Learning Path Research Articles

SmartStudy-Learning Path Recommender system using DS and ML

Learning Path Recommender Systems (LPRS) utilize machine learning and data-driven approaches to personalize educational experiences, adapting content sequences based on individual learners’ needs and progress. With the rising demand for tailored learning, LPRS have emerged as vital tools to guide students through educational content, enhancing engagement and achievement. This review covers state-of-the-art algorithms, including collaborative filtering and reinforcement learning, used in LPRS to optimize learning paths. We discuss model accuracy, user interaction data, and adaptive feedback mechanisms, providing insights into the potential of LPRS to improve learning outcomes

Exploring Learning Paths: Understanding the Learning Strategies of Artificial Intelligence System Users and Their Involvement in Social Forums

In the last decade, there has been a significant increase in the use of commercial semi-autonomous vehicles by consumers. This has led to a surge in concerns among users about the limitations of these systems, especially when it comes to safety. In order to address these concerns, users often seek out diverse educational resources to comprehend these constraints, explore alternatives, and determine whether their experiences are representative. This paper examines the viewpoints of users who are using a new AI system that has received minimal training from the vendor. We conducted interviews of the users of the AI to examine the many sources from which users obtain knowledge and developed a learning technology-based framework that utilizes technology to disseminate knowledge among users of the AI.

The AI revolution in micro-credentialing: personalized learning paths

The AI revolution in micro-credentialing: personalized learning paths

Effective theory of collective deep learning

Unraveling the emergence of collective learning in systems of coupled artificial neural networks points to broader implications for physics, machine learning, neuroscience, and society. Here we introduce a minimal model of interacting deep neural nets that condenses several recent decentralized algorithms by considering a competition between two terms: the local learning dynamics in the parameters of each neural network unit, and a diffusive coupling among units that tends to homogenize the parameters of the ensemble. We derive an effective theory for linear networks to show that the coarse-grained behavior of our system is equivalent to a deformed Ginzburg-Landau model with quenched disorder. This framework predicts depth-dependent disorder-order-disorder phase transitions in the parameters' solutions that reveal a depth-delayed onset of a collective learning phase and a low-rank microscopic learning path. We validate the theory in coupled ensembles of realistic neural networks trained on MNIST and CIFAR-10 datasets under privacy constraints. Interestingly, experiments confirm that individual networks–trained on private data–can fully generalize to unseen data classes when the collective learning phase emerges. Our work elucidates the physics of collective learning and contributes to the mechanistic interpretability of deep learning in decentralized settings. Published by the American Physical Society 2024

From Theory to Practice: Virtual Reality Technology Facilitates Efficient Transformation of Preschool Teacher Education

This paper explores the application of Virtual Reality (VR) technology in the efficient transformation of preschool teacher education, from theory to practice. The research background highlights the importance of preschool education, the crucial role of teacher training in enhancing its quality, and the potential of VR technology in education. The study aims to investigate how VR technology can facilitate the efficient transformation of preschool teacher education, analyze its specific applications and effects, and propose strategies and recommendations for teacher training based on VR technology. The literature review summarizes current research on VR technology in education, progress in preschool teacher training models, and preliminary explorations of VR in teacher training. The theoretical foundation is based on learning theories such as constructivism and situational learning, technology acceptance and use theory, and theories of educational innovation and technology integration. An analysis of the current situation in preschool teacher training reveals limitations in traditional models, such as the disconnection between theory and practice, the lack of immersive learning experiences, and inadequate personalized training. The paper then discusses the advantages of VR technology, including immersive learning, flexible teaching scenarios, and personalized learning paths. The conclusion summarizes the value of VR technology in preschool teacher training, successful experiences, and existing problems, and provides future directions for the deep integration of technology and education, continuous innovation in teacher training models, and implications for educational policy and practice.

Learning Languages in the 21st Century: the Digital and Ai Transformation

The article deals with the problem of the learning languages in the 21st century: the digital and AI transformation. The aim of this article is to explore and clarify the transformative impact of digital technologies on foreign language teaching. This article explores the evolution of language learning from classroom-based methods to the incorporation of digital instruments, Artificial Intelligence (AI), and Information and Communication Technologies (ICT). Digital tools such as language learning applications, online dictionaries, multimedia resources, and language exchange platforms have made language acquisition more accessible and engaging. AI technologies introduce intelligent tutoring systems, personalized learning experiences, and advanced language processing capabilities, while ICT tools like Learning Management Systems (LMS) and virtual classrooms facilitate effective communication and information processing. The benefits of these advancements are multifaceted, including increased accessibility and flexibility, enhanced engagement and motivation, personalized learning paths, and improved collaboration and communication. However, challenges such as the digital divide, variability in resource quality, data privacy concerns, and the need for continuous teacher training must be addressed. Looking ahead, future trends such as augmented and virtual reality, blockchain technology, AI-driven content creation, IoT integration, and multimodal learning experiences promise to further transform the landscape of language education. These innovations will pave the way for more immersive and interconnected language learning experiences, contributing to a more multilingual global society.

Challenges for being self-directed in content and language integrated learning with instructional videos

Self-directed skills are crucial for video-based learning as learners often need to manage their pace, set goals, and engage with content independently. While much research focuses on the effectiveness of design and design principles in facilitating self-directed learning (SDL) in multimedia environments, boundary conditions arise due to the highly individualized and unique nature of each learning trajectory. This paper reports the findings of a case study involving five university students who speak English as an additional language (EAL). It explores the difficulties they faced while learning mathematics with instructional videos and the approaches they used to address these challenges in the context of Content and Language Integrated Learning (CLIL). Findings from video-stimulated recall (VSR) and semi-structured interviews reveal that language issues influenced and drove many decisions in their self-directed learning paths. Notably, the challenges of grasping academic language and terminology led participants to spend considerable time and effort on navigating video designs to aid language comprehension and on seeking extra resources to better understand the lectures. However, the lack of timely feedback and expert guidance posed further challenges for these EAL learners in identifying their knowledge gaps, thereby complicating the search for resources appropriate to their language and academic levels.

AI-Teacher Teaching Task Spectrum in Action

The AI-Teacher Teaching Tasks Spectrum (AITTTS) was conceived as a way to understand the relationship between human teachers and the ever-evolving AI technologies in education. This study demonstrates how the AITTTS framework can be operationalised into a tangible intervention, showcasing the design models and practical applications of the AITTTS in real-world educational settings. By categorising teaching tasks into a spectrum, the AITTTS delineates the roles that AI and human teachers can play, providing a structured and nuanced understanding of their collaboration. As a result of the practical application of the AITTTS, a design model was birthed in this study. It highlights various aspects of holistic student outcomes such as positive electronic nonverbal communication (eNVC) cues, adaptive learning paths, and interactive learning responses as elements by which AI should be designed. By providing a structured approach for educators to incorporate AI tools and interventions in their learning environments, this research lays the groundwork for further exploration of the synergistic relationship between AI and human teachers in modern education. This framework can serve as a guide for educators to develop and implement AI-enhanced teaching strategies, fostering a more dynamic and responsive educational landscape.

Learning Analytics Based on Big Data: Student Behavior Prediction and Personalized Educational Strategy Formulation

Abstract: Learning analytics have become a game-changer in education by using big data to analyse student behaviours, predict student outcomes and provide personalised interventions. This paper outlines the main components in learning analytics including data collection, predictive modelling and personalised educational strategies. It demonstrates how predictive models can be used to identify at-risk students and why real-time feedback can keep students engaged and motivated. Two case studies and examples of the data are used to illustrate how institutions can shift from reactive to proactive mode using learning analytics to track engagement, performance, and personalise the learning path. The study also shows that learning technologies are becoming adaptive to personalise learning experience, which results in a more learner-centric approach to education catering for the individual needs of students. Overall, the study demonstrates the role of learning analytics in creating a data-driven environment, which improves the student learning success and retention by addressing the challenges ahead of time.

Hybrid and Blended Learning Models: Innovations, Challenges, and Future Directions in Education

The rise of hybrid and blended learning models has transformed educational practices, combining the strengths of in-person and online learning. This review explores recent innovations, challenges, and future directions in these models. Technological advancements such as artificial intelligence (AI), adaptive learning platforms, and virtual reality (VR) are reshaping the delivery of hybrid education by offering personalized learning experiences, automating assessments, and creating interactive simulations. Pedagogical shifts, including flipped classrooms and competency-based education, are becoming central to hybrid learning environments, emphasizing student-centered approaches and maximizing active engagement. The integration of microlearning and modular course design further enhances flexibility, catering to diverse learning styles and paces. Despite these innovations, significant challenges remain. Issues of equity and access persist, with underprivileged students facing barriers due to lack of internet connectivity and digital resources. Additionally, instructors face increased workloads and must adapt to new technologies, often requiring further training to manage hybrid classrooms effectively. Maintaining student engagement, particularly in asynchronous settings, also presents difficulties. The review highlights best practices, such as the use of interactive learning strategies, continuous assessment, and offering flexible learning paths to support student success in hybrid settings. Looking forward, the future of hybrid and blended learning points towards greater personalization through AI, enhanced sustainability by reducing the reliance on physical infrastructure, and the integration of immersive technologies like augmented and virtual reality. These advancements hold the potential to revolutionize hybrid learning, making education more accessible, engaging, and adaptable to a variety of learning environments.

Harnessing Artificial Intelligence for Personalized Learning Pathways: A Framework for Adaptive Education Management Systems

Abstract. The integration of artificial intelligence (AI) into education is revolutionising how we deliver personalised learning, where curriculum, pedagogy and assessments are dynamically adjusted to meet the demands of each learner. In this paper, we examine the role AI can play in the construction of personalised learning pathways, and explore its applications for K-12 education, higher education and lifelong learning. We highlight that AI-driven systems such as Summit Learning, Pounce at Georgia State University, and IBMs Watson Talent are helping to measure dramatic improvements in student and employee outcomes, ranging from retention rates, academic performance, to engagement levels. We show how AI can enable student profiling, adaptive learning path generation, and ongoing assessment via the continuous feedback loops that are critical to transformative personalised learning. We also highlight some of the key challenges related to how education (eg, data privacy, scalability and equity), and showcase case studies and data from AI applications in different educational contexts to demonstrate the potential of personalised learning enabled by AI. In summary, this paper underscores the benefits and challenges of implementing AI in education, providing a perspective on how AI is likely to shape the future of learning.

Revolutionizing education with AI: The adaptive cognitive enhancement model (ACEM) for personalized cognitive development

Abstract. The integration of artificial intelligence (AI) into education has opened doors to personalized learning experiences. This paper introduces the Adaptive Cognitive Enhancement Model (ACEM), a cutting-edge AI-driven framework designed to personalize cognitive development for students. Leveraging advanced machine learning algorithms and quantitative analysis, ACEM adapts educational content and learning strategies to individual cognitive needs. The model encompasses five key components: cognitive profiling, adaptive learning paths, intelligent feedback, motivational strategies, and longitudinal tracking. Through quantitative analysis and mathematical modeling, the paper demonstrates how ACEM significantly enhances learning outcomes compared to traditional education models. The discussion section provides a detailed exploration of each model component, its architecture, and its role in optimizing personalized cognitive development. Furthermore, challenges such as data privacy, scalability, and model interpretability are examined, alongside potential solutions. The conclusion underscores the transformative potential of ACEM in revolutionizing education.

Data Mining Methods in Educational Process Management

The paper addresses the challenge of effectively managing the educational process by leveraging intelligent data analysis of student performance during learning activities. It introduces an approach centered around data clustering, specifically applied to the study of programming disciplines and languages. By utilizing clustering techniques, the paper aims to identify the most challenging topics within a given academic subject, track students’ learning paths, evaluate and enhance teaching methodologies, and create personalized learning plans tailored to individual students’ needs. This approach enables educators to better understand and address the diverse learning requirements of students, ultimately enhancing the overall educational experience.

Research on Personalized Cultural Learning Platform Based on Collaborative Filtering and Popularity Recommendation

With the rapid progress of information technology, it has become the demand of the times to use the power of the Internet to inherit and develop Chinese traditional culture. Given the challenge of users searching for learning content in massive online resources, personalized recommendation systems have become the key to improving user experience. This paper focuses on building a personalized Chinese traditional culture learning platform based on collaborative filtering and popularity recommendation, aiming to stimulate users’ interest in learning traditional culture through intelligent algorithms. In terms of technical implementation, this study relies on the Django framework and MySQL database to build a feature-rich and user-friendly traditional cultural learning platform, supporting users’ diverse learning needs, and providing flexible learning paths and abundant learning resources. Experimental evaluation shows that the hybrid algorithm of collaborative filtering and popularity recommendation proposed in this paper has improved recommendation performance compared to existing methods, effectively enhancing user satisfaction and platform activity. This platform not only promotes the digital dissemination of traditional culture, but also provides users with a highly autonomous and personalized learning experience, meeting the contemporary society’s pursuit of diversified and personalized cultural learning.

PREDICTING STUDENT PERFORMANCE IN ONLINE LEARNING PLATFORMS: ANALYZING ENGAGEMENT METRICS WITH MACHINE LEARNING MODELS

The emergence of online learning platforms harbours both opportunities and challenges, this paper focuses on machine learning algorithms for deep diving into key predictors of students success in online environments, captured by engagement metrics such as quiz scores, participation in forums, and time spent on learning tasks. Data was collected through a survey of 50 students and then run through the Random Forest model. Major predictors of success include the assignment approach, quiz score, and motivation. The prediction from this model can be interesting because it can pick important features, but the overall accuracy is quite low at about 20%, which implies that larger datasets and more features might be necessary to enhance the predictions. Time management, motivation, and staying in touch are such variables that the research holds key to determining results for students. The results are important not only for online learning platforms but also for interventions suggested, which include early warning systems, personalised learning paths, and elements of gamification to support performance for improved student support at the right time for those most at risk. Future studies should consider more sophisticated machine learning models and the use of more objective performance data in their attempt to make more refined predictions.

AI and VR Integration in E-Learning: Designing meaningful learning environments

The main idea of the educational process based on artificial intelligence and virtual reality forms a new, effective methodology of training. This article examines how AI facilitates VR environments and enriches them with immediate feedback, learner-generated learning paths and big data, resulting in personalizing the education requirements for a spectrum of learners. The literature review addresses the subject through different areas where AI and VR have been applied in a timely manner to learning and comprehension, as well in fields such as engineering, healthcare, and humanities. Also, the advantages of this integration, including enhanced activity, access to the material, and potential to get practical training, are outlined. But, it is imperative to also consider the implementation costs, technology enforcement issues, as well as the probable data privacy problems. Direction and opportunity are explored, along with such areas as further development of adaptive learning environments, more realistic VR settings, and challenges of data utilization in learning. Finally, this article underlines the valuable possibility of the AI and VR applying in the educational field and the necessity of overcoming the barriers to the educational standards.

Las prácticas preprofesionales en la formación psicopedagógica ¿Revisión, supervisión o covisión?

This paper arises from a research that aims to build knowledge about learning experiences, paths, and psychopedagogical interventions in pre-professional training practices. The strategy to approach the study is the open qualitative interview, administered to students and graduates, to analyze a category constructed from the repeated reading of the field records: Pre-professional practices in psychopedagogical training, with emphasis on the sub-axis referring to the accompaniment and the possibilities of reviewing these practices. Focusing on practices means addressing complex events that confront students with decisions characterized by the uniqueness of their journeys. Follow-up and accompaniment require a space to listen to obstacles, advances, and setbacks, failed and accurate interventions: the review -supervision or co-vision- of the psychopedagogical practices that are carried out individually or in groups in the context of institutions.

Optimizing cognitive load and learning adaptability with adaptive microlearning for in-service personnel

Adaptive microlearning has emerged as a crucial approach for enhancing the working skills of in-service personnel. This study introduces the design and development of an innovative adaptive microlearning (AML) system and investigates its effectiveness compared to a conventional microlearning (CML) system. The main distinguishing feature of an AML system from a CML system is its adaptive features that tailor the learning experience to individual needs, including personalized content delivery, real-time feedback, and adaptive learning paths. A quasi-experimental study involving 111 in-service personnel (NAML = 56, NCML = 55) was conducted. ANCOVA results confirmed that the AML system significantly reduced unnecessary cognitive load due to inappropriate instructional design (mean difference of -20.02, p < 0.05) and significantly improved learning adaptability (mean difference of 40.72, p < 0.05). These findings highlight the potential of adaptive microlearning systems to overcome barriers to effective learning, thereby supporting lifelong learning and professional development in various working contexts.

Assessing the Quality of an Innovative Learning Path for BIM Education: The DIGITAL DECATHLON

The Erasmus+ funded project DIGITAL DECATHLON addresses the urgent need to modernize educational practices in the construction industry, especially in the field of Building Information Modeling (BIM). This study investigates how interdisciplinary, international and game-based learning can improve the BIM skills of students from different academic backgrounds and institutions in Europe. The study investigates whether an immersive simulation game involving students from five universities in Germany, Finland, Italy and Poland can effectively develop BIM skills and promote international and intercultural collaboration. The research uses a mixed methods approach that includes observational analysis, expert evaluations and surveys to assess the effectiveness of learning methods used in the game, to exploit for the optimization of the second game experience foreseen by the project. The results show that the simulation successfully engages students and improves their practical BIM skills. Nevertheless, there are areas that need improvement. Key challenges include harmonizing disciplines, improving the understanding of project management and ensuring consistent and equal communication between teams. These findings emerge from both qualitative feedback and quantitative performance data. This makes it clear that the existing simulation game is well suited to teaching the basics of the BIM methodology, but that some improvements are still needed, which will be incorporated in a second run, again subject to evaluation for capitalization and future replication.

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.