Personalized Instruction Research Articles

Optimization of English Classroom Interaction Models Incorporating Machine Learning

The optimization of English classroom interaction models incorporating machine learning represents a paradigm shift in language education, offering innovative ways to enhance student engagement, participation, and learning outcomes. With machine learning algorithms, educators can analyze various factors such as student proficiency levels, learning styles, and behavioral patterns to dynamically adapt classroom interactions in real-time. These models can predict and optimize the timing, content, and format of instructional activities, fostering more personalized and effective learning experiences.This paper explores the implementation of optimized interaction models and machine learning predictions in the English classroom to enhance student engagement, academic performance, and personalized instruction. Through the analysis of various tables and findings, key insights emerge regarding the effectiveness of student-centered learning, active learning strategies, differentiated instruction, and technology integration within the interaction model. Additionally, machine learning predictions offer valuable opportunities for personalized instruction based on predicted proficiency levels. The study demonstrates promising outcomes, but limitations such as sample size constraints and data quality issues must be addressed to ensure the reliability and applicability of the findings. Results indicate a 15% increase in student participation rates, a 12-point rise in average test scores, and a shift from high to moderate teacher intervention frequency. Additionally, machine learning predictions achieved an accuracy rate of 80%, with 8 out of 10 correct predictions. Aspect evaluation scores revealed high effectiveness in student-centered learning (9.2), formative assessment (9.3), and teacher facilitation (9.0). These findings contribute to enhancing teaching and learning practices, supporting educators in fostering more engaging and personalized learning experiences for students.

Student profiles based on in‐game performance and help‐seeking behaviours in an online mathematics game

AbstractWell‐designed online educational games can improve students' math knowledge, skills and engagement; however, more research is needed to understand how to formatively assess components of students' mathematical understanding and learning as students solve problems in online educational games. In this study, we examined how 7th‐grade students' (N = 760) in‐game performance (ie, strategy efficiency, mathematical validity of the first action, mathematical errors) and help‐seeking behaviours (ie, hint requests) correlated with their algebraic knowledge in an online mathematics game. The k‐means cluster analysis identified four groups of students based on their in‐game metrics, and some variabilities in their in‐game performance were found. Although hint requests were available, only a few students showed a high percentage of hint requests during their gameplay. The regression analysis revealed that students' in‐game performance and help‐seeking behavioural patterns explained a significant amount of variance in students' algebraic knowledge above and beyond their prior knowledge. Our results suggest that how students interact with the online learning game is related to their later math performance, and qualitative aspects of student behaviours captured using log data provide meaningful information to students' algebraic understanding. Practitioner notesWhat is already known about this topic Well‐designed online educational games can improve students' math knowledge, skills, and engagement. Several studies found that some in‐game metrics, such as in‐game progress or correctness of tasks, are positively associated with learning outcomes. However, many studies used simple metrics like the correctness of each task or the number of problems completed to measure students' performance in the game. Few studies measured more in the moment or qualitative aspects of student in‐game performance, for example, how efficiently students solved problems in the game. What this paper adds In‐game metrics measuring more in the moment and qualitative aspects of students' problem‐solving explained a significant amount of variance in students' algebraic knowledge above and beyond students' prior knowledge. Students' strategy efficiency and strategic planning were positively associated with students' math problem‐solving performance. Although hints were available for almost all problems, only a few students exhibited a high level of help‐seeking behaviours, and the students with the lowest in‐game performance who needed help the most showed low help‐seeking behaviours. Implications for practice and/or policy Classifying students into profiles could provide ways to identify students with different levels of problem‐solving skills and help teachers provide more personalized instruction. Teachers could use this information as a formative assessment to identify the core areas of intervention needed, particularly for students struggling with math concepts taught in the game. Instructional strategies that make hints more appealing or engaging to students might be needed.

TECHNOLOGY & PRODUCTIVITY AMONG EDUCATIONAL INSTITUTIONS INFORMATION COMMUNICATION AND TECHNOLOGY IN PUBLIC SECONDARY SCHOOLS IN MAKUENI COUNTY, KENYA

The primary objective of this research article is to identify the key impacts of integrating technology into public secondary schools in Makueni County. To achieve this, a descriptive research strategy has been employed, that brought in 140 teachers and 25 principals. Data collection was conducted via interview schedules and questionnaires that were aimed at administrators and teachers. The gathered data underwent quantitative and qualitative analysis. Descriptive statistics and mean calculations, a measure of central tendency, were utilized for data analysis. The research hypothesizes that Information and Communication Technology (ICT) plays a crucial role in enhancing educational quality. ICT enables more personalized instruction and immediate feedback, thereby improving literacy both inside and outside the classroom. The research article also examines how ICT can be used to foster creativity and higher-order thinking skills. In terms of administration, ICT is pivotal in streamlining tasks such as data collection, document processing, record-keeping, and financial document recovery, making retrieval more straightforward and administration more efficient. Furthermore, ICT aids in the efficient scheduling of school activities, including parents and teachers association (PTA) and Board of Management meetings, and managing school calendar events, classes, and exams. In a nutshell, the research article highlights the importance of ICT in disseminating information and fostering effective communication among stakeholders.

Application of Computer-Aided Technology in Teaching Spoken English

Computer-aided technology has revolutionized the teaching of spoken English by offering innovative tools and resources that enhance the learning experience. Through interactive software and multimedia platforms, learners can engage in immersive language exercises, receive real-time feedback, and access personalized instruction tailored to their proficiency levels. This paper introduces a groundbreaking approach for integrating computer-aided technology into the teaching of spoken English, utilizing Automated Probabilistic Markov Chain Machine Learning (APMC-ML). Recognizing the significance of effective communication skills in the modern world, particularly in the realm of English language proficiency, this research endeavors to enhance the pedagogical process through innovative technological means. The methodology involves the development and implementation of an interactive computer-aided system that leverages APMC-ML algorithms to facilitate language learning. This system encompasses various modules designed to cater to different aspects of spoken English acquisition, including pronunciation, vocabulary, fluency, and conversational skills. Through a user-friendly interface, learners are provided with personalized learning experiences tailored to their individual proficiency levels and learning preferences. The application of APMC-ML in teaching spoken English represents a paradigm shift in language education, offering a data-driven approach that complements traditional teaching methods. By harnessing the power of machine learning and probabilistic modeling, educators can optimize the learning experience, expedite skill acquisition, and promote greater language proficiency among learners.

Virtual Reality Technology in Practical Teaching Methods

This paper explores the integration of virtual reality (VR) technology into practical teaching methods, enhanced by the Whale Optimized Feature Extraction Recurrent Classification (WOFERC) model. The study aims to enhance the effectiveness and engagement of practical learning experiences through immersive VR simulations. Using simulated experiments and empirical validations, the efficacy of the WOFERC-enhanced VR teaching approach is evaluated. Results demonstrate significant improvements in student performance and engagement compared to traditional methods. For example, students using the WOFERC-enhanced VR platform achieved an average score increase of 20% in practical assessments. Additionally, the framework allowed instructors to optimize teaching strategies based on real-time student feedback, leading to more effective and personalized instruction. These findings highlight the potential of VR technology with WOFERC in transforming practical teaching methods and fostering immersive and impactful learning experiences.

Design and Implementation of English-Chinese Translation Teaching Platform Based on Deep Learning

The Translation Teaching Platform based on Deep Learning represents a groundbreaking advancement in language education, offering students immersive and personalized learning experiences. By leveraging deep learning algorithms, this platform facilitates real-time translation practice, allowing students to engage with authentic language materials and receive immediate feedback on their performance. Through interactive exercises, multimedia resources, and adaptive learning modules, students can enhance their language proficiency in a dynamic and engaging environment. Moreover, the platform's ability to analyze and adapt to individual learning styles enables personalized instruction tailored to each student's needs. With the Translation Teaching Platform based on Deep Learning, language educators can revolutionize their teaching methods and empower students to achieve fluency and proficiency in their target languages. This paper presents the design and implementation of an innovative English-Chinese Translation Teaching Platform based on Deep Learning, augmented by Embedding Feature Probabilistic Segmentation Classification (EF-PSC). The platform leverages advanced deep-learning techniques to provide students with immersive and interactive translation practice opportunities. Through simulated experiments and empirical validations, the efficacy of the platform is evaluated, demonstrating significant improvements in translation accuracy and learning outcomes compared to traditional methods. The EF-PSC model achieved an average accuracy rate of 91% in translating English to Chinese texts, outperforming conventional translation tools by 15%. Additionally, the platform's adaptive learning features enable personalized instruction tailored to each student's proficiency level and learning pace, leading to more effective language acquisition. These findings underscore the potential of deep learning with EF-PSC in revolutionizing translation teaching methods and enhancing language education.

Unveiling the dynamics of self-regulated learning in project-based learning environments

Project-based learning (PBL) has been found to exert a positive influence on learners' academic achievements, while also fostering their intrinsic motivation and playing a crucial role in nurturing sustainable learning capacities. Understanding individual differences in self-regulated learning (SRL) within project-based foreign language learning can guide language teachers in delivering personalized instruction. This study utilized a blended learning management system to collect and analyze data on SRL from 95 learners enrolled in a project-based College English course. Through microanalysis, latent profile analysis (LPA), and epistemic network analysis (ENA), the study identified distinct learner profiles and traced their developmental trajectories in project-based language learning. The findings revealed that PBL facilitates the occurrence of SRL behaviors and strengthens connections across different regulation phases. Notably, learners with diverse profiles displayed variations in their SRL epistemic network structures and developmental trajectories. These results highlight the dynamic nature of SRL within the PBL context, underscoring the significance of considering individual differences and supporting learners’ evolving self-regulatory behaviors and strategies throughout their engagement in PBL activities. To enhance SRL in PBL, educators are encouraged to provide scaffolding support, promote help-seeking behaviors, and implement interventions targeting metacognitive processes and reflective practices.

Reinforcement learning tutor better supported lower performers in a math task

Resource limitations make it challenging to provide all students with one of the most effective educational interventions: personalized instruction. Reinforcement learning could be a pivotal tool to decrease the development costs and enhance the effectiveness of intelligent tutoring software, that aims to provide the right support, at the right time, to a student. Here we illustrate that deep reinforcement learning can be used to provide adaptive pedagogical support to students learning about the concept of volume in a narrative storyline software. Using explainable artificial intelligence tools, we extracted interpretable insights about the pedagogical policy learned and demonstrated that the resulting policy had similar performance in a different student population. Most importantly, in both studies, the reinforcement-learning narrative system had the largest benefit for those students with the lowest initial pretest scores, suggesting the opportunity for AI to adapt and provide support for those most in need.

Research on the Application of Computer Music Technology in Solfeggio Teaching in Universities

This paper investigates the potential impact and benefits of integrating computer music technology into solfeggio teaching within university music programs. Solfeggio, as a foundational skill in music education, encompasses sight-singing and ear training, yet traditional teaching methods often encounter challenges in engaging students and providing personalized instruction. The paper explores the theoretical foundations of solfeggio teaching and the role of technology in music education, highlighting the potential advantages of incorporating computer music technology. Specific examples of technology applications, such as interactive software for ear training, digital music notation tools, and virtual reality applications, are examined for their potential to enhance student engagement and provide personalized feedback. Furthermore, the paper reviews case studies of successful technology integration in solfeggio instruction and presents empirical research findings on the impact of technology on learning outcomes. Pedagogical considerations for technology integration, including instructional design, assessment methods, and student engagement strategies, are discussed, along with recommendations for educators and institutions interested in adopting technology-enhanced solfeggio instruction. The paper emphasizes the broader significance of technology-enhanced music education and its potential to enhance students' aural skills and overall musical proficiency.

USE OF ARTIFICIAL INTELLIGENCE IN ENGLISH LANGUAGE TEACHING

In recent years, there has been a growing interest in the integration of artificial intelligence (AI) into various fields. Artificial Intelligence (AI) also has become a prominent technology in various fields, including education. One area where Artificial Intelligent (AI) has shown great potential is in English Language Teaching (ELT). With the rapid development of technology, Artificial Intelligent (AI) tools and applications have been integrated into language learning platforms, classrooms, and online resources, transforming the way English is taught and learned. In the realm of language teaching, Artificial Intelligent offers significant potential for enhancing the learning process and improving outcomes. English language teaching is no exception, as educators are exploring the potential of Artificial Intelligent (AI) to enhance language learning experiences. English language teaching is no exception, as Artificial Intelligent (AI) can enhance the learning experience for English language learners by providing personalized instruction, immediate feedback, and immersive language practice. This research addresses the extent to use artificial intelligence in English language teaching. Researcher used descriptive qualitative approach with questionnaire as technique of data collection. The results of this research are the Artificial Intelligence is needed in English language teaching. Artificial Intelligence also has negative and positive impact in English language teaching depend on utilized correctly and at the appropriate timing. Besides that, Artificial Intelligence also help students in improving their English skill like speaking, writing, and reading using product of Artificial Intelligence like Chat GPT, Quillbot, Grammarly, Plagiarism Checker, Paraphrasingtool.com. So, the use of Artificial Intelligent in English language teaching is very important or useful and also reducing the burden of teachers and improving teaching quality.

Insight into Two Education System: The Finland Phenomenon and A Journey to Guatemala

This article examines two documentaries related to the education system: The Finnish Phenomenon and Crossing the Bridge: A Journey to Guatemala. First, the article describes the mode of Finnish education, emphasizing the excellence of the Finnish education system, including teacher training and student choice. The article then explores the educational challenges in Guatemala described in Crossing the Bridge and how the Friendship Bridge program helps women lift themselves out of poverty and gain access to education through micro-credit and need-specific curricula. The author highlights the educational philosophy of allowing students to be creative and the importance of personalized instruction. This article provides contributions to International educators.

Opportunities and Challenges of AI in Vocational Education

In recent years, the rapid development of Artificial Intelligence (AI) technology has provided unprecedented opportunities for vocational education. This paper examines the value of AI in vocational education, using ChatGPT as a case study. It presents innovative application models, including assisted teaching and assisted learning, which can assist teachers in designing personalized instruction and providing on-demand support to students. These models serve as effective tools for the smart and personalized implementation of vocational education. However, caution is required to address risks such as data privacy and algorithmic bias. To ensure the responsible use of AI, particularly ChatGPT, collaboration among various stakeholders is essential. These efforts should focus on improving policies and regulations, establishing mechanisms for risk prevention and control, and promoting the harmonious coexistence and development of humans and machines. The proposed AI-driven model for intelligentization in vocational education offers valuable insights into empo wering vocational education with AI.

Personalized Gym Trainer Using Mediapipe

Abstract: With the growing demand in personalised fitness experiences, this paper takes a fresh look at home workouts by creating a Personalised Gym Trainer with the Mediapipe library. This sophisticated device combines pose detection technology with voice assistance to provide users with real-time feedback and personalised instruction while exercising. The system identifies various exercises accurately and leverages torso point detection for greater precision by leveraging the capabilities of the Mediapipe library and OpenCV for camera tasks. Individual Python modules for certain workouts such as pull-ups and bench press are written to support a flexible and scalable solution. Python, Git, GitHub, and Jenkins are among the tools and technologies used in the process. Furthermore, the Findpose library is used to calculate angles between torso locations, resulting in Providing a quantitative assessment of proper posture. The suggested Personalised Gym Trainer is a promising leap in home fitness solutions, integrating cutting-edge pose detection with voice assistance for an interactive and personalised workout experience.

A Systematic Review of the Role of Learning Analytics in Supporting Personalized Learning

Personalized learning is becoming more important in today’s diverse classrooms. It is a strategy that tailors instruction to each student’s abilities and interests. The benefits of personalized learning include students’ enhanced motivation and academic success. The average teacher-to-student ratio in classes is 1:15.3, making it challenging for teachers to identify each student’s areas of strength (or weakness). Learning analytics (LA), which has recently revolutionized education by making it possible to gather and analyze vast volumes of student data to enhance the learning process, has the potential to fill the need for personalized learning environments. The convergence of these two fields has, therefore, become an important area for research. The purpose of this study is to conduct a systematic review to understand the ways in which LA can support personalized learning as well as the challenges involved. A total of 40 articles were included in the final review of this study, and the findings demonstrated that LA could support personalized instruction at the individual, group, and structural levels with or without teacher intervention. It can do so by (1) gathering feedback on students’ development, skill level, learning preferences, and emotions; (2) classifying students; (3) building feedback loops with continuously personalized resources; (4) predicting performance; and (5) offering real-time insights and visualizations of classroom dynamics. As revealed in the findings, the prominent challenges of LA in supporting personalized learning were the accuracy of insights, opportunity costs, and concerns of fairness and privacy. The study could serve as the basis for future research on personalizing learning with LA.

English Teachers’ Perceptions and Practices of Differentiated Instruction (DI) in the Merdeka Curriculum

This research project investigated the use of DI by teachers and confirmed their practices to learn more about teachers' perceptions using a qualitative approach. Four research instruments were used to collect data in this investigation: document analyses, classroom observations, questionnaires, and interviews. Document analyses and classroom observations were employed to obtain data concerning teachers' actual practices of DI. In the meantime, a questionnaire has been used to obtain data on teachers' perceptions and practices regarding DI. Finally, interviews have also been conducted to determine teachers' perceptions of DI. The result of the study revealed that the participants exhibited high proficiency in using DI strategies in their classrooms. The assessment considered various dimensions, including understanding DI concepts and objectives, adapting instruction to diverse student needs, evaluating students through diagnostic assessments, and aligning teaching strategies with individual learning profiles. However, there was room for improvement, especially in tailoring instruction based on diverse learning profiles. Furthermore, the English teachers' perceptions of DI significantly shaped their instructional practices. While there was a solid theoretical understanding and commitment to personalized instruction, practical challenges and systemic constraints impacted the seamless integration of DI principles. The teachers' commitment to addressing these challenges, demonstrated through varied instructional practices, underscored the dynamic interplay between perceptions and the complex reality of implementing DI in the classroom.
 Keywords: English Teacher, Differentiated Instruction (DI), Merdeka Curriculum, Perceptions and Practices

Exploring the Perceptions of Teachers on the Utilization of Intelligent Tutoring System Introduction

School organization stands as a cornerstone in the holistic development of students, with its efficiency gauged by the relationship between inputs and corresponding outputs. In alignment with this principle, this study aims to bolster the role of educational technology, particularly the Intelligent Tutoring System (ITS), in elementary education. This descriptive-quantitative research endeavors to elucidate the perceptions of teachers regarding the integration and effectiveness of ITS in the educational landscape. The study employed a researcher-designed survey questionnaire to solicit feedback from educators regarding their experiences with ITS. A total of 80 teachers actively engaged with ITS participated in the survey. Data analysis was conducted using statistical methods, generating frequencies, weighted means, ANOVA, Kruskal Wallis Test, and Pairwise Mann-Whitney tests. The results uncovered a consensus among teachers across different subject areas and experience levels, indicating positive beliefs regarding the utilization of ITS. The findings suggest that teachers perceive ITS as a valuable tool for enhancing student learning outcomes, facilitating personalized instruction, and fostering a dynamic learning environment. Moreover, the study revealed that there were no significant differences in teacher perceptions based on subject area expertise or experience level, indicating a uniform belief in the potential of ITS across diverse educational contexts. The study further highlights the critical role of teachers in shaping the future of education through the effective integration of advanced technological tools like ITS. It underscores the importance of ongoing review and innovation in ITS implementation to ensure its continued impact on student learning and development. In an era marked by technological advancements and educational transformations, this research contributes to the broader discourse on leveraging intelligent tutoring systems to prepare informed and productive generations for the challenges of tomorrow's world.

Exploring the Transformative Role of Artificial Intelligence and Metaverse in Education: A Comprehensive Review

Introduction: this review paper provides a comprehensive examination of the applications and impact of artificial intelligence (AI) in the field of education. With advancements in AI technologies, the educational landscape has witnessed significant transformations. This review aims to explore the diverse AI techniques employed in education and their potential contributions to teaching, learning, assessment, and educational support. Objective: this research article aims to tracing the development of AI in education from its early beginnings to its current state. It highlights key milestones and breakthroughs that have shaped the field, including the emergence of intelligent tutoring systems and expert systems. Methods: the article provides a comprehensive overview of the various AI techniques utilized in education, such as machine learning, natural language processing, computer vision, and data mining. Each technique is discussed in detail, showcasing the algorithms, models, and methodologies used within each approach. Results: while the benefits of AI in education are substantial, the paper also addresses the challenges associated with its integration. Ethical considerations, privacy concerns, and the need for effective human-AI collaboration are discussed in-depth. Conclusion: this review underscores the transformative potential of AI in education. By harnessing AI technologies effectively and responsibly, educators and policymakers can unlock new possibilities for enhancing teaching and learning experiences, fostering personalized instruction, and driving educational advancement.

Distance Electronic Learning Strategy in Medical Teaching During the COVID-19 Pandemic: Cross-Sectional Survey Study.

Teaching hospitals have been regarded as the primary settings where doctors teach and practice high-quality medicine, as well as where medical students learn the profession and acquire their initial clinical skills. A percentage of instruction is now done over the internet or via electronic techniques. The present COVID-19 epidemic has pushed distance electronic learning (DEL) to the forefront of education at all levels, including medical institutions. This study aimed to observe how late-stage medical students felt about DEL, which was put in place during the recent COVID-19 shutdown in Jordan. We conducted a prospective, cross-sectional, web-based, questionnaire-based research study during the COVID-19 pandemic lockdown between March 15 and May 1, 2020. During this period, all medical schools in Jordan shifted to DEL. A total of 380 students responded to a request to fill out the questionnaire, of which 256 completed the questionnaire. The data analysis showed that 43.6% (n=112) of respondents had no DEL experience, and 53.1% (n=136)of respondents perceived the DEL method as user-friendly. On the other hand, 64.1% (n=164) of students strongly believed that DEL cannot substitute traditional clinical teaching. There was a significant positive correlation between the perception of user-friendliness and the clarity of the images and texts used. Moreover, there was a strong positive correlation between the perception of sound audibility and confidence in applying knowledge gained through DEL to clinical practice. DEL is a necessary and important tool in modern medical education, but it should be used as an auxiliary approach in the clinical setting since it cannot replace conventional personal instruction.

Examining the Implementation of Artificial Intelligence in Early Childhood Education Settings in Ghana: Educators’ Attitudes and Perceptions towards Its Long-Term Viability

Artificial Intelligence (AI) has witnessed significant advancements in recent years, with potential applications in various sectors, including education. Early childhood education (ECE) is a critical stage that lays the foundation for a child’s future development and learning. This study aims to explore the attitudes and perceptions of educators in Ghana towards implementing AI in ECE settings, focusing on its long-term viability. The research employs a qualitative approach, utilising interviews and focus group discussions to gather data from educators in selected ECE settings across four (4) early childhood settings in Ghana. The study investigates eight (8) educators’ experiences, concerns, and expectations regarding integrating AI technologies into their teaching practices. It explores the factors influencing their attitudes towards AI, including familiarity with the technology, pedagogical beliefs, cultural context, and training opportunities. Preliminary findings indicate a diverse range of perspectives among educators towards AI implementation in ECE settings. Some educators perceive AI as promising for enhancing teaching and learning experiences, providing personalised instruction, and facilitating early childhood development. They recognise the potential benefits of AI in supporting cognitive, social, and emotional growth among young learners. Concerns are expressed regarding the implications of AI on human interaction, child privacy, and the role of educators in fostering holistic development. This study contributes to the emerging field of AI in education by examining the context of ECE in Ghana. The findings informed policymakers, educational institutions, and AI developers about the perceptions and concerns of educators, ultimately guiding the development and implementation of AI technologies in a manner that aligns with the needs and aspirations of the Ghanaian ECE community.

The increasing demand for TOEFL tutoring among graduate students: Understanding the motivations behind it

Private extra academic support, also referred to as shadow education has experienced growth, in Asian nations, such as Indonesia. However, despite its use this type of education often receives attention from the government compared to formal schooling. This research aims to explore the motivations that influence university students’ choices to seek academic support specifically for TOEFL preparation—a vital step towards achieving an internationally recognized English proficiency score. Involving 60 university students for the data collection using questionnaire, this study identified factors that drive the demand for private TOEFL tutoring among Indonesian graduate students. Despite concerns about the absence of standardization and potential student exhaustion resulting from tutoring it continues to provide personalized instruction and assistance. This study highlights the role that private supplementary tutoring plays in meeting the growing demand for success and its impact on students’ academic and career paths, in Indonesia.