Personalized Learning System Research Articles

Digital Technologies and Personalised Learning in Higher Education Institutions, a Case of Uganda Institute of Information and Communications Technology (UICT)

The main hurdle in personal learning system deployment and success, which most of the institutions face is the challenge of outdated and lack of access to technology infrastructure. As a result, teachers, and more importantly learner’s experience via personal learning is impacted. This study sought to examine the relationship between Digital technologies and personalised learning at UICT in Uganda. The target population was 210 from where a sample of 136 including 91 and 45 for students and lecturers respectively was determined using the Krejcie and Morgan table and the study employed stratified random sampling to select students and lecturers as the unit of inquiry with the use of semi-structured questionnaires. Analysis was done using Statistical Package for Social Scientists (SPSS) computer programme version 25. Descriptive statistics of mean and standard deviation was used to present data and correlation coefficient used to establish the relationship between the variables. Results revealed a statistically significant positive relationship between digital technologies and Personalised learning in HIEs in Uganda. Findings indicated that digital technologies had a significant influence on differentiated instruction at UICT (r =0.954, p=0.000) and Adjusted R2 of 87.88%. Also digital technologies contribute significantly to individualized instruction at UICT (r =0.9605, p=0.000) and Adjusted R2 of 89.53%. Results further indicated that digital technologies had a significant relationship on knowledge construction at UICT (r =0.956, p=0.000) and Adjusted R2 of 88.40%. These findings are evident that digital technologies have an impact on the personalised learning at higher education institutions in Uganda. In order to improve personalised learning, digital technologies should be enhanced. These study results are useful in decision making and improving current teaching practices to realise personalised learning in HEIs in Uganda.

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.

Intelligent personalised learning system based on emotions in e-learning

Intelligent personalised learning system based on emotions in e-learning

Flexible English Learning Platform using Collaborative Cloud-Fog-Edge Networking

In the modern age, developing practical online learning tools for English language learners is challenging due to existing systems’ shortcomings. These systems often need proper instructional design, are well-connected to motivational theories, and have limited infrastructure for data sharing, leading to poor learning outcomes and low motivation. To tackle these issues, a new approach called OAELT has been proposed in this paper. OAELT is an Online Assisted English Learning Tool that uses the Fuzzy Analytical Hierarchy Process (FAHP) and collaborative cloud-fog-edge networking to create a flexible learning design that adapts to the needs and preferences of individual learners. Using the FAHP approach, OAELT provides an improved learning experience by tailoring its design to each learner’s unique needs. The collaborative cloud-fog-edge networking approach uses each computing layer’s strengths to deliver a personalized and seamless learning experience. OAELT employs adaptive and dynamic approaches within a flexible instructional paradigm to ensure effective instructional design. This paradigm facilitates collective learning data exchange across cloud, fog, and edge computing layers. The effectiveness of OAELT was evaluated using a descriptive statistics approach, which included a five-dimension questionnaire for students covering cognition, emotion, action, cooperation, and literacy. The results demonstrated that OAELT could enhance learning effectiveness and motivation while providing a flexible and seamless learning experience. According to the experimental data of the proposed model, 46.8% of learners often read English magazines and newspapers to improve their flexibility in English learning. Additionally, 50.4% classified and memorized English according to their categories, while 59% of learners often used context to memorize. These findings suggest that the traditional methods for flexible English learning are not adequate, and the average score of the student’s methods and strategies is mediocre. However, after using OAELT, some students have been able to use different learning curricular reading. Overall, OAELT’s integration of cloud-fog-edge computing with a flexible English learning design can create a more effective and personalized learning system that addresses the challenges of modern learning.

Role of Artificial Intelligence Tools in Enhancing Students' Educational Performance at Higher Levels

The abstract of the study titled "Role of Artificial Intelligence Tools in Enhancing Students' Educational Performance at Higher Levels" explores the integration of artificial intelligence (AI) tools into higher education and their impact on students' educational performance. The study investigates various AI applications such as personalized learning systems, intelligent tutoring systems, and automated assessment tools, and their potential to enhance learning outcomes, engagement, and overall academic achievement. Through a comprehensive review of existing literature and empirical research, the abstract highlights the benefits and challenges associated with AI adoption in education. It also underscores the need for effective implementation strategies, teacher training, and ethical considerations to ensure the responsible and effective use of AI technologies in higher education. The findings of this study contribute to a deeper understanding of how AI tools can be leveraged to optimize educational experiences and outcomes for students in higher education settings.

Smart education system to improve the learning system with CBR based recommendation system using IoT

Over the last few years, the research fields of intelligent learning systems have been improving the process of learning systems. Smart Tutoring System-(STS) applications have been used in e-learning. The results signify the importance of the learner's engagement in customizing a model. The design outcomes of this IoT-based personalized learning system purely work on the audience's learning requirements, their keyword search, their learning experience levels, proficiency level of subjects, and the type of the course being taught. Students spent an average of 25.67 h accessing textual materials weekly, 27.4 h accessing video assets weekly, and similarly 6.5 h accessing visual learning materials. The research analysis part concludes participants' percentage of learning as per evaluations assessment, which increases whenever the outcome analysis comes with Case-Based Reasoning classifiers-CBR based search model. The findings displayed significant differences before and after learning case by case for every learner as per chosen topic and quiz assessments: 42.57% of the students responded before learning the first question assessments whereas 74.82%, of the students responded, after completion of learning from online resources based on their choice with CBR. Recommendation Model Analysis discussed root means square error-RMSE lies from 10% to 20% for 550 students group size. The RMSE result is 24% for a size of 1600, which is low performance compared to other group sizes. This study focuses on the STS recommendation model for the slow learner group to identify required learning from various online resources.

An Evaluation of Redefining Education with Industry 4.0

In recent years, a new industrial paradigm known as Industry 4.0 has begun to trend in emerging technologies in the world, helping to bridge the gap between humans and machines in ways that can only be imagined in science fiction books. This technology is rapidly gaining traction in different parts of the world as a potential tool that can maximize corporate productivity, contribute to scientific growth, enrich a country's economy, and improve people's quality of life. This paper describes the concepts of the implementation of Industry 4.0 to redefine education in India. This study aims to identify the possible uses of Industry 4.0 technologies in the area of education and how they can be utilized to facilitate learning. The question is raised on why the Indian education system needs to implement Industry 4.0. A literature study method and a SWOT analysis were used in this study. The results show that it can help to achieve personalized learning, smart classrooms, Data analytics, collaborative learning, and learning management systems. Industry 4.0 has the potential to transform education in India by providing new tools and methods for teaching and learning that can enhance the quality and effectiveness of education.

To Learn, Unlearn, and Relearn with Personalized Language Learning and Educational Technology

The implementation of Personalized Learning as a learning system provides opportunities for students to manage learning according to their competencies, time, and interests. It has emerged as a trend in K-12 classes in the United States and the United Kingdom, but not that much in higher education. Thus, little research has examined higher education students' transition from conventional to personalized language learning. Students must unlearn and adapt to personalized learning. This research reports the experience of a private university students who chose to do personalized learning system. We examined their learning methods, including technology's effects. Based on a semi-structured interview held with 15 students, data were qualitatively collected on the participants' impression of the system and the technology incorporated to support their learning. The interview suggests that most participants integrate personalized learning and use educational technology to support it. To achieve the same goal, they were situated to unlearn the conventional structured system and relearn the new system provided in personalized learning. The participants claimed that personalized learning supports them in improving their self-management, taking care of non-academics matters, and flexibility in learning based on their competencies and interest. Personalized language learning, with the support of technology, has the potential to be the future of institutional foreign language learning.

Online Learning Platform Analysis During COVID- 19 Pandemic in Indonesia

Covid-19 forces Indonesia's education institutions to operate learning online. The continuity of online learning is influenced by several factors, such as personal space, environment, and learning system. Out of all categories, Learning Management System (LMS) has the most impact on online learning. LMS has a role in facilitating several activities, starting from scheduling classes to assignment collection. There are several choices when choosing LMS that are publicly available. The authors chose three LMSs that are the most popular: Google Classroom, Microsoft Teams, and Edmodo. This research aims to know the factors that affected the online learning process in Indonesia during the COVID-19 pandemic and what influenced the selection of LMS. This research uses a Systematic Literature Review and PRISMA to identify factors that affect the online learning process and learning management system selection. From this research, the authors have found that internet connection is the most discussed factor related to things that affect the online learning process. Next are Learners’ and Instructors’ Engagement and LMS. The authors have also found that easy use, class management, assignment, file sharing, and communication features are the main factors in LMS selection. From this result, the authors hope that this research can increase the quality of online learning in Indonesia. This research can be further developed by discussing more LMS that covers several scopes.

Analyzing the Impact of Artificial Intelligence in Personalized Learning and Adaptive Assessment in Higher Education

This This research aims to examine the impact of AI on personalized learning and adaptive assessment in higher education and investigate the ethical and social implications of using AI in these contexts. A mixed-methods approach was used, involving surveys, interviews, focus groups, institutional records, and system logs to collect both quantitative and qualitative data. The population included higher education institutions that use AI in personalized learning and adaptive assessment systems, as well as students and educators who use these systems. The results of the study showed that AI-based systems had a positive impact on student engagement and motivation, as well as providing personalized learning experiences. However, the analysis also revealed some limitations and potential concerns, such as technical issues and the potential for bias in the AI algorithms used in these systems. Ethical and social implications were analyzed using ethical frameworks such as the Belmont Report and principles of distributive justice. To ensure ethical and socially responsible use of AI in personalized learning and adaptive assessment, clear guidelines and standards for the development and implementation of these systems need to be established. This includes promoting transparency and accountability in the use of student data, ensuring that algorithms are developed and validated in a fair and unbiased manner, and involving diverse stakeholders in the design and implementation of these systems to promote equity and fairness. Informed consent should also be obtained from students and other stakeholders, and measures should be taken to ensure that student data is kept confidential and secure. Ongoing monitoring and evaluation should be conducted to assess the impact of AI-based systems on student outcomes and to identify and address any unintended consequences or biases.

A STUDY ON "AI IN EDUCATION: OPPORTUNITIES AND CHALLENGES FOR PERSONALIZED LEARNING

The use of Artificial Intelligence (AI) in education has the potential to revolutionize the way we teach and learn, making education more personalized, engaging, and effective. AI can be used to develop personalized learning systems that adapt to the needs and abilities of individual learners. These systems can use data such as learner performance and behavior to create customized learning experiences that improve engagement and learning outcomes. AI can also be used to develop intelligent tutoring systems that provide feedback and guidance to learners. These systems can use data analytics to track learner progress and provide targeted feedback and support to help learners achieve their learning objectives. In addition, AI-powered language learning tools, automated grading and assessment, and immersive virtual and augmented reality learning environments are some of the other potential applications of AI in education. However, the adoption of AI in education also brings some challenges, including issues related to privacy, data security, bias, and ethics. It is important to ensure that AI is developed and used in an ethical and responsible manner to avoid any negative consequences. Moreover, the implementation of AI in education requires collaboration and coordination among educators, administrators, developers, and policymakers. This paper reviews the opportunities and challenges of using AI in the field of education, and highlights some of the recent research in this area. The paper concludes with recommendations for future research and practice in the use of AI for personalized learning in education.

What is needed to build a personalized recommender system for K-12 students' E-Learning? Recommendations for future systems and a conceptual framework.

Online learning has significantly expanded along with the spread of the coronavirus disease (COVID-19). Personalization becomes an essential component of learning systems due to students' different learning styles and abilities. Recommending materials that meet the needs and are tailored to learners' styles and abilities is necessary to ensure a personalized learning system. The study conducted a systematic literature review (SLR) of papers on recommendation systems for e-learning in the K12 setting published between 2017 and 2021 and aims to identify the most important component of a personalized recommender system for school students' e-learning. Recommendations for later studies were proposed based on the identified components, namely a personalized conceptual framework for providing materials to school students. The proposed framework comprised four stages: student profiling, material collection, material filtering, and validation.

Reuse of e-learning personalization components

Personalized learning systems use several components in order to create courses adapted to the learners’characteristics. Current emphasis on the reduction of costs of development of new resources has motivated the reuse of the e-learning personalization components in the creation of new components. Several systems have been proposed in the literature. Each system implements a specific approach and includes a set of software components. However, many of these components are not easily reusable. This paper proposes an architecture, which aims to improve the representation of learning components in a reusable and interoperable way. As a result, these components could be integrated easily for the creation of personalized learning courses. This architecture consists of five main packages: learner model, adaptation model, reuse facilities, learner interface and pedagogical knowledge. An experiment is conducted to validate the proposed architecture. The obtained results illustrate the optimal composition of e-learning personalization components through an example.

A Precedence Constrained Knapsack Problem with Uncertain Item Weights for Personalized Learning Systems

This paper studies a unique precedence constrained knapsack problem in which there are two methods available to place an item in the knapsack. Whether or not an item weight is uncertain depends on which one of the two methods is selected. This knapsack problem models students’ decisions on choosing subjects to study in hybrid personalized learning systems in which students can study either under teacher supervision or in an unsupervised self-study mode by using online tools. We incorporate the uncertainty in the problem using a chance-constrained programming framework. Under the assumption that uncertain item weights are independently and normally distributed, we focus on the deterministic reformulation in which the capacity constraint involves a nonlinear and convex function of the decision variables. By using the first-order linear approximations of this function, we propose an exact cutting plane method that iteratively adds feasibility cuts. To supplement this, we develop novel approximate cutting plane methods that converge quickly to high-quality feasible solutions. To improve the computational efficiency of our methods, we introduce new pre-processing procedures to eliminate items beforehand and cover cuts to refine the feasibility space. Our computational experiments on small and large problem instances show that the optimality gaps of our approximate methods are very small overall, and that they are even able to find solutions with no optimality gaps as the number of items increases in the instances. Moreover, our experiments demonstrate that our pre-processing methods are particularly effective when the precedence relations are dense, and that our cover cuts may significantly speed up our exact cutting plane approach in challenging instances.

Multivariate Statistical Analysis of Quality Improvement Effect of Innovation and Entrepreneurship Education Based on Random Matrix Theory

The reform can better improve students’ innovation and entrepreneurial skills and comprehensively improve the innovation and entrepreneurial competitiveness of college students, which is conducive to their survival and development in the market. Therefore, this paper analyzes the data generated in the educational process from the perspective of educational data mining and studies students’ entrepreneurial interests and motivations so as to improve the quality of students’ entrepreneurial guidance. The knowledge map, an important visualization tool in personalized learning systems, is used as a research object to analyze students’ online evaluation data. We develop a prototype system for improving teaching quality—a knowledge map generation and knowledge point recommendation path analysis system. We hope to further explore new ways and methods to promote the reform of innovation and entrepreneurship education in colleges and universities, giving full play to the proper role and value of innovation and entrepreneurship education.

Applying Artificial Intelligence Algorithm through Immersive Technology for Physical Education

Inclusive education is a fundamental human right. However, students with learning disabilities are sometimes excluded from physical education. Virtual Reality (VR) and Augmented Reality (AR) technology are the type of immersive learning that have provided better learning experiences. Personalized learning makes its way towards inclusion in the classroom due to its effectiveness in individualized learning materials by considering the students' different attributes. Thus, this study aims to redesign a personalized and immersive trainer application to facilitate the learning process for the students with learning disabilities (SLD) in physical education using an Artificial Intelligence (AI) algorithm and immersive technologies. This qualitative study assesses ten teachers and 30 students from a special needs school via observation and interviews to design a personalized, immersive physical education learning app. Experts’ views and secondary data are also used as supplementary material for the construction of the app. From the interviews and observations done with the students and teachers, it can be concluded that the SLD can learn visually with suitable guidance from the teacher and trainer. The end output of this study is the design of a Personalized Learning System (PLS) that hopefully can be beneficial for developer and educator that wanted to design a good PLS for SLD. Educators and developers can use the result to create a suitable customized app for students with disabilities in learning physical education.

FCM Clustering on Interaction Pattern Analysis of Chinese Language Learner Behavior.

In order to meet the needs of the current personalized education, improve the shortcomings of the current digital learning system in personalized learning, and introduce the service concept into education, the development and research of the personalized learning system based on the analysis of user behavior patterns has become the development of digital learning. This paper studies a language learning method, that includes obtaining the user's language learning interaction data to determine the user's language level: wherein, the user's language level data includes the user's initial language level and the current language level; initial learning model: according to the current language level, an adaptive algorithm is used to update the initial learning model, and the user performs linguistics further according to the updated learning model. In addition, in order to realize the clustering analysis of Chinese language online learning users' learning behavior, since the Fuzzy C-means (FCM) clustering results are easily affected by the selection of their initial cluster centers, a Harmony Search (HS)-FCM-based Chinese language learning user learning behavior clustering analysis is proposed. The participation dimension, focus dimension, regularity dimension, interaction dimension, and academic performance are selected as the analysis indicators of learning behavior. The learner level is divided into 5 levels, namely excellent, good, medium, qualified, and poor. Compared with HSFCM, and decision tree, it is found that the algorithm Improved HS (HIS)-FCM in this paper has higher clustering accuracy, faster convergence speed, and lower fitness, which provides new opportunities for learner level division and optimization of course learning.

A Study on E-Learning and Recommendation System

Introduction: Today’s technology and the internet are proliferating due to which information access is becoming easier for many people, and creating new challenges and opportunities in all fields, especially when working with education. For example, the e-learning education system can personalized in order to acquire knowledge level and learner’s requirements in a learning process. The learning experience, as per the individual learner’s goals, should be adopted. Background: In the current educational environment, e-learning is playing a significant role. For many researchers, it has become one of the most important subjects, as using e-learning whole education system would revolutionize. There are many areas of e-learning in which research work is going on, such as Mass Communication, Information and Technology (IT), Education and Distance Education. Objective: To meet the various needs of the learners such as talents, interests, goals and needs, an e-learning system has to design a personalized learning system by considering various educational experiences. Many methods such as ontologies, clustering, classification and association rules have used along with filtering techniques to enhance the personalization and performance of the learner. Methods: This paper presents a detailed review of literature of previous work that has done in e-learning area especially on recommendation system. Current research works on e-learning to discover the research developments in this discipline have discussed in this work. Conclusion: One of the vital functions of the current e-learning system is creating a personalized resource recommendation system. In this paper, we reviewed some crucial papers on both e-learning and recommendation systems. Future research work of this paper would be designing efficient and precise e-learning and recommendation system to deal with the problem of substantial personalized information resources and further e-Learning plays vital role in preventing virus spread during COVID-19 pandemic.

A New Web-Based Personalized Learning System Improves Student Learning Outcomes

Although there is increasing acceptance that personalization improves learning outcomes, there is still limited experimental evidence supporting this claim. The aim of this study was to implement and evaluate the effectiveness of an adaptive recommendation system for Singapore primary and secondary education. The system leverages users trace data and learning analytics to generate assessment worksheets customized to individual learner’s proficiency. Analysis of online data is used to measure students’ skill levels in specific knowledge domains and monitor their progress. Notably, online measurements correlate positively with offline academic outcomes. A randomized controlled trial conducted on forty-three primary school students revealed statistically significant improvement on academic performance of a group of students receiving personalized content over a control group using non-adaptive material. The authors conclude that the reported recommender system successfully helps students improve their academic achievements.

Construction of personalized learning and knowledge system of chemistry specialty via the internet of things and clustering algorithm

Construction of personalized learning and knowledge system of chemistry specialty via the internet of things and clustering algorithm