Personalized Learning Materials Research Articles

Design and Evaluation of a Learning Resource Recommendation System Based on Machine Learning

In the era of information overload, learners are often overwhelmed by the vast amount of educational content available online. To address this challenge, we have developed a Learning Resource Recommendation System (LRRS) that leverages machine learning techniques to provide personalized learning material suggestions. The LRRS is designed to analyze learners' profiles, preferences, and learning behaviors to deliver a tailored learning experience. By employing collaborative filtering, content-based filtering, and hybrid methods, the system can predict and recommend resources that are most relevant to individual learners. This paper presents the architecture of the LRRS, the methodologies used for recommendation, and the results of its evaluation. We conducted experiments using a dataset of learners and resources to assess the accuracy and effectiveness of the system. The evaluation metrics, including precision, recall, and F1-score, demonstrate the system's ability to enhance the learning process by providing relevant and engaging content. The LRRS also includes a feedback mechanism that allows for continuous improvement of the recommendation algorithms based on user interactions. The findings of this study contribute to the field of educational technology by offering insights into the development of intelligent systems that can support personalized learning.

REMEDIAL PRACTICES AND STUDENTS COMPETENCES IN MATHEMATICS IN PUBLIC SECONDARY SCHOOLS IN RWANDA A CASE OF GICUMBI DISTRICT

Background: Mathematics plays a crucial role in both vocational success (Jetter, 2017) and everyday life (Umameh, 2011), yet many students face challenges in mastering it. Globally, educational equity remains a priority, with persistent disparities in math education, especially among low-SES students (UNESCO, OECD). Tanzania and Rwanda are actively addressing these challenges to enhance educational outcomes. Materials and Methods: This descriptive survey study involved 171 participants from 32 secondary schools in Gicumbi District: 105 mathematics instructors, 50 subject leaders, 35 head teachers, and 110 students. Data collection employed random sampling, with Slovenes method used to select the study sample. SPSS version 21.0 facilitated analysis, encompassing descriptive statistics (frequency, percentage, mean, standard deviation) and inferential statistics (correlation, regression coefficients). Results and Discussions: Findings revealed strong consensus among respondents on the efficacy of modifying instructional methods (80.0%), using personalized learning materials (63.1%), integrating visual and auditory resources (60.0%), and creating tailored exercises (68.0%) to improve math competencies. Key indicators such as problem-solving (64.3%), method application (96.6%), reasoning (92.3%), and mathematical representation (92.3%) demonstrated significant improvements. Enhanced physical facilities also correlated positively with student outcomes, underscoring the role of supportive environments in fostering mathematical competence. Conclusion: The study emphasizes the effectiveness of adapting teaching methods and environments to enhance students math skills. Customized approaches, including personalized materials and multimedia integration, proved instrumental in improving competencies like problem-solving and reasoning. Statistical analyses consistently supported these findings, highlighting the importance of personalized and diversified teaching strategies in enhancing math education in Gicumbi, Rwanda.

Leadership development through self-upskilling: role of generative artificial intelligence

PurposeThis viewpoint paper investigates the changing role of leadership in a dynamic, technologically driven society, and the vital requirement for leaders to engage in continuous self-upskilling to remain effective. It emphasizes the importance of generative artificial intelligence (GAI) in transforming personalized learning experiences for leaders and allowing them to adapt to an ever-changing world.Design/methodology/approachA review of current research papers, articles, and case studies is conducted to evaluate the integration of generative AI in leadership self-upskilling. It examines the possibilities and possible benefits of generative AI, and the issues it offers regarding data privacy, algorithmic bias, and learning requirements.FindingsThe findings highlight the transformational potential of GAI in self-upskilling for leaders. It demonstrates how GAI can build personalized learning materials, provide real-time feedback, and adapt content to individual learning styles. It identifies notable executives who have effectively embraced GAI for their self-upskilling journeys, resulting in increased productivity and competitiveness.Practical implicationsThe paper investigates the application of GAI for self-improvement, addressing challenges such as data privacy and algorithmic bias while suggesting responsible AI use tactics.Originality/valueThis study investigates the relationship between leadership and AI, emphasizing the importance of leaders in self-improvement as well as the possibility of AI-powered self-upskilling to democratize leadership development while also promoting ethical use.

Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review

ChatGPT is a large language model (LLM) based on artificial intelligence (AI) capable of responding in multiple languages and generating nuanced and highly complex responses. While ChatGPT holds promising applications in medical education, its limitations and potential risks cannot be ignored. A scoping review was conducted for English articles discussing ChatGPT in the context of medical education published after 2022. A literature search was performed using PubMed/MEDLINE, Embase, and Web of Science databases, and information was extracted from the relevant studies that were ultimately included. ChatGPT exhibits various potential applications in medical education, such as providing personalized learning plans and materials, creating clinical practice simulation scenarios, and assisting in writing articles. However, challenges associated with academic integrity, data accuracy, and potential harm to learning were also highlighted in the literature. The paper emphasizes certain recommendations for using ChatGPT, including the establishment of guidelines. Based on the review, 3 key research areas were proposed: cultivating the ability of medical students to use ChatGPT correctly, integrating ChatGPT into teaching activities and processes, and proposing standards for the use of AI by medical students. ChatGPT has the potential to transform medical education, but careful consideration is required for its full integration. To harness the full potential of ChatGPT in medical education, attention should not only be given to the capabilities of AI but also to its impact on students and teachers.

Investigating How Generative AI Can Create Personalized Learning Materials Tailored to Individual Student Needs

Investigating How Generative AI Can Create Personalized Learning Materials Tailored to Individual Student Needs

A Comprehensive Model for Recommending Personalized Learning Resources for the Development of Linguistic Competence

With the continuous advancement of globalization and informatization, the linguistic competence of college students has become a key index for evaluating their comprehensive quality. Faced with diverse needs of students and educational environments, it is increasingly important and complex to accurately locate the linguistic competence goals of college students. Although existing research methods, such as standardized testing and teacher assessment, provide certain convenience, they rely on single data sources and have a certain degree of subjectivity, which limits their universality and accuracy. This study aimed to solve this problem by doing comprehensive research on two aspects: first, curriculum analysis based on relation extraction. A relation extraction model, such as Casrel, was used for advanced text analysis, which provided educators with more in-depth insights; second, personalized learning material recommendation based on text recommendation. Personalized learning paths were provided for students of different levels using the abstractness-based text recommendation algorithm. This study not only filled the gaps in existing research methods, but also provided a new, scientific and efficient solution, helping improve the quality of education and promote the formulation of scientific education policies.

Using a neural network in the work of an informatics teacher

Neural networks in education are a promising direction for encreasing the level of teaching and improving the quality of learning. Neural networks can be used for various types of learning, from teaching to creating personalized learning materials for individual students. The article describes the prospects and possibilities of using neural networks in the work of an informatics teacher, provides examples of queries to the neural network and options for their execution by the neural network, and analyzes the responses issued by the neural network to queries. Methodological ideas for using a neural network will be useful to informatics teachers when preparing for informatics lessons, extracurricular activities and competitions. The use of neural networks by teachers in their activities will allow the teacher to save time when preparing for a lesson, use a creative approach when planning lessons, diversify the teacher's teaching materials and will contribute to the formation of the ability to use information technology to solve teaching problems.

Improving the Productivity of the E-learning Process

The e-Learning systems have become extremely popular over the last decade. Moreover, not a long time ago they were considered as an alternative way of representing learning content. However, suddenly due to the COVID-19 pandemic they turned out to be the only possible option for keeping the learning process uninterrupted. This makes any research on possible ways of optimizing the presented learning content and its better absorption by the learners, as this paper, extremely important. The paper considers the possibilities for improving the productivity of the e-Learning process in four main areas: (1) reusability of learning content; (2) personalized representation of learning content; (3) proper identification of participants in an e-Learning process; (4) opportunities for easy scaling of learning environments. As a result of the research, a set of methods and models for creating personalized learning materials has been developed. The learning materials are aligned to the learner's preferred learning style and created from a thematicoriented content. All the conceptual models were then implemented into a software environment, which provides an opportunity for their validation and verification and assessment of their effectiveness. The research also presents a so-called "concept for software scaling" from the perspective of an e-Learning environment and a novel software architecture to be used as a base of a system implementation.

Automatic Assessment Method of Oral English Based on Multimodality

With the rapid development of Internet technology and educational informatization, there are more and more oral materials available on the Internet. Therefore, how to adapt to learners’ dynamic abilities and provide them with personalized learning materials has become a very important issue in educational technology. Aiming at the inefficiency of the existing automatic assessment of spoken English, a multimodal-based automatic assessment method of spoken English is proposed. The Word2Vec model is used to extract text features, and then, speech and text are input into GRU temporal structure, and encoder coding is used for multimodal fusion to realize automatic evaluation of multimodal spoken English. Simulation results show that the multimodal model proposed in this paper is superior to the traditional oral English automatic assessment model in terms of fluency, emotional expression, and sense of rhythm and can better improve learners’ oral English level.

The Teaching Pattern of Law Majors Using Artificial Intelligence and Deep Neural Network Under Educational Psychology.

With the increasing attention to the cultivation of legal talents, a new teaching model has been explored through artificial intelligence (AI) technology under educational psychology, which focuses on improving learning initiative, teaching methods, and teaching quality of students. First, the application of AI and deep neural network (DNN) algorithms are reviewed in education, and the advantages and disadvantages of traditional learning material recommendation algorithms are summarized. Then, a personalized learning material recommendation algorithm is put forward based on DNN, together with an adaptive learning system based on DNN. Finally, the traditional user-based collaborative filtering (UserCF) model and lifelong topic modeling (LTM) algorithm are introduced as the control group to verify the performance of the proposed recommendation system. The results show that the best learning rate of model training is 0.0001, the best dropout value is 0.5, and the best batch size is 32. The proposed personalized learning resource recommendation method based on deep learning (DL) still has good stability under various training data scales. The personalized test questions of recommended students are moderately difficult. It is easier to recommend materials according to the acquisition of knowledge points and the practicability of the recommended test questions of students. Personalized learning material recommendation algorithm based on AI can timely feedback needs of students, thereby improving the effect of classroom teaching. Using the combination of AI and DL algorithms in teaching design, students can complete targeted personalized learning assignments, which is of great significance to cultivate high-level legal professionals.

Improving Learner-Computer Interaction through Intelligent Learning Material Delivery Using Instructional Design Modeling.

This paper describes an innovative and sophisticated approach for improving learner-computer interaction in the tutoring of Java programming through the delivery of adequate learning material to learners. To achieve this, an instructional theory and intelligent techniques are combined, namely the Component Display Theory along with content-based filtering and multiple-criteria decision analysis, with the intention of providing personalized learning material and thus, improving student interaction. Until now, the majority of the research efforts mainly focus on adapting the presentation of learning material based on students’ characteristics. As such, there is free space for researching issues like delivering the appropriate type of learning material, in order to maintain the pedagogical affordance of the educational software. The blending of instructional design theories and sophisticated techniques can offer a more personalized and adaptive learning experience to learners of computer programming. The paper presents a fully operating intelligent educational software. It merges pedagogical and technological approaches for sophisticated learning material delivery to students. Moreover, it was used by undergraduate university students to learn Java programming for a semester during the COVID-19 lockdown. The findings of the evaluation showed that the presented way for delivering the Java learning material surpassed other approaches incorporating merely instructional models or intelligent tools, in terms of satisfaction and knowledge acquisition.

PAKES: A Reinforcement Learning-Based Personalized Adaptability Knowledge Extraction Strategy for Adaptive Learning Systems

Advancements in adaptive educational technologies, specifically the adaptive learning system, have made it possible to automatically optimize the sequencing of the pedagogical instructions according to the needs of individual learners. The crux of such systems lies in the instructional sequencing policy, which recommends personalized learning material based on the learning experiences of the learner to maximize their learning outcomes. However, limited available information such as cognitive, affective states, and competence levels of the learners ongoing knowledge points servers critical challenges to optimizing individual-specific pedagogical instructions in real-time. Moreover, making such decisions policy for every learner with a unique knowledge profile demands a trade-off between learner current knowledge and curiosity to learn next knowledge point. To address these challenges, this paper proposes a personalized adaptability knowledge extraction strategy (PAKES) using cognitive diagnosis and reinforcement learning (RL). We apply the general diagnostic model to track the current knowledge state of the learners. Subsequently, an RL-based Q-learning algorithm is employed to recommend optimal pedagogical instructions for individuals to meet their learning objectives while maintaining equilibrium among the learner-control and teaching trajectories. The results indicate that the learning analytics of the proposed framework can fairly deliver the optimal pedagogical paths for the learners based upon their learning profiles. A 62% learning progress score was achieved with the pedagogical paths recommended by the PAKES, showing a 20% improvement compared to the baseline model.

Applications of Artificial Neural Networks in E-Learning Personalization

Artificial learning is playing a very important role in supporting emerging and existing technologies present around us. In the areas of learning and tutoring from a person to a researcher, it plays a key role in the field of knowledge due to its advancement. The adaptive e-learning is providing personalized and advanced learning system to the learners. Why this is important? This is because it facilitates the capacity of a person which provides more accuracy in reading and understanding in more detail. To find the most appropriate personalized learning material for a learner is difficult on the net. Artificial Intelligence-based E-Learning is a platform that possesses the ability to perform different tasks that require human intelligence. It helps in analyzing and creating different solutions to human-related problems, speech recognition, software problems. Due to its diversifying nature in different fields here it is in much demand for the process of e-learning. Artificial Neural Networks is making everything easily available and in finding their own personalized learning environment through innumerable resources by suggesting to users depending on their interests and in taking less amount of time and effort. In this paper, I will discuss the importance of artificial neural networks in E-Learning personalization along with its advantages and disadvantages. Also, I will be discussing some of the use cases and the importance of artificial neural networks as Neural Networks, personalization, artificial intelligence, applications.

A Genetic Algorithm-Based Personalized Remedial Learning System for Learning Object-Oriented Concepts of Java

Contribution: An online genetic algorithm-based remedial learning system is presented in order to strengthen students’ understanding of object-oriented programming (OOP) concepts by tailoring personalized learning materials according to each student’s strengths and weaknesses. Background: Prior studies on computer programming education have analyzed methods of learning OOP, and shown that teaching this topic is a challenge. A simple and personalized learning system for generating remedial learning materials would therefore be valuable, but had yet to be designed. Intended Outcomes: Students’ grasp of OOP concepts is expected to improve through study of the tailored remedial learning materials generated by the system. Application Design: Students who had previously studied OOP were recruited to test the learning system in a two-semester pre-experiment using a one-group pre-test-post-test design. The students first took a pre-test that determined their individual strengths and weaknesses in these concepts. They then read three sets of quiz-based remedial learning materials; each set was generated by the system according to the individual student’s answers in the pre-test and previous quizzes. Findings: 1) Overall, the changes between learners’ pre-and post-test scores were significant; 2) Score changes for different learners (junior, senior, low-achievement, and high-achievement learners) and for different learning styles (intensive and non-intensive) were also significant; and 3) Score changes for low-achievement learners were greater than those for high-achievement learners.

From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning

The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.

The Structural Way for Binding a Learning Material with Personal Preferences of Learners

Learning content creation process requires more than just collection and presentation of set of information. In order to gain knowledge, the learning content should be designed in such a way to meet predefined learning goals. Learning goals determine the entire process of learning. Bloom’s Taxonomy provides a description of a cognitive process with six hierarchical levels, each containing specific learning goal to achieve. It could be adapted into a model by which tutors create learning materials. However, when it comes to productivity of learning, it is important to consider the personalization of the presented content according to the learning style of the individual. This article analyzes the correlation between Bloom’s Taxonomy and Honey and Mumford’s learning cycle, providing a way to bind the structure of learning material to the personal preferences of learners. This novel way of creating learning materials is integrated into a model that is used for automatic generation of personalized learning materials. The effectiveness of the model is further verified through an experiment with real participants. The results of the experiment show promising potential in the way of how a learner’s capabilities may be enriched. However, while experimenting and rest of the work on the model outline some challenges before the model’s application and future work.

An Adaptive e-Learning System for Enhancing Learning Performance: Based on Dynamic Scaffolding Theory

Adaptive learning for individual learners has recently become popular in education. This study aims to fill the void in the existing literature by building an adaptive e-learning system with self-assessment rubrics based on the dynamic scaffolding theory in response to different student needs. Meanwhile, the purpose of this study is to explore the effectiveness of using adaptive e-learning with dynamic scaffoldings and rubrics in fostering students’ learning outcomes. An experimental design was conducted to evaluate learning effectiveness and learning satisfaction in the Excel (spreadsheet) of the course for using the developed adaptive e-learning system. Sixty undergraduate students from a technology university in central Taiwan participated in this experimental study and executed a pretest and a posttest. Research results revealed that the developed adaptive e-learning system can effectively support students with personalized learning materials and successfully helps students acquired knowledge and develop cognitive abilities. The results recommend that teachers could employ rubrics as a self-assessment tool for supporting students with dynamic scaffoldings to conduct a learner-centered e-learning environment. Additionally, the lack of generalizability is clearly a limitation of the present data due to a few participants. Finally, future research direction of this study was also discussed.

집단지성 기반 학습자료 북마킹 서비스 시스템

최근 IT 환경의 변화에 따라 웹 서비스를 기반으로 대규모 사용자 대상의 상호 참여적인 MOOC(Massive Open Online Courses)과 같은 온라인 교육 환경이 부상하고 있다. 그러나 온라인 교육 시스템은 원거리로 학습이 이루어짐에 따라 학습자의 자발적 동기를 꾸준히 유지하기 어려우며, 또한 학습자 간에 지식을 공유하고 공유한 지식을 활용하는 기능이 부족하다. 이러한 문제를 극복하기 위해 구성주의적 학습이론과 집단지성에 기반하여 학습자가 보유한 학습자료를 공유하고 개인화된 학습자료 추천을 받을 수 있는 학습자료 북마킹 서비스인 WeStudy를 구현하였다. 위키피디아(Wikipedia), 슬라이드쉐어 (SlideShare), 비디오렉쳐스 (VideoLectures) 등 현존하는 집단지성 기반 서비스들의 주요 기능으로부터 필요한 집단지성 기능들을 검토하였으며, 본 서비스의 주요 기능으로 1) 리스트 및 그래프 형태의 학습자료 리스트 시각화, 2) 개인화된 학습자료 추천, 3) 보다 상세한 학습자료 추천을 위한 관심 학습자 지정 등을 도출하여 시스템을 설계하였다. 이후, 웹 기반으로 구현된 세 가지 주요기능 별로 개량된 휴리스틱 사용성 평가 방법을 통해 개발된 시스템의 사용성 평가를 실시하였다. 10명의 HCI 분야 전공자 및 현업 종사자를 대상으로 정량적 및 정성적인 평가 결과, 세 가지의 주요 기능에서 전반적으로 사용성이 우수한 것으로 판정되었다. 주요 기능 별 정성적인 평가에서 도출된 여러 마이너 이슈들을 반영할 필요가 있으며, 향후 대규모 사용자를 대상으로 본 서비스를 보급하고 이용할 수 있도록 제공하여 자발적인 지식 공유 환경을 조성할 수 있을 것으로 전망된다. Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

An adaptive e‐learning system for Java programming course, based on Dokeos LE

ABSTRACTThis article describes an adaptive e‐learning system based on Dokeos open‐source e‐learning environment (Dokeos LE), developed to enable personalized e‐learning. The proposed adaptive e‐learning system offers creating personalized learning materials and scenarios, adapted individually by students, in accordance with their targeted knowledge level, background knowledge, learning styles, and interests for various topics. The proposed adaptive e‐learning system was implemented for Java programming course, however, in the same fashion this software can be used for many other engineering courses. © 2014 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:337–343, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21603

EBFO-based Personalized Learning Materials Recommendation in Cooperative Learning Group

EBFO-based Personalized Learning Materials Recommendation in Cooperative Learning Group