Personalized Learning Path Research Articles

Research on the construction of personalized learning system supported by big data in education

Abstract In order to optimize the drawbacks of current personalized learning systems on the market, a big data algorithm is used to optimize the personalized learning system. This paper first analyzes the system model, constructs the basic framework of the system, and optimizes the algorithm based on a collaborative filtering algorithm that converts user behavior into ratings and recommends personalized learning content for learners. Since learning resources and learners are interconnected, this connection is analyzed by an ant colony algorithm to provide the optimal path for students to learn and create a personalized learning path. After comparing the student models, we understand that the clearer the description of students’ interests, the clearer the accuracy returned, where User A and User C have the highest similarity of 99% and accuracy of 85% and 88% respectively, proving the feasibility of the system. The personalized learning system supported by big data in education can optimize the drawbacks of the current personalized learning system in the market, meet the concept of teaching according to student’s abilities, and outperform the learning system in the market.

Recent Progress in the Use of Artificial Intelligence Tools in Education

The use of artificial intelligent (AI) tools in education has had a significant impact on learning experiences and outcomes. This review looks at recent advances in artificial intelligence tools and their implications for future research and practice. The review article followed the PRISMA method. Relevant articles on AI in education, specifically those describing the integration of AI or machine learning in undergraduate was extracted from the SCOPUS database. The inclusion criteria focus on articles directly related to teaching and training in structured programs that published in 2019 to 2023. A total of twelve documents were recognized and subjected to hand identification for subsequent analysis. The result shows that cognitive tutors, which are interactive learning environments facilitated by intelligent tutoring systems, improve learning outcomes. The challenge of balancing instructional assistance and self-directed learning, on the other hand, is inherent in AI-driven tools. AI tools in higher education provide numerous advantages at the institutional, social, and instructional levels. Disruptive AI tools such as ChatGPT have emerged, but challenges include job displacement concerns and the need for constant adaptation. AI language learning tools are important in language acquisition processes because they provide personalized learning paths and interactive engagement. However, ethical considerations and competencies related to AI-based tools in education are assessed, with parallels drawn to healthcare guidelines. It emphasizes the importance of strong ethical frameworks, as well as the critical role of educators and professionals in responsible AI use, in order to maintain a symbiotic relationship between human expertise and technological advancement. Overall, this review summarizes recent advances in the use of artificial intelligence tools to revolutionize education, emphasizing the importance of ongoing research, cross-disciplinary collaboration, and careful implementation.

Analysis of Meta-Cosmos Empowering Basic English Teaching and Learning

Metaverse is a virtual, interactive digital environment that provides students with a new learning scenario through technologies such as virtual reality, augmented reality and artificial intelligence. In order to provide a more attractive and effective English learning environment, researchers have begun to introduce meta-universe technology into English language teaching. In meta-universe-enabled basic English teaching, students can interact with the virtual world through immersive learning experiences that simulate real-life scenarios and improve their language skills. Metaverse technology can also provide students with personalized learning paths and resources. In addition, social interaction in the metaverse environment promotes the development of students' intercultural communication and cooperation skills. However, metaverse-enabled basic English language teaching also faces some challenges, such as the cost of technological equipment, teacher training and technical support, etc. Future research needs to further explore how to maximize the effect of metaverse technology in English language teaching.

Visualization design of talent training paths in university education based on pattern knowledge

Abstract In the background of big data informatization, social and economic development needs talent support, and colleges and universities are the main positions of talent training. Using the features of self-organization, distributed multi-intelligence and positive feedback of the ant colony algorithm, a personalized learning path recommendation algorithm supporting pattern knowledge is proposed by combining the above learning feature model. The subject starts an independent solution search simultaneously at multiple points in the problem space, which increases the algorithm’s reliability and gives the algorithm a strong global search capability. When performing talent development path recommendation, the collaborative mechanism of distributed multi-subjects of the ant colony algorithm makes it possible to find an acceptable path quickly. Combined with the new knowledge representation model, the matching degree of students and learning objects is calculated from three dimensions, the heuristic information is obtained accordingly, and the Euclidean distance is used for matching degree calculation. Using the ant colony optimization algorithm to analyze the paths for cultivating talents in college education, it is concluded that in terms of the number of teachers: the total number of teachers with positive senior titles in five colleges and universities is 238, accounting for 8.88%, and the number of full-time teachers with senior associate titles is 618, accounting for 23.07%, all of which are lower than the average level in Fujian Province. This study has some reference significance to the cultivation of applied talents in local colleges and universities and has some reference value to the government in formulating the policy of cultivating talents in colleges and universities.

Innovative interactive technologies in the training of professional education specialists by means of digital technologies

This article is devoted to the study and analysis of the role and influence of innovative interactive technologies in the training of professional education specialists using digital technologies. Digital technologies are becoming an increasingly integral part of our daily lives and impacting various fields, including education. The article examines the modern foreign and domestic experience of using modern innovative interactive technologies in the professional training of future vocational education specialists. The possibilities of implementing augmented reality (AR) and virtual reality (VR) to create immersive learning environments are described. Emphasis is placed on adaptive educational platforms that are able to individualize the educational process taking into account the personal needs and individual characteristics of students. The analysis of the influence of interactive technologies on the activity and interest of students, increasing the level of involvement and motivation to study was carried out. The advantages of these technologies in the training of specialists are analyzed, their impact on student motivation, understanding of complex concepts and the creation of interactive learning environments is noted. It highlights how the adaptability of learning platforms helps create personalized learning paths for each student, taking into account their needs and pace of learning. The experience of using innovative and interactive technologies in various fields, including education, science and technological development, is outlined.
 The results of the study indicate the importance of integrating digital technologies into the educational process to achieve more effective learning and development of relevant skills among students of vocational education.

Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm

To solve the problems of slow convergence and low accuracy when the traditional ant colony optimization (ACO) algorithm is applied to online learning path recommendation problems, this study proposes an online personalized learning path recommendation model (OPLPRM) based on the saltatory evolution ant colony optimization (SEACO) algorithm to achieve fast, accurate, real-time interactive and high-quality learning path recommendations. Consequently, an online personalized learning path optimization model with a time window was constructed first. This model not only considers the learning order of the recommended learning resources, but also further takes the review behavior pattern of learners into consideration, which improves the quality of the learning path recommendation. Then, this study constructed a SEACO algorithm suitable for online personalized learning path recommendation, from the perspective of optimal learning path prediction, which predicts path pheromone evolution by mining historical data, injecting the domain knowledge of learning path prediction that can achieve best learning effects extracted from domain experts and reducing invalid search, thus improving the speed and accuracy of learning path optimization. A simulation experiment was carried out on the proposed online personalized learning path recommendation model by using the real leaner learning behavior data set from the British “Open University” platform. The results illustrate that the performance of the proposed online personalized learning path recommendation model, based on the SEACO algorithm for improving the optimization speed and accuracy of the learning path, is better than traditional ACO algorithm, and it can quickly and accurately recommend the most suitable learning path according to the changing needs of learners in a limited time.

Personalised learning paths for information literacy using Canvas MasteryPaths

This project report describes using MasteryPaths in the Canvas Virtual Learning Environment as a method of helping improve the information literacy (IL) competence for undergraduate science students studying in their first year at university. The MasteryPaths incorporated a series of formative quiz assessments on referencing and finding and evaluating information, which depending on the students’ initial score directed them to further enrichment or support materials. Four degree courses (Biology, Forensics, Biomedicine and Sport) each included the MasteryPaths in a first-year module in 2020/21, which were available in Canvas following face-to-face IL sessions. Focus groups were conducted with students, and interviews were carried out with two module leaders to explore perceptions of the MasteryPaths design and effectiveness for IL skills. The article provides insight into how online, self-paced, IL formative quiz assessments can be developed in a way to motivate and engage students in their learning.

DEVELOPMENT OF A DIGITAL EDUCATIONAL ENVIRONMENT IN CHINA

Current model design research in the field of personalized learning mainly includes three categories: personalized learner model, personalized learning resource recommendation model, and personalized learning path model. The development of a personalized student model mainly depends on the basic information of the student, his interests and preferences, learning style, behavioral characteristics, etc.; and all learner information and data are also used to develop and implement a personalized learning resource recommendation model and an individualized learning path model Essential framework. The learner model is developed through an in-depth analysis of the learner's main attributes, learning process and learning outcomes, and on this basis, a model of the learning path that fits the learner is planned and personalized learning resources are presented. Among the various developed models, the student model occupies the largest share. In the study of personalized learning, different models can be developed that correspond to the individual characteristics and actual needs of students, so as to be more in line with the concept of respecting the individual characteristics of students, thereby contributing to the high-quality development of individualized learning and the overall development of students. We also described the model with tables that are given in the article.

Educational Platforms: Digital Tools for the teaching-learning process in Education

Educational platforms are digital tools that have revolutionized the teaching-learning process in education. Through a bibliographic-type methodology, relevant information has been compiled from various authors and experts in the field. These platforms offer access and flexibility, allowing students to learn anytime, anywhere, adapting to their individual needs. They facilitate interaction and collaboration between students and teachers, through discussion forums, online chats and shared spaces. In addition, they focus on personalization and adaptability, offering personalized learning paths and adaptive content. Assessment and progress tracking are also key aspects of educational platforms, providing tools for taking online assessments and generating grade reports. Lastly, they offer a wide range of educational resources and content, such as texts, videos and interactive activities, enriching the teaching-learning process. Finally, educational platforms, supported by a bibliographic methodology, have transformed education by providing flexible access, fostering interaction and collaboration, enabling personalization of learning, facilitating assessment and monitoring of progress, and offering a variety of educational resources. These digital tools have significantly improved the educational process, adapting to the needs of students in the digital age.

A Personalized Learning Path for French Study in Colleges Based on a Big Data Knowledge Map

The education industry is gradually improving with the rapid development of information technology. The learners use networks and computers to alter the traditional instructional framework based on educational information technology and achieve personalized learning. This teaching method emphasizes each learner’s identity and autonomy. However, due to the huge number of learning resources available on the Internet, students lack relevant courses, clear learning tasks, and the connection between various knowledge points, resulting in an unsatisfactory effect on the learning process. Knowledge maps for different learner types are created using historical learners’ conceptual knowledge and the segmentation and correlation technique of big data knowledge maps. Using a big data method in this process will automatically generate a set of weak conceptual learning pathways. For this problem, in the era of big data, people put forward the concept of knowledge map and used the algorithm based on the big data knowledge map to study the personalized learning path for college French. The content, structure, and relationship of college French knowledge points can be accurately expressed using this method, which is preferred by college administrators and teachers. This paper aims to investigate the personalized learning path for college French using a big data knowledge map, starting with the characteristics of a college French field of study. This study provides technical support in the establishment of a big data knowledge map based on a learning path recommendation framework. So, after the performance of several commonly used learning path recommendation algorithms, three French students have been selected at random for learning path planning. The results show that personalized learning path planning can be realized based on a knowledge map pre-repair relationship and objective attributes. In the analysis, not only the proposed technique is compared with the conventional optimization approach, but also a comparison study on the benefits of several learning effect prediction models is also performed. The results of this study suggest that this algorithm has a high learning efficiency and that the effective implementation of recommendations produced using our proposed strategy has a significant advantage.

Research on a personalized learning path recommendation system based on cognitive graph with a cognitive graph

ABSTRACT The necessity for students to clarify their own cognitive structure and the amount of their knowledge mastery for self-reflection is often ignored in building the student model in the adaptive model, which makes the construction of the cognitive structure pointless. Simultaneously, knowledge forgetting causes students’ knowledge level to fall short of what is required to enable the subsequent learning stage. The students’ cognitive map was displayed using cutting-edge cognitive graph technology to address the aforementioned problems, and the reasoning rules were created using the Dual Process Theory and Meaningful Learning Theory. The degree of memory retention in students was calculated using the universal forgetting curve proposed by Ebbinghaus. To improve semantic information, multimodal data were coupled with knowledge graph. Finally, a cognitive graph integrated into the learning path recommendation system was developed. Comparative tests were conducted to verify the system’s efficacy. The experimental group outperformed the control group with an average score of 77.286. The results of the experiment show that the experimental group using the CGLPRS has significantly higher levels of knowledge use and mastery than the control group, showing that the use of cognitive graph and accounting for forgetting can significantly improve conceptual understanding and overall learning satisfaction.

Scheduling personalized study plans considering the stress factor

ABSTRACT Learning path generation involves the computation of learning trajectories to personalize academic instruction to prevent school problems. The Educational Planning Problem ( EPP ) considers generating personalized learning paths by scheduling activities that satisfy expected grades while minimizing plans makespan. In this work, we propose two scheduling models with two new activity duration functions incorporating student stress and sequence dependence to mimic learning effects and increase personalization. We also developed a metaheuristic algorithm to solve real-size instances composed of a greedy randomized adaptive search procedure enhanced by a variable neighborhood search. Experimental results validate the efficiency of our approach and the importance of considering stress in learning trajectories personalization.

Making Bioinformatics Training Events and Material More Discoverable Using TeSS, the ELIXIR Training Portal

Many trainers and organizations are passionate about sharing their training material. Sharing training material has several benefits, such as providing a record of recognition as an author, offering inspiration to other trainers, enabling researchers to discover training resources for their personal learning path, and improving the training resource landscape using data-driven gap analysis from the bioinformatics community. In this article, we present a series of protocols for using the ELIXIR online training registry Training eSupport System (TeSS). TeSS provides a one-stop shop for trainers and trainees to discover online information and content, including training materials, events, and interactive tutorials. For trainees, we provide protocols for registering and logging in and for searching and filtering content. For trainers and organizations, we also show how to manually or automatically register training events and materials. Following these protocols will contribute to promoting training events and add to a growing catalog of materials. This will concomitantly increase the FAIRness of training materials and events. Training registries like TeSS use a scraping mechanism to aggregate training resources from many providers when they have been annotated using Bioschemas specifications. Finally, we describe how to enrich training resources to allow for more efficient sharing of the structured metadata, such as prerequisites, target audience, and learning outcomes using Bioschemas specification. As increasing training events and material are aggregated in TeSS, searching the registry for specific events and materials becomes crucial. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Searching for training events and materials in TeSS Support Protocol: Integrating TeSS widgets on your website Basic Protocol 2: Logging in to TeSS using an institutional account Alternate Protocol: Creating and logging in to a TeSS account Basic Protocol 3: Manual registration of training events in TeSS Basic Protocol 4: Manual registration of training materials in TeSS Basic Protocol 5: Registration of a content provider in TeSS Basic Protocol 6: Automated harvesting of training events and materials in TeSS.

Envisioning Architecture of Metaverse Intensive Learning Experience (MiLEx): Career Readiness in the 21st Century and Collective Intelligence Development Scenario

Th metaverse presents a new opportunity to construct personalized learning paths and to promote practices that scale the development of future skills and collective intelligence. The attitudes, knowledge and skills that are necessary to face the challenges of the 21st century should be developed through iterative cycles of continuous learning, where learners are enabled to experience, reflect, and produce new ideas while participating in a collective creativity process. In this paper, we propose an architecture to develop a metaverse-intensive learning experience (MiLEx) platform with an illustrative scenario that reinforces the development of 21st century career practices and collective intelligence. The learning ecosystem of MiLEx integrates four key elements: (1) key players that define the main actors and their roles in the learning process; (2) a learning context that defines the learning space and the networks of expected interactions among human and non-human objects; (3) experiential learning instances that deliver education via a real-life–virtual merge; and (4) technology support for building practice communities online, developing experiential cycles and transforming knowledge between human and non-human objects within the community. The proposed MiLEx architecture incorporates sets of technological and data components to (1) discover/profile learners and design learner-centric, theoretically grounded and immersive learning experiences; (2) create elements and experiential learning scenarios; (3) analyze learner’s interactive and behavioral patterns; (4) support the emergence of collective intelligence; (5) assess learning outcomes and monitor the learner’s maturity process; and (6) evaluate experienced learning and recommend future experiences. We also present the MiLEx continuum as a cyclic flow of information to promote immersive learning. Finally, we discuss some open issues to increase the learning value and propose some future work suggestions to further shape the transformative potential of metaverse-based learning environments.

Анализ тематик выпускных квалификационных работ студентов переводческих специальностей и их корреляции с потребностями рынка труда

Problem Statement. This study contains a statistical analysis of the translation market by translation type and domain and the distribution of translation student graduation project subjects. The purpose of this study is to improve the translation teaching quality through a better match between the graduation project subjects and the translation market demand. Materials and Methods. The authors reviewed the demand statistics for the Russian, EU, and US translation markets. We also collected a large database of translation graduation projects defended in Russian universities in 2018–2020. The series contains 245 projects. It covers 8 % of the relevant universities, which makes it representative with a 20 % confidence interval and 85 % confidence probability. The subjects of the graduation projects were divided into the following domains: audiovisual translation; theoretical linguistics; game and software localization; medical translation; research and technical translation; political journalism; advertising; interpretation; fiction; legal translation. Results and Discussion. It was found that the university-level translator training does not match the actual market demand. The maximum discrepancies were found in the following categories: “fiction” (21.66 % excess), “research and technical translation” (20.08 % deficiency), and “political journalism” (16.32 % excess). In addition, we analyzed the subjects of 138 graduation papers submitted to the Translator Creativity and Education International Contest in 2022. The lack of positive dynamics was found: the share of fiction reached 34.06% while the research and technical translation category reduced to just 11.59 %. Conclusions. The following corrective measures are proposed: harmonization of the graduation paper subjects with the market demand, engaging practicing translators as instructors, internships with LSPs, personalized learning paths, and interdepartmental projects.

Personalized Learning Paths: Adapting Education with AI-Driven Curriculum

Personalized Learning Paths: Adapting Education with AI-Driven Curriculum

The Use of AI in E-Learning Recommender Systems: A Comprehensive Survey

Nowadays, Artificial Intelligence (AI) technology, especially, machine learning has received significant attention from many researchers and professionals in several domains. For instance, this technology has revolutionized industrials sectors, such as e-commerce, health, manufacturing, and entertainment, as well as, the educational sector. In this context, during the pandemic, educational institutions around the globe recognized the benefits of adopting remote learning and teaching based on many e-learning platforms. Hence, deploying virtual classrooms is not that easy, due to many constraints, such as time, technological cost, lack of interactivity of learners, and the unsuitability of the learner's cognitive abilities and learning style. To this end, understanding the learner's style and providing adapted and personalized content are the main concerns of today's e-learning systems. To this end, integrating efficient recommender systems into online learning environment is required in order to consider student's behaviors and preferences when recommending various learning materials. Thus, intelligent recommender systems are able to provide a personalized learning path in which the system adapts to the student's learning requirements and abilities. This review discusses the different applications, methods, and challenges of AI-based recommendation systems used to assist different stockholders in an online learning environment.

A Study on Emotion Analysis for Online Learning Based on Students' Feedback via Social Networks

Educational Data mining (EDM) in e-Learning systems is a rapidly growing phenomenon. It’s used to improve education by monitoring student performance and trying to understand the students’ learning. Past two decades the teachers collecting the feedback from their students through a written words. This method is very time consuming. Now a dayse-Learning system familiar for the virtual class room environment and learners free to learn at their own pace and to define personal learning path based on their individual needs and interests. This system provides the learning support through explanation, examples, interactive and feedback. The data of feedback is an essential part of effective learning. It helps students understood the subject being studied and gives us idea to give how to improve their learning. The data of feedback can be collected in different ways, such as chart window, SMS, e-Mail, Voice mail and Social Media like twitter, whatsapp. This kind of data should be collected from student in the mode of audio, video and text through social media. This data should be realised the positive and negative or emotion of the students. This paper presents a survey on the analyse of the students feedback by using sentiment analysis methodologies.

Personalized Learning Path Recommendation for E-Learning Based on Knowledge Graph and Graph Convolutional Network

In e-learning, the increasing number of learning resources makes it difficult for learners to find suitable learning resources. In addition, learners may have different preferences and cognitive abilities for learning resources, where differences in learners’ cognitive abilities will lead to different importance of learning resources. Therefore, recommending personalized learning paths for learners has become a research hotspot. Considering learners’ preferences and the importance of learning resources, this paper proposes a learning path recommendation algorithm based on knowledge graph. We construct a multi-dimensional courses knowledge graph in computer field (MCCKG), and then propose a method based on graph convolutional network for modeling high-order correlations on the knowledge graph to more accurately capture learners’ preferences. Furthermore, the importance of learning resources is calculated by using the characteristics of learning resources in the MCCKG and learners’ characteristics. Finally, by weighting the two factors of learners’ preferences and the importance of learning resources, we recommend the optimal learning path for learners. Our method is evaluated from the aspects of learner’s satisfaction, algorithm effectiveness, etc. The experimental results show that the method proposed in this paper can recommend a personalized learning path to satisfy the needs of learners, thus reducing the workload of manually planning learning paths.

Learning Path Optimization Based on Multi-Attribute Matching and Variable Length Continuous Representation

Personalized learning path considers matching symmetrical attributes from both learner and learning material. The evolutionary algorithm approach usually forms the learning path generation problem into a problem that optimizes the matching degree of the learner and the generated learning path. The proposed work considers the matching of the following symmetrical attributes of learner/material: ability level/difficulty level, learning objective/covered concept, learning style/supported learning styles, and expected learning time/required learning time. The prerequisites of material are considered constraints. A variable-length representation of the learning path is adopted based on floating numbers, which significantly reduces the encoding length and simplifies the learning path generating process. An improved differential evolution algorithm is applied to optimize the matching degree of learning path and learner. The quantitative experiments on different problem scales show that the proposed system outperforms the binary-based representation approaches in scaling ability and outperforms the comparative algorithms in efficiency.