around the use of GAI, including data privacy, intellectual property, and the potential for bias in AI-generated content. Additionally, our findings indicate varying acceptance and readiness among educators to adopt these technologies. While some express enthusiasm for the potential of GAI to transform educational practices, others remain cautious, highlighting the need for comprehensive training and support to leverage GAI capabilities fully. This paper captures the essence of the study's findings, illustrating both the promising opportunities and the complex challenges associated with using Generative AI in higher education. Higher education institutions can significantly enhance teaching and learning landscapes by addressing these challenges and fostering an informed and ethical approach to GAI integration.

Transforming higher education for the digital age

In this study, I explore the transformative potential of artificial intelligence (AI) and emerging technologies in higher education, focusing on case studies and pedagogical innovations that are reshaping the learning experience. Through an in-depth analysis of key initiatives—such as Stanford University's AI-driven personalized learning platform, the AI chatbot implemented at the University of Murcia, Knewton's adaptive learning system, and the intelligent tutoring platform developed by Pai et al.—the study highlights how AI enhances learner engagement, customizes educational experiences, and improves academic outcomes. The research also critically examines the ethical challenges and policy considerations associated with AI integration in educational settings. It emphasizes the need for clear guidelines to ensure responsible and equitable use of AI, particularly in addressing issues of fairness, student welfare, and access. The paper concludes by calling for further research into the long-term implications of AI on educational equity and ethical standards in higher education.

Blockchain in Education: Transforming Learning, Credentialing, and Academic Data Management

Background and Aims: Blockchain technology is critical in education for transforming learning, credentialing, and academic data management by ensuring secure, immutable, and transparent handling of academic records. This technology improves the integrity of credentials, streamlines administrative processes, and promotes personalized learning, resulting in a more dependable and efficient educational environment. This review is intended for educators, administrators, policymakers, and technologists who are interested in blockchain's potential to transform the education landscape. Methodology: The methodology for this review included a systematic approach to gathering and analyzing literature on blockchain in education. A rigorous protocol was used to screen, extract, and categorize relevant data from academic databases and supplementary sources. Thematic analysis, aided by tools such as NVivo, enabled a thorough understanding of blockchain's impact and prospects in educational settings. Results: Blockchain technology has the potential to transform the educational sector by addressing critical issues such as data security, credentialing, and administrative efficiency. Blockchain, which provides a decentralized, immutable system for managing educational records and credentials, can improve the integrity and accessibility of academic information, streamline processes, and support personalized learning experiences. Although challenges such as scalability, legal complexities, and institutional resistance persist, ongoing advancements and research are likely to pave the way for wider adoption. As educational institutions investigate and implement blockchain solutions, the technology's transformative impact on the global education system may result in a more transparent, equitable, and innovative learning environment for future generations. Conclusion: blockchain technology has the potential to revolutionize education by improving data security, credentialing, and administrative efficiency through its decentralized and immutable system. Despite current challenges, its continued development and implementation could result in a more transparent, equitable, and innovative educational environment.