Learning Path Research Articles

Improving Teaching Using Artificial Intelligence and Augmented Reality

With the rapid advancements in technology, the educational landscape is witnessing significant transformations in pedagogy and classroom dynamics. Two prominent technologies, Artificial Intelligence (AI) and Augmented Reality (AR), are gaining prominence in the field of education, promising to revolutionize the way teaching and learning take place. This article explores the potential benefits, challenges, and practical applications of integrating AI and AR into the teaching process to enhance student engagement and learning outcomes. The integration of AI in education brings forth personalized learning experiences. AI-powered algorithms analyze vast amounts of student data, including learning patterns, strengths, and weaknesses, to create tailored learning paths. This individualized approach helps educators identify students' unique needs and provide targeted support, ensuring that no student is left behind. Moreover, AI-based chatbots and virtual teaching assistants are increasingly being used to address student queries promptly, providing real-time support and fostering a more interactive learning environment. AR, on the other hand, enables the overlay of virtual objects and information in the real-world environment.. Students can explore complex concepts through visualizations, simulations, and interactive demonstrations, facilitating a deeper understanding of abstract topics. AR also fosters collaboration and teamwork among students, promoting active learning and peer-to-peer knowledge sharing. Combining AI and AR technologies offers a powerful synergy in the educational realm. AI can analyze AR-generated data and adapt instructional strategies in real time, responding to individual students' progress. This synergy not only enhances learning outcomes but also empowers teachers with data-driven insights, enabling them to make informed decisions about their teaching methodologies. However, successfully implementing AI and AR in education comes with its challenges. Issues related to data privacy, ethical considerations, and the need for effective teacher training in utilizing these technologies require careful attention.

Leadership development in virtual communities of practice: The case of school principals from the GCC Region

AbstractThis study explores the potential of virtual communities of practice (vCoPs) in fostering leadership growth that aligns with the diverse needs and challenges faced by school principals. The study employs a qualitative research design, and conducts semi-structured interviews with 16 school principals from K-12 schools belonging to the Gulf Cooperation Council (GCC) region. Findings suggest that vCoPs offer a rich environment for school principals to engage in collaborative learning, knowledge sharing, and professional networking, all of which contribute to their differentiated leadership development. Findings suggest that vCoPs offer flexible personalized learning paths that can support instructional, digital, and distributed leadership development of school principals. This advancement is made possible by creating a supportive and secure environment that is fortified by resilience, group cohesion, and robust feedback mechanisms. Additionally, vCoPs contribute to leadership development by fostering collective efficacy, cultivating shared beliefs in driving positive change, and disseminating these principles throughout the educational leadership community via social influence.

Smart Education: An Emerging Teaching Pedagogy for Interactive and Adaptive Learning Methods

Smart education, which incorporates technology to enhance teaching and learning, offers several advantages that can benefit both students and educators. Personalization: Smart education can adapt to individual students' needs and learning styles. It offers personalized learning paths, allowing students to progress at their own pace and receive targeted support in areas where they need it most. Smart education can be accessed from virtually anywhere with an internet connection, making learning more accessible to students regardless of their location. This is especially valuable for remote or underserved communities. Technology-enhanced learning often includes interactive elements, multimedia, and gamification, which can increase student engagement and motivation. Interactive simulations and virtual labs can make complex concepts more approachable. Smart education platforms provide immediate feedback to students, helping them understand their mistakes and guiding them toward improvement. This immediate feedback loop can accelerate the learning process. Smart education allows for flexible learning schedules. Students can access materials and complete assignments at their convenience, making it easier to balance education with work or other responsibilities. Digital resources, including videos, animations, and interactive simulations, can provide a richer and more dynamic learning experience compared to traditional textbooks. Smart education systems collect and analyze data on student performance. Educators can use this data to identify struggling students, track progress, and adjust their teaching methods accordingly.

Investigating the Impact of Machine Learning in Personalized Education Systems

Personalized education systems leverage robust capabilities of machine learning (ML). Tailoring learning experiences to individual needs preferences and abilities. This survey explores integration of ML in personalized education. It examines methodologies. It highlights applications impacts Future directions are discussed. Key ML techniques include supervised learning for performance prediction and feedback personalization. Unsupervised learning for identifying learning styles. Reinforcement learning.Personalized education systems leverage robust capabilities of machine learning (ML). Tailoring learning experiences to individual needs preferences and abilities. This survey explores integration of ML in personalized education. It examines methodologies. Highlighting applications. Impacts and future directions are discussed. Key ML techniques include supervised learning for performance prediction, feedback personalization unsupervised learning for identifying learning styles, reinforcement learning. Optimizing content sequences. Deep learning for tasks like automated grading. Intelligent tutoring included. Applications such as intelligent tutoring systems. Adaptive learning platforms. Learning analytics. Personalized learning paths and automated assessment systems discussed. The impact of ML-driven personalized education systems is profound. They enhance engagement. Motivation. Academic performance and reduce dropout rates. Challenges such as data privacy algorithmic bias, scalability and resistance to change are also addressed. Future research directions include interdisciplinary collaboration. Advancements in natural language processing are essential. Enhanced personalization algorithms are needed. Long-term studies. Considered crucial. Areas for ongoing work. Key Words: Machine learning, Personalized education, Adaptive learning, Intelligent tutoring systems, Predictive analytics, Recommendation systems, Ethical considerations, Student engagement, Educational technology

Research Paper on Geek-Grid

Geek-Grid is a transformative web application designed to bridge the gap between traditional education and the dynamic needs of modern industry. Powered by React.js and Firebase, Geek-Grid offers personalized learning, industry integrated content and intelligent job search algorithms to prepare learners for the competitive job market. This article explores the motivation behind Geek-Grid, its features, system requirements, and its impact on education and career readiness. The traditional education system often fails to keep pace with the rapidly changing demands of the modern workforce, creating a significant gap between what students learn and what employers need. Geek-Grid is a web application specifically designed to bridge this gap by integrating educational content with real-world industry requirements. Utilizing modern technologies such as React.js and Firebase, Geek-Grid offers a comprehensive learning platform that includes personalized learning paths, video lectures, informative articles, and live job alerts. It also provides recruiters with tools to search for and connect with suitable candidates, making it a dual-ended platform benefiting both students and employers. By leveraging the SERP API, Geek-Grid ensures that job listings are current and relevant, thereby enhancing the job search experience for users. This paper explores the motivation behind Geek-Grid, its innovative features, the technologies used in its development, and its potential impact on education and career readiness. Geek-Grid aims to not only enhance the learning experience but also to make students industry-ready, ultimately contributing to a more competent and prepared workforce.

Role of motivation in the path of learning: leading towards golden road of sustainability

Role of motivation in the path of learning: leading towards golden road of sustainability

Big Data Analysis and Machine Learning for Predicting Individual Differences and Learning Paths in Education

This study explores the application of big data analytics and machine learning in identifying individual learner differences and predicting learning paths within the educational sector. As education becomes increasingly digitized, personalized learning has emerged as a key strategy for enhancing educational quality. The rapid advancement of big data analytics and machine learning offers new tools and methods that provide precise identification of individual learner differences and predict learning trajectories. Employing a literature review and theoretical analysis, this study systematically examines the current status of these technologies in education. It focuses on their theoretical foundations and mechanisms for identifying learner differences and predicting learning paths. Through an in-depth analysis of relevant literature, the study summarizes the principal application scenarios and technical methods of these technologies in education and discusses their advantages and challenges in personalized learning. Findings indicate that big data analytics and machine learning can process and analyze massive volumes of educational data to precisely identify learners' styles, interests, and capability differences, thereby providing a scientific basis for personalized teaching. Moreover, these technologies can predict future learning trajectories by analyzing historical learning data, aiding educators in developing more effective teaching strategies. In conclusion, while big data analytics and machine learning hold great potential in education, they also face challenges related to data privacy and technical complexity, necessitating further research and practical exploration.

Based on the QQK Corpus — Mistake Analysis of Korean Students’ Momentum Complement “次” and Teaching Designs

Momentum complement “次” plays an important role in the daily communication of Chinese as Second language learners. This paper searches and extracts the QQK corpus of Korean students’ use of the momentum complement “次”, and analyses the corpus in terms of the four types of mistakes: omission, misordering, substitution, and misaddition. The three main reasons for the mistakes are summarised in the corpus: negative transfer of mother tongue, negative transfer of target language and the influence of learning communication strategies. Based on this, a practical teaching design is proposed to optimise the learning path of Korean students in the learning of the “次” of the momentum complement.

VARIABLE APPROACH TO FOREIGN LANGUAGE EDUCATION AT A HIGHER INSTITUTION

Variable approach to foreign language teaching aims to develop the secondary language personality of future specialists, ensures professionally oriented research, individualizes the training process and encourages students to self-train in foreign languages. It is necessary to create a diverse educational space, an individual learning path. Applying a variable approach in a higher education institution will focus on the language skills, personal and professional interests, cognitive learning styles and psychological characteristics of students, it will improve foreign language learning motivation and ensure better language training for future professionals.

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.

Understanding the Perspectives of Cambodian Provincial English Learners on Using AI Chatbots to Enhance Self-Paced Learning Paths

Understanding the Perspectives of Cambodian Provincial English Learners on Using AI Chatbots to Enhance Self-Paced Learning Paths

차세대 자격증 제도 탐색을 통한 우리나라 고교 학점 운영 및 졸업 제도의 방향성 탐색 연구

Objectives The purpose of this study is to understand the characteristics and issues of this system through the case of New Zealand, which introduces and operates NCEA as a next-generation certification system, and to present implications for Korea's high school credit management and graduation system. Methods To confirm the NCEA system's details, the contents and major documents posted on major sites, includ-ing the NZQA, New Zealand's qualification management officer, were checked. At this time, the contents were or-ganized with the purpose of NCEA, key methods, means as the core topics, and additional sub-themes were se-lected according to frequency and weight, and the contents were reorganized accordingly. Results The NCEA system pursued the purpose of establishing a successful vision named graduate profile, prac-ticing equity based on it, and allocating human resources correctly. To pursue this purpose, a flexible learning path for each student was designed, a credit completion and qualification acquisition system centered on ‘ability’ rather than ‘time’ was established, and a ‘evidence presentation/accumulation type’ and ‘future-oriented/open type’ grade recording method were introduced. However, the NCEA still exists in the NCEA, the lack of logic in the form of ROA, and the increase in pressure on literacy and numeracy remain challenges to be solved. Conclusions This study suggested that a new transcript of school records, a system reorganized to acquire com-petency-based credits in time, an evaluation system based on credits as a medium, a link between high school graduation systems and national certification systems, and a preparation for mutual recognition of high school graduation qualifications between countries.

Tracing Student Activity Patterns in E-Learning Environments: Insights into Academic Performance

In distance learning educational environments like Moodle, students interact with their tutors, their peers, and the provided educational material through various means. Due to advancements in learning analytics, students’ transitions within Moodle generate digital trace data that outline learners’ self-directed learning paths and reveal information about their academic behavior within a course. These learning paths can be depicted as sequences of transitions between various states, such as completing quizzes, submitting assignments, downloading files, and participating in forum discussions, among others. Considering that a specific learning path summarizes the students’ trajectory in a course during an academic year, we analyzed data on students’ actions extracted from Moodle logs to investigate how the distribution of user actions within different Moodle resources can impact academic achievements. Our analysis was conducted using a Markov Chain Model, whereby transition matrices were constructed to identify steady states, and eigenvectors were calculated. Correlations were explored between specific states in users’ eigenvectors and their final grades, which were used as a proxy of academic performance. Our findings offer valuable insights into the relationship between student actions, link weight vectors, and academic performance, in an attempt to optimize students’ learning paths, tutors’ guidance, and course structures in the Moodle environment.

Practice challenge recommendations in online judge using implicit rating extraction and utility sequence patterns

PurposeThe existing traditional recommendations based on content-based filtering (CBF), collaborative filtering (CF) and hybrid approaches are inadequate for recommending practice challenges in programming online judge (POJ). These systems only consider the preferences of the target users or similar users to recommend items. In the learning environment, recommender systems should consider the learning path, knowledge level and ability of the learner. Another major problem in POJ is the learners don't give ratings to practice challenges like e-commerce and video streaming portals. This purpose of the proposed approach is to overcome the abovementioned shortcomings.Design/methodology/approachTo achieve the context-aware practice challenge recommendation, the data preparation techniques including implicit rating extraction, data preprocessing to remove outliers, sequence-based learner clustering and utility sequence pattern mining approaches are used in the proposed approach. The approach ensures that the recommender system considers the knowledge level, learning path and learning goals of the learner to recommend practice challenges.FindingsExperiments on practice challenge recommendations conducted using real-world POJ dataset show that the proposed system outperforms other traditional approaches. The experiment also demonstrates that the proposed system is recommending challenges based on the learner's current context. The implicit rating extracted using the proposed approach works accurately in the recommender system.Originality/valueThe proposed system contains the following novel approaches to address the lack of rating and context-aware recommendations. The mathematical model was used to extract ratings from learner submissions. The statistical approach was used in data preprocessing. The sequence similarity-based learner clustering was used in transition matrix. Utilizing the rating as a utility in the USPAN algorithm provides useful insights into learner–challenge relationships.

Combining reinforcement learning algorithm and genetic algorithm to solve the traveling salesman problem

AbstractWith the growing recognition of the unique advantages of reinforcement learning and genetic algorithms in addressing combinatorial optimization problems, this study aims to integrate these two methods to collectively tackle the classic combinatorial optimization challenge of the travelling salesman problem (TSP). The TSP stands as a quintessential combinatorial optimization challenge, tasked with determining the shortest path among designated cities. This paper introduces an innovative approach by amalgamating reinforcement learning's path selection prowess with genetic algorithms' global search strategy, aiming to uncover superior solutions in TSP. Specifically, the experiment employs a dual Q‐learning algorithm within reinforcement learning to identify multiple optimal paths, serving as progenitors for the genetic algorithm to further enhance performance. The paper meticulously outlines the problem modelling process, elucidating TSP instance definitions, descriptions, and precise objective function definitions. Experimental findings underscore the substantial enhancements achievable in TSP optimization through this comprehensive approach, offering a fresh perspective and methodology for tackling combinatorial optimization challenges.

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

The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update.

Galaxy (https://galaxyproject.org) is deployed globally, predominantly through free-to-use services, supporting user-driven research that broadens in scope each year. Users are attracted to public Galaxy services by platform stability, tool and reference dataset diversity, training, supportand integration, which enables complex, reproducible, shareable data analysis. Applying the principles of user experience design (UXD), has driven improvements in accessibility, tool discoverability through Galaxy Labs/subdomains, and a redesigned Galaxy ToolShed. Galaxy tool capabilities are progressing in two strategic directions: integrating general purpose graphical processing units (GPGPU) access for cutting-edge methods, and licensed tool support. Engagement with global research consortia is being increased by developing more workflows in Galaxy and by resourcing the public Galaxy services to run them. The Galaxy Training Network (GTN) portfolio has grown in both size, and accessibility, through learning paths and direct integration with Galaxy tools that feature in training courses. Code development continues in line with the Galaxy Project roadmap, with improvements to job scheduling and the user interface. Environmental impact assessment is also helping engage users and developers, reminding them of their role in sustainability, by displaying estimated CO2 emissions generated by each Galaxy job.

Exploring a practice theoretical tool-kit approach for studying professional and organisational learning processes: a pragmatist interpretation

ABSTRACT This article conceptually explores different practice theoretical vantage points in empirical studies of professional and organisational learning processes. Through an ethnographic study of a management development program in Denmark, possibilities and limitations of three different practice-theoretical approaches are considered. Firstly, learning processes are examined as professional identity formation through participation in the community of practice around the development program. Next, Schatzki’s practice-theoretical approach is applied, focusing on management as situated work practice. A third approach focusing on persons’ learning paths in professional and organisational practices is considered: Ole Dreier's concept of person is discussed as a further development of practice theories in line with Schatzki’s thinking. The article explores the epistemological and ontological stances taken by the three practice theories in relation to the study of learning processes. A pragmatist interpretation of a ‘tool-kit’ approach to operationalising different practice theoretical approaches in alliance is developed, considering how this can generate constructive analytical tensions for exploring and understanding learning processes.

Self-Supervised pre-training model based on Multi-view for MOOC Recommendation

Recommendation strategies based on concepts of knowledge are gradually applied to personalized course recommendation to promote model learning from implicit feedback data. However, existing approaches typically overlook the prerequisite dependency between concepts, which is the significant basis for connecting courses, and they fail to effectively model the relationship between items and attributes of courses, leading to inadequate capturing of associations between data and ineffective integration of implicit semantics into sequence representations. In this paper, we propose Self-Supervised pre-training model based on Multi-view for MOOC Recommendation (SSM4MR) that exploits non-explicit but inherently correlated features to guide the representation learning of users’ course preferences. In particular, to keep the model from relying solely on course prediction loss and overmphasising on the final performance, we treat the concepts of knowledge, course items and learning paths as different views, then sufficiently model the intrinsic relevance among multi-view through formulating multiple specific self-supervised objectives. As such, our model enhances the sequence representation and ultimately achieves high-performance course recommendation. All the extensive experiments and analyses provide persuasive support for the superiority of the model design and the recommendation results.