Personalized Learning Platform Research Articles

Transforming higher education for the digital age

In this study, I explore the transformative potential of artificial intelligence (AI) and emerging technologies in higher education, focusing on case studies and pedagogical innovations that are reshaping the learning experience. Through an in-depth analysis of key initiatives—such as Stanford University's AI-driven personalized learning platform, the AI chatbot implemented at the University of Murcia, Knewton's adaptive learning system, and the intelligent tutoring platform developed by Pai et al.—the study highlights how AI enhances learner engagement, customizes educational experiences, and improves academic outcomes. The research also critically examines the ethical challenges and policy considerations associated with AI integration in educational settings. It emphasizes the need for clear guidelines to ensure responsible and equitable use of AI, particularly in addressing issues of fairness, student welfare, and access. The paper concludes by calling for further research into the long-term implications of AI on educational equity and ethical standards in higher education.

The Design and Implementation of Personalized Learning Platform Based on Android

Through an investigation and analysis of learning platforms, the author designed and implemented the personalized learning platform based on Android, adhering to the design principles. The platform is customized to learners’ learning styles and ubiquitous learning. Learners may receive personalized recommendations from the platform and get optimized user experience, which in turn enhances learners’ motive and efficiency in learning and supports teachers’ teaching.

AI Enabled Personalized Learning Platform

The AI-powered individualized learning environment designed specifically for teaching programming. The platform makes it easier for users to log in, choose a domain, take interactive tests, assess their performance, and receive personalized video recommendations based on how well students perform. Understanding the value of individualized instruction in programming, the platform dynamically adjusts to each user's skill level and learning goals. Front-end and back-end technologies are seamlessly integrated to allow users to take quizzes specific to the domain they have chosen, get instant response, and get performance reviews. The website makes recommendations for videos based on users' skill levels. The architecture, functionality, user experience, implementation specifics, evaluation outcomes, and future directions of the platform are all made clear in this document. Through providing insights into AI-powered tailored learning, this study advances teaching methodologies and gives students more confidence when it comes to programming.

‘Someone else in the universe is trying to teach you’: teachers’ experiences with platformized instruction

ABSTRACT This article examines how teachers at one elementary school made sense of their instructional practice after their school adopted a personalized learning platform for teaching mathematics. Data includes interviews (n = 21) with 12 teachers and administrators as well as recordings of 18 hours of professional learning sessions. The study investigates how teachers’ perceptions of their instructional practice shifted as their interactions with students became increasingly mediated by platforms. Findings illuminate how platformization invited digitally mediated surveillance into the classroom and how this surveillance impacted teachers’ perceptions of their relationships with students and with content. The authors offer a framework of platformized instruction to guide research on teaching the digital era.

Transformation Research on the Teaching Mode of Ideological and Political Courses in Colleges and Universities under Big Data Environment

Abstract The application of big data technology in the teaching of ideology and politics courses in colleges and universities can provide strong support for the precise reform and innovation of ideology and politics work. In this paper, we first sort out the status quo and dilemma of the teaching of ideology and politics courses in colleges and universities and analyze the advantages and feasibility of big data technology in the learning of ideology and politics. Then, based on the big data streaming computing Spark framework, a personalized learning service platform for teaching Civics and Politics courses in colleges and universities was established. Aiming at the online learning behavior of students on the platform, the K-mean algorithm is used to select their behavioral characteristics. The personalized recommendation of learning resources for Civics and Political Science courses is implemented based on the learning behavior. Data was utilized to analyze the effectiveness of the personalized learning platform in transforming the teaching of Civics and Political Science courses in colleges and universities using S University as the research object. The study found that the number of focused learners in category 1 of the online learning behavior reached 303, and the teaching of the Civic and Political Science course based on the personalized learning platform helped the students’ will cultivation score increase by 2.49 points and more than 95% of the students recognized the platform. The customized service learning platform for teaching Civics courses established based on big data can realize the innovative transformation of Civics courses in colleges and universities and improve the teaching quality and level of Civics courses in colleges and universities.

Internet+ Teaching Model of Community-Based Elderly Education in the Context of Smart Cities

Abstract The rapid emergence of Internet+, artificial intelligence, virtual reality, and other innovative technologies has led to the gradual penetration of online learning into traditional teaching, and face-to-face teaching and online teaching have moved towards deep integration. This paper explores the Internet+ teaching mode of online and offline integration of community-based senior education in the context of a smart city and designs a personalized learning platform for community-based geriatric education. Subsequently, a learning path recommendation model based on two-dimensional features of learners and learning resources is constructed, and then the SASBPSO algorithm is used to realize model optimization and improve the online learning effect of community-based senior education, and the effect of the model application is analyzed. The SASBPSO algorithm is more reliable than other algorithms when solving optimal solutions and converges at less than 0.1, which is significantly superior to other algorithms in terms of convergence. The time used by elderly learners under the use of this paper method is significantly shorter than the traditional method, and the average learning time gap is smaller. The difference between the average scores of the pre-test and post-test of the two groups is −16.247 and −8.146, and the significance value of the difference is 0.003 and 0.016, respectively, 0.003<0.016<0.05, indicating that the learning effect based on personalized learning path recommendation is significantly higher than that of the traditional learning method. This paper provides feasible design ideas for the Internet+ teaching mode of community-based geriatric education.

Enhancing Online Learning Experiences through Personalization Utilizing Recommendation Algorithms

This research investigates the implementation and impact of personalized learning systems underpinned by advanced recommendation algorithms in the realm of online education. The study encompasses a diverse group of participants from various educational backgrounds and explores their interactions with the personalized learning platform. The key findings of this research are noteworthy. Participants who had access to the personalized learning environment exhibited a substantial increase in engagement, satisfaction, and learning outcomes compared to those in the control group. This signifies the transformative potential of personalized learning in online education. The research emphasizes the critical role of personalization in enhancing learner engagement and satisfaction. It highlights how learners actively engaged with the system, making use of personalized recommendations to tailor their learning experiences. Moreover, the study sheds light on the positive impact of personalization on learning outcomes, indicating that learners achieved higher academic performance when their learning experiences were customized to their needs and preferences. In addition to its benefits for learners, the research underscores the advantages of personalized learning for instructors. The system provided instructors with valuable insights into each learner's progress and challenges, enabling more targeted and effective support. While the study demonstrates the effectiveness of personalized learning, it acknowledges certain limitations, including a relatively limited sample size and short duration. Future research endeavors could involve larger and more diverse samples and extend the study duration to gain a more comprehensive understanding of the long-term effects of personalized learning. In conclusion, this research contributes to the growing body of literature on personalized learning in online education. It provides compelling evidence that personalized learning, facilitated by sophisticated recommendation algorithms, can significantly enhance the online learning experience. The findings offer insights for educators and institutions looking to integrate personalized learning features into their online platforms to improve learner engagement, satisfaction, and learning outcomes.

Retracted: Construction of Personalized Learning Platform Based on Intelligent Algorithm in the Context of Industry Education Integration

Retracted: Construction of Personalized Learning Platform Based on Intelligent Algorithm in the Context of Industry Education Integration

Construction of a personalized learning platform based on genetic algorithm for specialized education in industrial colleges in the context of industry-education integration

Abstract Under the progress trend of the information technology period environment of deepening study curriculum reform, personalized learning in specialized education of industrial colleges has become one of its key contents. This paper mainly studies the necessary system design based on characteristics of genetic calculation for specialized education in industrial colleges under industry-education integration. This paper first introduces the principle of genetic algorithm. It then proceeds to introduce in detail the shortcomings of The advantage of this calculation method is that the standard calculation tendency is unable to move forward in an area extremes, the iteration is relatively slow, and the low accuracy when dealing with optimization problems. Then the design and construction ideas of the personalized platform and the database design from Learners, educators (teachers), and administrators are compared by experimentally analyzing the data diversity and convergence of the three algorithms on the database. Experimentally, the diversity of the self-adaptive genetic algorithm is maintained at 0.8, and the diversity, iteration time, and convergence standard are better compared with the comparison subject. Using a self-adaptive genetic algorithm to evaluate and rank learners’ personality differences and to provide and recommend suitable learning methods for learners helps develop education and improve teaching quality and has great historical significance for China’s development and international status.

Construction of Personalized Learning Platform Based on Intelligent Algorithm in the Context of Industry Education Integration

In order to meet the personalized needs of different students and provide students with a variety of intelligent learning strategies and learning content, this research is aimed at the current lack of intelligent function design in the network teaching platform of colleges and universities. Based on the concept of integration of production and education, with the help of intelligent recommendation algorithm of reinforcement learning, a personalized learning platform based on intelligent algorithm is constructed. First, through the method of questionnaire survey to understand the students’ needs for personalized learning and the functions of online teaching platform, on this basis, build a personalized learning platform based on reinforcement learning and data mining intelligent algorithm. The system test results show that when the number of accesses is less than 2000, the CPU and memory resources of the system basically remain unchanged. When the number of accesses is less than 3000, the CPU utilization rate reaches 70%, and the performance decreases, but it can also ensure normal operation.

Construction of Personalized Learning Platform Based on Collaborative Filtering Algorithm

On the network service platform for vocational education, there are currently over 10,000 online courses. Learners face a challenge in selecting interesting courses from the vast resources available. Learners’ urgent need for personalized learning is becoming more apparent as educational informatization progresses. Personalized recommendation (PR) technology can aid personalized learning and increase learners’ learning efficiency significantly. This paper constructs a smart classroom model based on AI (artificial intelligence) by studying the connotation and characteristics of smart classroom in light of the current research status and trend of smart classroom at home and abroad. The merits of the recommendation system are determined by the recommendation algorithm used by PR system. This paper primarily focuses on developing a personalized learning platform based on the CF (collaborative filtering) algorithm, as well as conducting system requirements analysis, database design, functional module design, implementation, and testing on this foundation. Experiments are carried out to see if the optimized PR algorithm in the network learning platform is effective.

Integration of “Offline + Online” Teaching Method of College English Based on Web Search Technology

The change of social demand for English application-oriented talents has affected the transformation of the way of cultivating talents in College English. The traditional training mode of indoctrination has exposed more and more disadvantages, and the demand of new teaching mode is imminent. This paper first analyzes the current situation of College English learning platform in China. Then, it discusses the design and implementation of “Online + offline” College English learning platform based on Web. The overall structure and function of the database are designed in detail. The teaching platform provides students with learning tools, learning resources, communication platform, testing and evaluation functions, and can evaluate students’ learning behavior, learning process and learning effect. This paper traces and collects a large amount of data left by learners in learning college English courses, and analyzes learners’ learning habits, learning progress and learning effect. Finally, according to the learning big data, this paper customized a reasonable personalized learning platform and improved the online teaching personalized service system.

Investigating metalinguistic written corrective feedback focused on EFL learners\u2019 discourse markers accuracy in mobile-mediated context

Among a growing body of research that examined the contradictory role of written corrective feedback (WCF) in development of L2 writing accuracy, this study investigated the possible impact of focused metalinguistic WCF on discourse markers (DMs) in writing performance of an intact group of 42 Iranian English as a Foreign Language (EFL) learners over an eight-week period. In an authentic, situated, and personalized learning platform, giving and receiving WCF were made possible only through the mobile-mediated application of WhatsApp. Before participants wrote on selected elicitation topics, they had taken part in a 2004 version of Oxford Preliminary Test in order to be screened for their initial differences in writing performance. After receiving metalinguistic WCF on their scripts, participants were required to work on the coded feedback and try to eliminate the DM errors in their revised writing assignment. After collecting the scripts over an eight-week period, the content of written assignment was thematically analyzed using NVivo 21 Software for the additive, adversative, causal and temporal DMs, following Halliday and Hasan’s (Cohesion in English., 1976) typology. In a convergent mixed-methods design, the content analysis of the qualitative data showed a larger distribution of additive DM than adversative, causal, and temporal DMs in all participants’ written scripts. Exploring the possible impact of metalinguistic WCF on improving the DMs accuracy, analysis of the frequency count data with Statistical Package for the Social Sciences (SPSS) multivariate Chi-square test reported the fluctuation and unsystematic patterns of distribution for four types of DMs with no sign of significant long-term improvement in DMs accuracy after receiving metalinguistic WCF. These findings implied further research on practicing alternative WCF strategies focused on variety of error types in actual and virtual L2 writing environments.

Research on the Construction of Intelligent Learning System Based on Big Data

Internet technology and online learning are in a period of high integration. The learner-centered online learning environment is no more restricted by distance. Facing the large amount of learning resources on the Internet, learners often have no choice. The problem cannot be solved in time, so it is imperative to build an intelligent learning system that according to the characteristics of learners. This article analyses the design of an intelligent learning system based on big data, and analyses the key points of the fit between big data and personalized learning features. According to the design principles of intelligent learning system based on big data, it is divided into four categories: learning strategies, learning tools, learning resources, and learning activities. To build an intelligent learning system in three dimensions. Then the recommended mode of resources in the intelligent learning system is introduced in detail. According to the above design ideas of the intelligent learning system based on big data, a personalized learning platform is constructed, and the structure, functions, and development technology of the platform are elaborated.

Система персонализированного обучения в школьном образовании: проблемы внедрения и адаптации

In the context of global trends in the humanization of education, issues of differentiation, individualization and personalization of education are actively discussed. At the same time, the key question remains – how to preserve the individual capabilities, interests and needs of each student while maintaining collective learning formats? How to take into account the personal characteristics and capabilities of each when passing and mastering general education programs? One such solution was the PCBL personalized learning platform developed in the USA. Currently, the Khoroshevskaya school is introducing and adapting this platform to the Russian conditions of education. The article examines the specific case of implementing a system of personalized competency-based education in a separate school – what problems, barriers and difficulties are encountered in its implementation. The study is built in the logic of qualitative research on the basis of high-quality research interviews with the main participants in the educational process in the context of introducing a personalized learning system.

On Spoken English Phoneme Evaluation Method Based on Sphinx-4 Computer System

In oral English learning, HDPs (phonemes that are hard to be distinguished) are areas where Chinese students frequently make mistakes in pronunciation. This paper studies a speech phoneme evaluation method for HDPs, hoping to improve the ability of individualized evaluation on HDPs and help provide a personalized learning platform for English learners. First of all, this paper briefly introduces relevant phonetic recognition technologies and pronunciation evaluation algorithms and also describes the phonetic retrieving, phonetic decoding and phonetic knowledge base in the Sphinx-4 computer system, which constitute the technological foundation for phoneme evaluation. Then it proposes an HDP evaluation model, which integrates the reliability of the speech processing system and the individualization of spoken English learners into the evaluation system. After collecting HDPs of spoken English learners and sorting them into different sets, it uses the evaluation system to recognize these HDP sets and at last analyzes the experimental results of HDP evaluation, which proves the effectiveness of the HDP evaluation model.

An Asynchronous, Personalized Learning Platform―Guided Learning Pathways (GLP)

The authors propose that personalized learning can be brought to traditional and non-traditional learners through a synchronous learning platform that recommends to individual learners the learning materials best suited for him or her. Such a platform would allow learners to advance towards individual learning goals at their own pace, with learning materials catered to each learner’s interests and motivations. This paper describes the authors’ vision and design for a modular, personalized learning platform called Guided Learning Pathways (GLP), and its characteristics and features. We provide detailed descriptions of and propose frameworks for critical modules like the Content Map, Learning Nuggets, and Recommendation Algorithms. A threaded user scenario is provided for each module to help the reader visualize different aspects of GLP. We discuss work done at MIT to support such a platform.