Personalized Learning Path Research Articles

SUCCESS TRACK: Website for New Students with Personalized Skill Development

The research is designed to help new students entering campus by offering them skill development opportunities right from the start of their academic journey. The research provides a platform where students can choose a specific skill domain to improve upon. It also offers a personalized learning paths for them, packed with various resources. Additionally, a continuous assessment through tests, AI chat bot support, personalized student logins, and faculty admin access for efficient management is also offered. The goal of the research is to support students in enhancing their skills and succeeding academically.

Utilization of artificial intelligence in Outcome-Based Curriculum Evaluation and Development

The integration of artificial intelligence (AI) in education has revolutionized traditional teaching and learning methodologies, offering significant improvements in curriculum evaluation and development. This study explores the utilization of AI in the evaluation and development of an Outcome-Based Curriculum (OBC). AI technologies, including machine learning algorithms and natural language processing, are employed to analyze vast amounts of educational data, providing insights into student performance and curriculum effectiveness. The research highlights how AI can identify learning gaps, predict student outcomes, and recommend personalized learning paths, thereby enhancing the overall educational experience. By leveraging AI, educators can design more adaptive and responsive curricula that meet the dynamic needs of students and industry standards. The findings suggest that AI-driven tools not only streamline the evaluation process but also facilitate continuous curriculum improvement, ensuring that educational programs remain relevant and outcome-focused. This paper underscores the potential of AI to transform curriculum development practices, promoting a more efficient, data-driven approach to education that aligns with contemporary educational goals and outcomes.

The Role of Artificial Intelligence in Learning and Development

Artificial Intelligence (AI) is transforming corporate learning and development by offering personalized, efficient, and scalable training solutions. This paper explores how AI technologies, such as machine learning, natural language processing, and data analytics, enhance learning experiences and outcomes in corporate environments. Key benefits include personalized learning paths, automated content creation, and real-time feedback. Challenges such as data privacy, implementation costs, and the need for human oversight are also discussed. By examining current applications and future trends, this paper highlights AI’s potential to revolutionize corporate training and suggests best practices for integrating AI-driven solutions in learning and development programs. Key Words: Artificial Intelligence (AI), Corporate Learning and Development, Personalized Learning, Real-Time Feedback, Adaptive Learning Technologies, Data Privacy, Emotional Intelligence in AI, AI and Augmented Reality (AR), Social Learning. Predictive Analytics, Human Oversight

Image analysis and teaching strategy optimization of folk dance training based on the deep neural network

To improve the recognition effect of the folk dance image recognition model and put forward new suggestions for teachers’ teaching strategies, this study introduces a Deep Neural Network (DNN) to optimize the folk dance training image recognition model. Moreover, a corresponding teaching strategy optimization scheme is proposed according to the experimental results. Firstly, the image preprocessing and feature extraction of DNN are optimized. Secondly, classification and target detection models are established to analyze the folk dance training images, and the C-dance dataset is used for experiments. Finally, the results are compared with those of the Naive Bayes classifier, K-nearest neighbor, decision tree classifier, support vector machine, and logistic regression models. The results of this study provide new suggestions for teaching strategies. The research results indicate that the optimized classification model shows a significant improvement in classification accuracy across various aspects such as action complexity, dance types, movement speed, dance styles, body dynamics, and rhythm. The accuracy, precision, recall, and F1 scores have increased by approximately 14.7, 11.8, 13.2, and 17.4%, respectively. In the study of factors such as different training images, changes in perspective, lighting conditions, and noise interference, the optimized model demonstrates a substantial enhancement in recognition accuracy and robustness. These findings suggest that, compared to traditional models, the optimized model performs better in identifying various dances and movements, enhancing the accuracy and stability of classification. Based on the experimental results, strategies for optimizing the real-time feedback and assessment mechanism in folk dance teaching, as well as the design of personalized learning paths, are proposed. Therefore, this study holds the potential to be applied in the field of folk dance, promoting the development and innovation of folk dance education.

ProQuiz

ProQuiz is an app designed for educational purposes, specifically to help users prepare for multiple-choice exams, especially in the field of computer science. The app aims to make studying enjoyable by incorporating elements of gamification. The primary objective of ProQuiz is to assist users in getting ready for subjective tests used in admissions and recruitment processes, with a focus on the computer science domain. The app is organized into three main modules: computer science, reasoning, and analytical skills. Each of these modules covers various sub-categories to address different subjects. During the quiz, users have access to helpful features such as hints, the ability to skip questions, and a pause/life-lines option. These features can be used only once per user. The app also provides feedback on progress throughout the quiz, and upon completion, it displays the user's results. The overall aim is to provide users with an engaging platform for practicing and enhancing their knowledge in a variety of subjects related to computer science. This research paper examines the development and effectiveness of ProQuiz. Through user testing and analysis, we evaluate its usability and engagement. Additionally, we propose enhancements, including personalized learning paths and social features. This paper contributes insights into innovative educational technology and suggests avenues for future improvement of ProQuiz.

An adaptable and personalized framework for top-N course recommendations in online learning

In recent years, the proliferation of Massive Open Online Courses (MOOC) platforms on a global scale has been remarkable. Learners can now meet their learning demands with the help of MOOC. However, learners might not understand the course material well if they have access to a lot of information due to their inadequate expertise and cognitive ability. Personalized Recommender Systems (RSs), a cutting-edge technology, can assist in addressing this issue. It greatly increases resource acquisition through personalized availability for various people of all ages. Intelligent learning methods, such as machine learning and Reinforcement Learning (RL) can be used in RS challenges. However, machine learning needs supervised data and classical RL is not suitable for multi-task recommendations in online learning platforms. To address these challenges, the proposed framework integrates a Deep Reinforcement Learning (DRL) and multi-agent approach. This adaptive system personalizes the learning experience by considering key factors such as learner sentiments, learning style, preferences, competency, and adaptive difficulty levels. We formulate the interactive RS problem using a DRL-based Actor-Critic model named DRR, treating recommendations as a sequential decision-making process. The DRR enables the system to provide top-N course recommendations and personalized learning paths, enriching the student's experience. Extensive experiments on a MOOC dataset such as the 100 K Coursera course review validate the proposed DRR model, demonstrating its superiority over baseline models in major evaluation metrics for long-term recommendations. The outcomes of this research contribute to the field of e-learning technology, guiding the design and implementation of course RSs, to facilitate personalized and relevant recommendations for online learning students.

Pedagogical support of technical college students during professional training

Relevance. The training of future specialists, especially in technical fields, is highly relevant today. This priority for pedagogical research involves analysing current methods and effective approaches for supporting future specialists. The urgency is heightened by socio-political changes and the informatisation of the educational environment. Purpose. This study aims to examine the features of pedagogical support during the educational and professional training of students at a technical college. Methodology. The study employs various scientific methodological tools, including functional and dialectical approaches, and methods of analysis, synthesis, comparison, and generalisation. Results. The study explores the concept of pedagogical support, detailing its theoretical characteristics and practical application. Based on an analysis of Kostanay Polytechnic Higher College, identifies key methods such as personalized learning paths, integration of technology, and continuous assessment. These methods address the specific needs of students, enhancing their professional competence and readiness for the workforce. Conclusions. The research confirms the effectiveness of pedagogical support in technical colleges, showing that well-implemented support significantly enhances professional training quality. The findings offer insights for developing programmes to improve educational outcomes through individualized learning strategies and technology use. The complexity of the subject suggests ongoing research is needed, especially in optimizing individual educational trajectories to further refine training processes and outcomes in technical education. Keywords: professional establishment; individual trajectory; educational process; development

The Human Side of Digital Technology: Supporting the Inclusion of Refugees in Higher Education Through Blockchain-backed EQPR

The spectrum of digital technologies that impact Higher Education (HE) is broad. This study explores the potential of Blockchain (BC) technology in the personalized learning path creation and data exchange in the learning processes. The flexibilization of education and digitizing student data through Blockchain may contribute to a more inclusive and sustainable HE system. According to the EU, the Blockchain supports sustainability in four main aspects: cybersecurity, accountability, transparency, and traceability. These aspects are also a driver of social impact and a higher capacity to include disadvantaged groups, such as refugees. Therefore, it is essential to start a debate between scholars and professionals about how the actors in the HE system engage in a collective meaning-making effort to sustain the adoption, diffusion, and use of BC for HE. The paper focuses on the experience of CIMEA DiploMe and EQPR for the recognition of refugees' qualifications. Through a collective consensus-making and awareness-raising effort, the blockchain-backed EQPR could be perceived as a critical tool to foster inclusion within the HEIs and enhance their social outreach.

The Current State and Future Trends of AI-assisted Higher Vocational English Education

As information technology evolves, particularly with advancements in artificial intelligence (AI), higher vocational English education is witnessing significant transformations. This paper explores the integration of AI in teaching methods and predicts future trends. AI applications, such as automated assessments and personalized learning paths, enhance teaching efficiency and personalize education. Technologies like natural language processing (NLP) evaluate students' language skills in real-time, providing instant, objective feedback which lightens educators' workloads and increases assessment precision. AI-driven tools, including language learning software like Duolingo and virtual teaching assistants, utilize voice recognition and machine learning to simulate interactive learning environments. These technologies adjust teaching content dynamically and help students practice English in realistic scenarios, thereby improving language proficiency and motivation. Future advancements in AI are expected to introduce more sophisticated algorithms for complex tasks like discourse analysis and cultural education, supported by evolving big data technologies. This will enable deeper, more precise learning analytics and content customization, further enhancing educational outcomes. The integration of AI not only enriches resources but also fosters educational model innovation, supporting vocational colleges in training globally competent professionals. As AI continues to develop, its role in shaping higher vocational English education will grow increasingly vital.

Construction and Implementation of a "Learner-Centered" Business Education Model Based on Artificial Intelligence Technology

With the rapid development of artificial intelligence (AI) technology, its application in education has attracted increasing attention. This paper aims to explore how to construct a "learner-centered" business education model based on AI technology and analyze its practicality from a theoretical perspective. The study first elaborates on the concept of "learner-centered" education through literature review and analyzes the existing problems in business education. Then, drawing on theories from education and information technology fields, this paper proposes a framework for a business education model that integrates AI technology. The study adopts logical reasoning and model construction methods to analyze the application of AI in personalized learning path design, learning process management, and learning effectiveness evaluation systematically. The paper emphasizes the role of AI in assisting teachers in understanding student needs, optimizing teaching content and methods, and improving the interactivity of teaching activities and the accuracy of learning effectiveness measurement. The conclusion suggests that an AI-based business education model can enhance personalization and adaptability in education, improve student engagement and satisfaction, while highlighting the need to address ethical and privacy concerns in AI applications. This research has important theoretical significance and practical value for guiding future innovations in business education models.

Mobile Learning: Enhancing Self-Directed Education through Technology, Wireless Networks, and the Internet Anytime, Anywhere

Background and Aim: Mobile learning is critical because it uses technology, wireless networks, and the internet to provide educational access anytime and anywhere, allowing students to direct their education. This flexibility promotes personalized learning experiences and aids in the seamless integration of education into daily life, making learning more adaptable and accessible. Thus, this paper aims to investigate mobile learning for enhancing Self-Directed Education through Technology, Wireless Networks, and the Internet Anytime, Anywhere. Materials and Methods: The paper used secondary sources and a structured literature search to examine recent advances in mobile learning. It identifies key themes and trends to shed light on mobile learning's current impact and future potential in education. Results: The study discovered that mobile learning revolutionized education through its flexibility and personalization, allowing students to access content on demand and tailor their studies to their specific needs. While challenges such as the digital divide persisted, mobile learning's transformative benefits in increasing engagement and expanding access to global resources were significant. This approach encouraged self-directed, lifelong learning, which was critical for adapting to the changing educational landscape. Conclusion: The study revealed that mobile learning fundamentally reshaped education by providing students with flexible, on-demand access and personalized learning paths. Despite ongoing challenges such as the digital divide, the approach's key benefits—such as increased engagement and greater access to resources—promoted a self-directed and lifelong learning mindset, which was critical for adapting to the changing educational environment.

Application of Interdisciplinary Knowledge Integration in English Blended Education

Blended education in English combines traditional face-to-face teaching methods with online learning components, offering students a dynamic and flexible educational experience. It integrates technology into classroom instruction, providing access to digital resources, interactive learning platforms, and multimedia content. This approach allows for personalized learning experiences tailored to individual student needs while promoting collaboration, critical thinking, and digital literacy skills. Blended education in English fosters a conducive learning environment that combines the benefits of both traditional and online learning, enhancing student engagement and outcomes in English language acquisition and comprehension. This paper explores the potential of Cooperative Optimized Blended Education (COBE) to enhance interdisciplinary knowledge integration within English-blended education settings. Drawing upon theoretical frameworks such as game theory and optimization strategies, alongside empirical analyses and simulation results, the study elucidates the effectiveness of COBE in fostering collaborative learning outcomes. Through the utilization of digital resources, personalized learning paths, collaborative projects, blended assessment methods, and teacher training programs, COBE offers a dynamic and engaging approach to pedagogical innovation. Empirical findings and student performance data underscore the significance of COBE in promoting student engagement, knowledge retention, and interdisciplinary understanding. Furthermore, the paper highlights the importance of continuous monitoring and assessment to support student academic achievement within COBE. OBE resulted in a mean increase of 10% in student engagement, a 15% improvement in knowledge retention, and a 20% enhancement in interdisciplinary understanding. Furthermore, the paper emphasizes continuous monitoring and assessment to support student academic achievement within COBE.

Personalized Learning Path Recommendation Algorithm for English Listening Learning

A personalized learning path recommendation algorithm for English listening learning leverages data on users' proficiency levels, learning preferences, and past performance to suggest tailored learning paths. By incorporating natural language processing (NLP) techniques, the algorithm can analyze audio content, transcripts, and user interactions to assess comprehension and identify areas for improvement. It then recommends a sequence of listening exercises, podcasts, audiobooks, or other resources matched to the user's skill level and interests. This paper introduces the Ranked Path Recommendation (RPR) algorithm, designed to facilitate personalized English listening learning. Leveraging data analytics and machine learning techniques, the RPR algorithm aims to provide tailored recommendations of listening materials based on individual learners' preferences, proficiency levels, and learning objectives. Through a series of experiments and analyses, the effectiveness of the algorithm is evaluated, considering factors such as recommendation accuracy, learner satisfaction, and adaptability. Results demonstrate the algorithm's ability to curate diverse and relevant listening materials, enhancing learner engagement and comprehension. However, challenges such as algorithmic biases and the need for ongoing refinement are acknowledged. Ultimately, the RPR algorithm represents a promising approach to adaptive learning in language education, contributing to the advancement of personalized and effective language learning experiences. Results demonstrate that the RPR algorithm achieved recommendation accuracy ranging from 85% to 92% across ten different scenarios, with corresponding learner satisfaction ratings ranging from 6.9 to 8.8 on a scale of 1 to 10. Learner feedback indicates that recommended materials were perceived as relevant, engaging, and diverse, contributing to enhanced comprehension and motivation.

A Comprehensive Study to Build Immersive Virtual Reality-Powered Language Learning

In the rapidly evolving landscape of education technology, this project explores the integration of Immersive Virtual Reality (IVR) to revolutionize language learning experiences. Leveraging the power of virtual reality, this innovative approach aims to provide a dynamic and immersive language acquisition platform that transcends traditional methods. The project focuses on creating a virtual environment where language learners can engage with authentic scenarios, fostering practical language application and cultural understanding. Users will navigate through lifelike settings, interact with virtual native speakers, and participate in real-world conversations, thus enhancing their linguistic proficiency in a contextualized manner. Key features include personalized learning paths, adaptive challenges, and gamified elements to sustain user motivation and accommodate diverse learning styles. The incorporation of artificial intelligence ensures tailored feedback, tracking individual progress, and dynamically adjusting the learning curriculum to suit each user's proficiency level. The virtual reality language learning experience is designed to be accessible and user-friendly, catering to a broad demographic. Emphasis is placed on creating a sense of presence and cultural immersion, allowing learners to develop language skills in an environment that mirrors real-life scenarios. Through a combination of cutting-edge virtual reality technology, linguistic expertise, and pedagogical principles, this project aspires to redefine language education. The research and development aim to assess the efficacy of this immersive approach, comparing learning outcomes with traditional methods, with a vision to contribute to the ongoing discourse on the future of education technology and language acquisition.

Study Notion: Edtech Web Application using MERN Stack

One of the pivotal roles of technology in education lies in its capacity to equalize opportunities among students. Technology serves as a potent instrument for revolutionizing learning, fostering stronger connections between educators and students, reimagining learning methods and teamwork, diminishing persistent disparities in equity and accessibility, and tailoring learning experiences to cater to the requirements of every learner. Study Notion is an innovative online learning web application built using the MERN stack. It is designed to offer a comprehensive educational experience for students and educators alike. It provides a user-friendly interface, allowing seamless navigation through its diverse features, including a well-organized course management system that facilitates the creation and delivery of educational content. Study Notion fosters collaboration among users through its integrated communication tools, such as discussion forums and live chat options. The platform caters to individual learning needs by offering personalized learning paths, adaptive technologies, and tailored recommendations. It ensures a robust assessment and feedback mechanism, allowing for various forms of evaluation and timely student performance tracking. Study Notion seamlessly integrates with various third-party tools and services, enhancing its functionality and enriching the overall learning experience for its users. Key Words: edtech, study notion, MERN stack, collaboration, web application.

Supporting flexible learning paths with interactive learning resources in mathematics: lessons learned

ABSTRACT The COVID-19 pandemic caused some serious setbacks in primary and secondary education wordlwide. In 2021, the Estonian Ministry of Education and Research (MoER) contracted a group of researchers and expert teachers to design and validate of a set of innovative digital learning resources that support personalized learning paths to meet the needs of a diverse learners trying to work on their knowledge gaps caused by pandemic. This paper presents the results of designing a set of innovative open educational resources for 9th grade mathematics and piloting these in nine pilot schools. Based on the analysis of the results, we identified the main technological and pedagogical challenges in supporting flexible learning paths with interactive digital learning resources in post-COVID mathematics education.

Individual education: Competency-based personalized learning paths

Relevance. A competence-focused strategy in education necessitates the proactive involvement of learners in their professional development, aiming to build essential skills for future challenges. This research is highly relevant due to the importance of forming a competence-based approach in education and implementing personalised learning trajectories in the modern education system. Purpose. This study aims to assess the feasibility of forming an individual trajectory of education based on a competence-oriented strategy within the contemporary educational framework. It also aims to evaluate the prospects and expected effectiveness of such an approach in the realities of the current education system, which are important in its main trends. Methodology. The study involved qualitative and analytical analysis of students from the Master's program in Smart City: New Technologies at L. N. Gumilyov Eurasian National University. Results. One of the study's main outcomes is developing a theoretical model for the individualised learning route based on the competency approach. Additionally, it lays out the prerequisites for developing the unique educational trajectory that is built upon this methodology. Conclusions. The outcomes of this study are of considerable interest due to their assessment of the real prospects for introducing a competency-based approach into the methodology of forming an individual trajectory in the existing educational system. This research highlights the potential benefits of a competency-based approach in improving the quality of education by making learning more personalised and relevant to individual needs. Implementing these strategies can contribute significantly to the qualitative development of the education system, benefiting both students and educators. The study provides a foundation for future research and practical applications in educational planning and curriculum development. Keywords: education system; professional competencies; educational institution; personality formation

A Study of Artificial Intelligence-Driven Change in Higher Education in the Age of Digital Intelligence

The arrival of the age of digital intelligence signifies the increasingly important role of artificial intelligence (AI) in driving change in higher education. The incorporation of AI technology improves the efficiency of teaching and learning and facilitates fundamental changes in the mode, content, and management of education. This paper discusses the application of AI in personalized learning path design, intelligent tutoring systems, virtual labs, and course content development, and analyzes the challenges and countermeasures of technology access and equality, data privacy and security, education quality and AI dependency, and resistance to changing the traditional education system.

CourseKG: An Educational Knowledge Graph Based on Course Information for Precision Teaching

With the rapid development of advanced technologies, such as artificial intelligence and deep learning, educational informatization has entered a new era. However, the explosion of information has brought numerous challenges. Knowledge graphs, as a crucial component of artificial intelligence, can contribute to the quality of teaching. This study proposes an educational knowledge graph based on course information named CourseKG for precision teaching. Precision teaching seeks to individualize the curriculum for each learner and optimize learning efficiency. CourseKG aims to establish a correct and comprehensive curriculum knowledge system and promote personalized learning paths. CourseKG can address the issue that current general-purpose knowledge graphs are not suitable for the education field. Particularly, this study proposes a framework for educational entity recognition based on the pre-trained BERT model. This framework captures relevant information in the educational domain using the BERT model and combines it with the BiGRU and multi-head self-attention mechanism to extract multi-scale and multi-level global dependency relationships. In addition, the CRF is used for character-label decoding. Further, a relationship extraction method based on the BERT model, which integrates sentence features and educational entities and estimates the similarity between knowledge pairs using cosine similarity, is proposed. The proposed CourseKG is verified by experiments using real-world C programming course data. The experimental results demonstrate the effectiveness of CourseKG. Finally, the results show that the proposed CourseKG can significantly enhance the precision teaching quality and realize multi-directional adaptation among teachers, courses, and students.

Big Data Analytics in Higher Education: A New Adaptive Learning Analytics Model Integrating Traditional Approaches

Despite the explosion of interest in adaptive learning and learning analytics (LA) for higher education (HE), there has been relatively little research integrating educational approaches’ indicators to build an adaptive learning analytics model. Adaptive learning analytics (ALA) models have grown in favor of HE due to their claims of enhancing student learning outcomes, providing personalized learning paths, and allowing students to interact with course material at their own pace. With focus on using data to personalize the learning experience and the environment in which the experience of learning occurs, LA centers on enhancing education through meticulous data analysis, while big data (BD) in education addresses the overarching challenges and opportunities arising from extensive and varied datasets. These concepts are interconnected, where LA and ALA specifically apply BD principles within the educational context. In this paper, we explain some BD concepts used in HE, define the essential perceptions related to LA, and analyze educational approaches to define the fundamental implications. Besides, we try to connect some LA model with educational approaches based on the big educational data collected in order to establish an efficient educational model. We include all steps cited before we try to build an ALA model in HE that resolves the limitations of the oldest models, thus improving the learner’s learning process by adding and treating additional indicators.