Personal Learning Environments Research Articles

Effects and side effects of personal learning environments and personalized learning in formal education

Effects and side effects of personal learning environments and personalized learning in formal education

Artificial Intelligence Chatbots in Chemical Information Seeking: Narrative Educational Insights via a SWOT Analysis

Artificial intelligence (AI) chatbots are next-word predictors built on large language models (LLMs). There is great interest within the educational field for this new technology because AI chatbots can be used to generate information. In this theoretical article, we provide educational insights into the possibilities and challenges of using AI chatbots. These insights were produced by designing chemical information-seeking activities for chemistry teacher education which were analyzed via the SWOT approach. The analysis revealed several internal and external possibilities and challenges. The key insight is that AI chatbots will change the way learners interact with information. For example, they enable the building of personal learning environments with ubiquitous access to information and AI tutors. Their ability to support chemistry learning is impressive. However, the processing of chemical information reveals the limitations of current AI chatbots not being able to process multimodal chemical information. There are also ethical issues to address. Despite the benefits, wider educational adoption will take time. The diffusion can be supported by integrating LLMs into curricula, relying on open-source solutions, and training teachers with modern information literacy skills. This research presents theory-grounded examples of how to support the development of modern information literacy skills in the context of chemistry teacher education.

Effects Integrating Personalized Learning Environment On Students’ Achievement in Social Studies in Secondary Schools in Imo State

This study is aimed at determining the effects integrating Personalized Learning Environment on students’ achievement in social studies in secondary schools in Imo State, Nigeria. A Qusi-experimental research design with non-equivalent intact groups, pre-test and post- test was adopted in this study. The population of the study comprised the entireUpper Basic( Junior Secondary School) students of 94,963 with 47,481 male and 48,481 female in the 68 secondary schools in Owerri Zone 1 of Imo State. Purposive sampling technique was used as the sample techniques. 39 Jss 2 students (19 male and 20 female) in one school were in treatment one (personalised learning environment) group, while 43 Jss 2 students (21 male and 22 female in the school were in treatment two (lecture method) group. The instrument for data collection was Researcher Made Social Studies Achievement Test (RMSSAT). The items in the instrument were subjected to content and face validation by five specialists, three specialists from Educational Measurement and Evaluation and two from Educatonal technology. The reliability of the research instrument was established using Kuder-Richardson (K-20) and this gave a reliability coefficient of 0.86. Mean and standard deviation were used to answer the research questions while the hypotheses were tested using Analysis of Covariance (ANCOVA) F- test statistics at 0.05 level of significance was used to interpret the result. From data analysed, the findings from study are: Personal Learning environment is effective in the improvement of students’ academic achievement and also revealed that personal learning is more effective on the male students than on the female students. The study recommended among others that: since the use of personalized learning environment has been found to enhance achievement in social studies, teachers of social studies should employ this technologically enriched method to teach students in the classroom more ewspecially those abstract to topics.

The Digital Transformation of College English Classroom: Application of Artificial Intelligence and Data Science

A major step forward in educational technology is the application of Data Science additionally Artificial Intelligence (AI) into undergraduate English courses. Improving teaching approaches and student involvement in the context of English language acquisition is an important issue that this study seeks to address. Even though there have been great strides in educational technology, conventional English classes still have a hard time meeting the demands of their different student bodies and offering individualized lessons. This is a major problem that prevents English language training from being effective, according to the material that is already available. In this study, we provide an approach to this issue called English Smart Classroom Teaching with the Internet of Things (ESCT-IoT). Utilizing data science techniques, artificial intelligence (AI) algorithms, and Internet of Things (IoT) sensors, ESCT-IoT intends to provide a personalized learning environment that is both immersive and adaptable. The fuzzy hierarchical evaluation technique is used to determine the assessment's final result, which measures the smart classroom's instructional impact. To overcome the limitations of conventional education, ESCT-IoT gathers and analyses data in real time to give adaptive material, individualized feedback, and learning suggestions. There are noticeable benefits as compared to traditional methods of instruction when it comes to evaluation metrics like student engagement, learning outcomes, and teacher satisfaction. Furthermore, ESCT-IoT is excellent in encouraging active learning, improving language fluency, and boosting overall academic achievement, according to qualitative comments from both students and teachers.

Design and Implementation of an Intelligent Cross-Border E-Commerce English Learning Platform Based on a Knowledge Map

At present, most online education platforms still have problems such as single learning modes and loose knowledge structures. Using the knowledge map, the study employs a personalized adaptive intelligent adjustment strategy based on the structural expression of the CBEC English subject system. Firstly, the study uses the Scapy framework to crawl the subject knowledge data. Then use the LTP platform to process sentences containing multiple entities. Input the sentence into the dependency parser to analyze and extract the entity relationship. Finally, according to the relevance between knowledge points and topics in the knowledge map, the final learning path recommendation result is obtained. And cluster the similarity of curriculum content to build a complete curriculum system. Based on the above operations, a knowledge map-based smart learning platform for the CBEC English discipline has been designed and implemented to provide a smart, personalized learning environment for learners. According to the experimental analysis, the average satisfaction of learners with the learning platform designed by the study is 81.56%, which can meet the learning needs of learners and provide an excellent mobile learning environment for students.

Enhancing English Language Education Through Big Data Analytics and Generative AI

This research paper provides a comprehensive examination of the significant impact of big data analytics and generative artificial intelligence (GAI) on the field of English language education. Utilizing a meticulous framework rooted in the evolutionary network influence of big data, our study critically analyzes several aspects of student engagement, learning motivation, self-efficacy, and the existing disparities among learners. Our primary objective is to enhance students’ active participation, intrinsic interest, and self-confidence in the context of English language learning, thus advancing their overall linguistic competence. To achieve these objectives, our study systematically integrates the concept of practice education with a multidisciplinary approach, leveraging the power of big data analysis and GAI, and reveals profound insights into student learning behaviors, preferences, and personalized educational needs. We employ advanced techniques for meticulous data processing and interpretation, empowering educators to make data-informed decisions and tailor pedagogical strategies to meet the unique requirements of each student. This data-driven pedagogical approach not only facilitates the implementation of effective teaching methodologies but also effectively addresses the disparities stemming from diverse student backgrounds, thereby fostering a more inclusive and personalized learning environment.

Personal Learning Environment and Writing Performance: The Case of Jordanian Young EFL Learners

This study examines the potential effect of incorporating personal learning environment-based instruction on young EFL learners’ writing performance. A quasi-experimental design was used as two intact sixth-grade sections of twenty pupils each from Juffein Primary School for Boys in the Directorate of Education of the Koura District, Irbid (Jordan) constituted the sample of the research. With a toss of a coin, one section was assigned as the control group and the other as the experimental group. A ten-week instructional treatment based on the creation of individual personal learning environments was designed and implemented with the experimental group following proper validation procedures. The control group was instructed using the guidelines of the prescribed Teacher’s Book. A writing test was also designed, validated, and administered to all 40 participants before and after the treatment. The findings revealed that the experimental group outperformed the control group with statistically significant differences found between the two groups in both overall writing and writing features (viz., ideas, organization, sentence fluency, conventions, and legibility). Relevant recommendations and pedagogical recommendations are put forth.

Deep Learning for Real-time Affective Hand Gesture Recognition in EMASPEL

This research marks a transformative leap in personalized learning through real-time affective hand gesture recognition in EMASPEL (Emotional Multi-Agents System for Peer-to-peer E-Learning), an educational platform. Our deep learning model, a meticulously crafted ensemble of convolutional and recurrent neural networks, deciphers the unspoken language of emotions embedded within student gestures, accurately capturing both spatial and temporal patterns. This detailed emotional map empowers EMASPEL to tailor its interactions with exquisite precision, addressing frustration, nurturing curiosity, and maximizing student engagement. The impact is profound: students flourish in personalized learning environments, experiencing enhanced outcomes and a newfound connection to their educational journey. Teachers, equipped with real-time emotional insights, provide targeted support and cultivate a more inclusive, responsive classroom. Beyond gestures, we envision a future enriched by multimodal data integration, encompassing facial expressions, voice analysis, and potentially physiological sensors, to paint even richer portraits of student emotions and cognitive states. Continuous refinement through rigorous longitudinal studies will pave the way for deeper understanding and ensure responsible implementation. Ultimately, this research reimagines education as a dynamic ensemble of personalized learning, where technology serves as a bridge between teacher and student, unlocking not just academic success but a lifelong love of knowledge.

Educational Learning-Based Sign Language System Using Machine Learning

This study proposes an innovative approach to multicultural education by integrating Indian Sign Language (ISL) and American Sign Language (ASL) through Machine Learning (ML) techniques. By collecting and preprocessing high-quality video data of ISL and ASL, we aim to develop ML models capable of recognizing and generating signs in both languages. Through bidirectional transfer learning and cross-language representation learning, we seek to enhance the learning experience and address common challenges in sign language acquisition. Additionally, personalized learning environments and culturally sensitive design, informed by collaboration with Deaf communities in India and America, ensure inclusivity and accuracy. Evaluation metrics and ethical considerations are integrated into the development process to promote responsible implementation and continuous improvement. Ultimately, this project aims to lay the groundwork for advancing multilingual sign language education globally. By employing advanced ML techniques, this study aims to bridge the gap between Indian Sign Language (ISL) and American Sign Language (ASL) education, fostering inclusivity and accessibility in learning environments. Through meticulous data collection, preprocessing, and collaborative development processes, our approach emphasizes accuracy, cultural sensitivity, and personalized learning experiences. By engaging with Deaf communities in both India and America, we ensure the authenticity and relevance of our platform. Evaluation metrics and ethical considerations are prioritized to uphold privacy, consent, and fairness principles. By establishing a robust foundation for multilingual sign language education, this project contributes to broader discussions on leveraging ML for enhancing accessibility and inclusivity in education systems worldwide. Keywords- Hand Gesture, Sign language Recognition, OpenCV, Media-pipe, tensorflow.

Personalized strategies for academic success in learning anatomy: Exploring metacognitive and technological adaptation in medical students.

Personalization of learning is an educational strategy rooted in metacognition and is significant in academic training. This is especially true in medical contexts. This study explored the relationship between the metacognitive profile of students of human anatomy, the classification of questions according to their difficulty, and the anatomical domain. It also covered the integration of educational technologies to create personalized learning environments. The identification of metacognitive profiles ("Active", "Pragmatic", "Theoretical", and "Reflective") has been highlighted as a critical influence on students' responses to different pedagogical approaches. Personalized adaptation based on these profiles has shown potential for improving grades and increasing student satisfaction and engagement with learning. The results revealed variations in student performance in relation to different pedagogical approaches, learning units, and evaluation modalities. The "Experience" evaluation modality, personalized according to metacognitive profiles, level of competence, and learning objectives, resulted in higher average scores. However, there was significant variability in the results. Those findings confirm the effectiveness of metacognitive adaptation in improving academic performance. Furthermore, they provide a solid basis for formulating personalized and effective pedagogical strategies in medical education. They recognize the influence of metacognitive profiles on student performance and contribute to advancing medical pedagogy.

How to study - supporting university students in their design of digital Personal Learning Environments

How to study - supporting university students in their design of digital Personal Learning Environments

Exploring autonomy support and learning preference in higher education: introducing a flexible and personalized learning environment with technology

Learner autonomy is particularly important in higher education, where students are fully responsible for their own learning. Despite this, there is a lack of research on aspects of autonomy support in higher education compared with that of primary and secondary education. To address this gap, this study explored autonomy support and learning preference in higher education, introducing a flexible and individualized learning environment with technology after the COVID-19 pandemic. A survey was conducted among 849 Japanese university students to gather their perceptions about autonomous support, learning preferences (face-to-face or distance), use of learning strategies, and academic performance (grade point average). Correlations were identified between certain variables: for example, perceived autonomy support, class format preference, and grade point average. The scores of different scales were compared among the subject groups. Autonomy support provided by instructors included explaining the lesson’s outline to students. A few teachers provided opportunities for students to select learning methods, teaching materials, and assignment content; yet, few instructors seemed to truly understand students’ learning needs. Although many participants took face-to-face courses, students’ learning preferences were evenly split between face-to-face and distance learning courses. Students who strongly preferred face-to-face learning performed well regardless of instructional format. Students with a little preference for distance education performed well, especially in distance education courses. Students with no preference performed the worst. This study suggests that a learning environment that provides students with options to suit their diverse learning preferences is beneficial and that introducing “hybrid-flexible” courses and feedback for students’ learning strategies has the potential to promote learner autonomy in higher education.

Education Inequality Between Rural and Urban Areas in China

Educational inequality is one of the significant societal challenges currently facing China. In particular, the educational disparity between rural and urban areas constrains rural students' development and opportunities and negatively impacts overall socioeconomic progress. Through a comprehensive study, this article seeks to elucidate the definition of developmental imbalance, understand the underlying causes of such disparity, and delve deeper into the roots, effects, and solutions of educational inequality between rural and urban areas in China. It also explores the potential of online education (MOOCs) as a solution. This paper summarizes and highlights the educational inequality problems in urban education in terms of enrollment indicators and school funding support. Through data investigation and analysis, we can discover the causes of educational inequality in implementing compulsory education and the issue of academic immigration. People have also found innovative education models such as MOOCs can provide rural students with more educational opportunities and resources. However, challenges related to interactivity, personalized learning environments, and communication between learners and teachers must be addressed. The paper also acknowledges the need to tailor MOOC course design to meet students' needs and improve learning outcomes in different educational contexts.

Assessment of Generic Competencies in Multicultural and Pluri-ethnic Contexts through Personalized Learning Environments

Este trabajo de investigación propone un modelo actualmente inexistente de Formación y evaluación de competencias genéricas (Saber Pro) en el ámbito universitario colombiano a través de entornos personalizados de aprendizaje, sobre la base de los planteamientos teóricos en cuanto a la exegesis del aprendizaje y la asignación taxonómica de niveles de complejidad cognitiva ascendente en contextos multiculturales y pluriétnicos, propuesta por SOLO. La muestra de la población objeto de estudio es de 14 000 estudiantes, los cuales cohabitan en el alma máter de la Universidad de La Guajira (Colombia). Se utiliza la encuesta como técnica y el diseño de un instrumento de evaluación por competencia genérica, estructurados en ítems, bloques y cuadernillos. Para analizar las repuestas registradas en los instrumentos se utiliza la Teoría de Respuesta al Ítem (TRI), así como la Estadística Descriptiva para identificar las dificultades específicas más iterativas de los estudiantes tras ser evaluados en las competencias genéricas preestablecidas por el Icfes. Finalmente, las conclusiones infieren la clasificación (taxonómica) ascendente de niveles de complejidad cognitiva al utilizar Entornos Personalizados de Aprendizaje en el proceso de Formación y Evaluación; Educación individualizada al contexto, aptitudes e intereses del discente a través de herramientas de Aprendizaje automático (“Machine learning”).

The puzzle of hybrid teaching: active methodologies and public education in transformation

This study, conducted in the past, addressed the challenge of adapting the school curriculum to the demands of the 21st century, considering the growing influence of digital technologies. The central problem was to effectively integrate educational technology and active methodologies into the curriculum to promote more relevant education. The general objective was to analyze this relationship and identify ways to make education more meaningful. The methodology involved an  literature review, analyzing works by various authors and academic research. The results revealed that technology is essential in promoting active learning and curriculum transformation. The integration of digital technologies creates dynamic and personalized learning environments, encouraging student engagement. Active methodologies, such as project-based learning, benefit from this integration. The final considerations emphasized the need to rethink the school curriculum for student-centered, active, and collaborative education. Investments in infrastructure and teacher training are essential. Flexibility is importante to adapt approaches to the specific needs of each context.

New psychological education: Leveraging the power of ChatGPT and artificial intelligence

Due to various reasons, the fact is that psychological problems are becoming increasingly serious worldwide. In recent years, Artificial Intelligence (AI) and chatbot have developed rapidly. With the advancement of natural language processing, AI driven chat robots have become an innovative tool that can enhance learning experiences and provide support in various fields. In the field of psychological education, ChatGPT is expected to promote interactive and personalized learning environments. It is possible and necessary to try using them for psychological education. This article analyzes the application of chatgpt in psychology and how AI is used in psychological counseling. These chat robots can simulate conversations similar to humans, allowing students to participate in the conversation and receive immediate feedback. In addition, their ability to adjust their reactions according to personal needs promotes autonomous learning and cultivates students' autonomy. Subsequently, this article outlines the specific applications of ChatGPT in psychological education, including mental health support, cognitive assessment, and virtual reality therapy. Finally, this article discusses some of the disadvantages of ChatGPT, such as appearing correct but actually having the wrong answer. In summary, this article discusses the potential of ChatGPT and artificial intelligence in psychological education. With interactive and personalized learning experiences, AI driven chat robots can supplement traditional teaching methods and empower students in their educational journey. However, in order to ensure the responsible and effective integration of artificial intelligence in psychological education, careful implementation and continuous evaluation are necessary.

The impact of a Personal Learning Environment on Chinese postgraduates’ online self-regulated learning skills / Impacto de un Entorno Personal de Aprendizaje en las aptitudes de aprendizaje autorregulado en línea de estudiantes de posgrado en China

This study investigated the impact of Personal Learning Environments (PLE) on the development of self-regulated learning (SRL) skills among postgraduate learners in China. 213 participants, divided into an experimental group ( n = 113) using a researcher-designed PLE platform and a control group ( n = 100) employing face-to-face instruction, joined a course on International English Language Testing Systems (IELTS). A mixed-methods approach was adopted, utilizing the Online Self-Regulated Learning Questionnaire (OSLQ) to explore SRL experiences, and weekly goal-achievement reports. Results demonstrated improvement in SRL skills in both groups, with the experimental group exhibiting significantly greater gains. Quantitative and qualitative results revealed that participants excelled in goal fulfillment but struggled with time management, help-seeking and self-evaluation skills. The study confirmed the positive influence of the PLE on postgraduates’ SRL skills, with implications for online learning design and the potential for PLEs to effectively promote SRL skills in online learning contexts.

The influence of the personal learning environment (PLE) approach on english speaking ability

The aim of this research is to determine the effect of the Personal Learning Environment (PLE) Approach on English Speaking Ability. The concept of the Personal learning environments (PLE) approach will make learning individuals become a subject. Each person is both a teacher and a student who can decide the direction of the learning. The method used in this research was an experimental method using two class groups. The research instrument used was a written test. Based on the results of data analysis, it can be concluded that the average score for English speaking skills in the control class is 53.7. Furthermore, in testing the t test hypothesis, a sig of 0.00 was obtained. Based on the conclusion, if the sig (2-tailed) value is <0.05, then there is a significant difference. Meanwhile, the average value of English speaking skills for the experimental class is 80.1. So, it can be concluded that there is an influence.

Problems and Development Strategies of Music Education in Primary and Secondary Schools Based on Network Information Technology

Music education has traditionally followed a one-size-fits-all approach, often failing to address the diverse needs and preferences of individual students. A recommender system for music education is a specialized software or algorithm that helps students, educators, or music enthusiasts discover and access relevant music content, learning materials, or educational resources. This paper introduces the Graph Theory-based Information Technology(GTIT) music learning system, a revolutionary platform designed to personalize music education. The GTIT system leverages advanced technology, data-driven recommendations, and social learning networks to enhance the musical journey for students. The proposed GTIT model uses the graph theory-based music education model for the estimation of the data features with information technology. The proposed GTIT model comprises the stacked recommender model for the classification of the appropriate features in the music education data. The data for the analysis of the proposed GTIT model is collected for the existing sources. With the extracted features the recommender system is designed for the classification and recommendation of the features related to the music education. The proposed model explores the key components of the GTIT system, including the assessment of students' musical preferences, skill levels, and learning patterns. These factors are analyzed to provide tailored music recommendations that keep students engaged and motivated. Additionally, the GTIT system employs graph theory to create connections among students, fostering collaboration and social learning. Simulation results reveals that students who engage with the learning system experience significant improvements in their musical skills and knowledge. The personalized recommendations and collaborative learning environment contribute to enhanced skill development and musical progress. Moreover, the sense of community established by the system encourages peer interaction and mentorship. This paper provides a comprehensive overview of the GTIT music education system's impact on students' learning experiences, skill development, and collaborative efforts. The analysis expressed the transformative potential of personalized recommendations and networked learning environments in music education. The GTIT system serves as a model for the future of music instruction, offering a promising approach for educators and institutions seeking to provide engaging and personalized music education.

Formal and informal foreign language learning at university: blurring the boundaries

ABSTRACT This paper is concerned with the integration of informal language learning into university language modules in a foreign language environment. It begins by exploring the evolving nature of formal and informal language learning. This is followed by analysis of a pedagogic innovation intended to integrate foreign language activities in an informal university space shared by domestic and international students into formal German and Spanish language modules in an anglophone context. The initiative is evaluated within an action research framework using the following research instruments: student attendance data, reflective reports, end-of-module student surveys and a collaborative debrief with two language tutors. The results indicate that the explicit integration of informal language learning into formal language modules increases student engagement with informal language learning, provides opportunities for oral practice in particular, and supports the development of language learner autonomy. While care must be taken to ensure that the informal nature of the informal language learning experience is retained using low stakes assessment and a fluid, flexible, student-led pedagogical approach, the findings indicate that the explicit integration of informal language learning activities into formal language learning has the potential to expand the personal learning environment of the foreign language learner to their benefit.