Personal Learning Environments Research Articles

Designing branching scenarios to support clinical reasoning in dental education

Branching scenarios are educational tools where dental students can explore their decision making in a clinical environment without risking harm to patients. By incorporating productive failure, adaptive learning and serious gaming elements into their design, they offer interactive and personalised learning environments. We aimed to design a branching scenario based on a patient presenting with dental pain and evaluate its implementation in a classroom setting. We employed a validated serious gaming model as the framework for our design and mapped a decision tree on a scalable whiteboard for the case. A survey platform was used to build the scenario using rule-based logic. The scenario included multiple ‘learning points’ with tailored feedback. Students can track how many learning points they have ‘unlocked’ and are encouraged to revisit previously uncovered pathways. Classroom observations, learning analytics and student feedback have been collected to evaluate the activity. Preliminary feedback has informed improvements to the branching scenario design and educators have found the learning analytics can be used to tailor future teaching sessions. Future steps include useability testing and design improvements based on student feedback.

Exploring the Perception of the Impact of Personal Learning Environments (PLEs) in the Generative AI Era in Engineering Education

Objective: This study explores students' perceptions of using Personal Learning Environments (PLEs) in the era of generative artificial intelligence as a learning resource in higher education. The specific objectives are: 1) to determine the current tools that students incorporate into their PLEs and 2) to identify students' perceptions of the use of PLEs for lifelong learning. Theoretical Framework: UNESCO and other institutions recognize lifelong learning as an essential competency for personal and professional development. Personal Learning Environments (PLEs) are considered effective tools for promoting self-directed learning, allowing students to manage their learning processes, especially with the advent of generative artificial intelligence technologies. Method: A qualitative study was conducted with 207 first-semester engineering students from a public university in Peru. Participants were introduced to a training program to develop their PLEs using various digital tools, including generative AI tools such as ChatGPT DALL-E. The instrument used to evaluate students' perceptions was the MUSIC model (Jones, 2009), which focuses on empowerment and usefulness. Results: The results indicate that more than 80% of the students positively perceive PLEs, considering them valuable and empowering tools for achieving their learning objectives. Integrating generative AI tools into PLEs is notable, as it provides a dynamic and personalized learning experience. Most students found PLEs helpful for their future and relevant for promoting lifelong learning skills. Research Implications: The research suggests that PLEs, especially when integrated with generative AI tools, positively impact self-directed learning and promote collaborative and social learning. These findings can inform the development of educational programs and policies that encourage using PLEs as a student-centered learning strategy. Originality/Value: This study contributes to the understanding of the role of PLEs in the context of higher education and generative artificial intelligence. It is one of the first studies to examine students' perceptions of using PLEs with generative AI tools, highlighting their potential to enhance learning autonomy and personalization.

The Impact of AI on Personalized Learning and Educational Analytics

The rapid advancement of artificial intelligence (AI) has revolutionized personalized learning and educational analytics, presenting new opportunities and challenges for adaptive education. This paper explores the impact of AI-driven technologies in creating personalized learning environments by examining how adaptive algorithms and data analytics shape educational experiences. The primary objective of this study is to assess the effectiveness of AI in enhancing learner engagement and outcomes through tailored instructional methods. Utilizing a mixed-method approach, this research gathers quantitative data from learning management systems to analyze engagement metrics, while qualitative insights are derived from interviews with educators and students. The findings indicate that AI-driven personalized learning significantly improves both student motivation and academic performance by adapting content to individual learning needs. Moreover, educational analytics enabled by AI offer educators critical insights into student progress, enabling proactive intervention and support. However, the study also highlights concerns regarding data privacy and the potential over-reliance on AI technologies in educational settings. These findings suggest that while AI holds transformative potential, a balanced approach is necessary to integrate technology with traditional teaching methods to ensure optimal educational outcomes. The study concludes that AI can serve as a powerful tool in enhancing personalized learning and educational analytics, provided that ethical considerations and data security are prioritized.

Large language models for whole-learner support: opportunities and challenges.

In recent years, large language models (LLMs) have seen rapid advancement and adoption, and are increasingly being used in educational contexts. In this perspective article, we explore the open challenge of leveraging LLMs to create personalized learning environments that support the "whole learner" by modeling and adapting to both cognitive and non-cognitive characteristics. We identify three key challenges toward this vision: (1) improving the interpretability of LLMs' representations of whole learners, (2) implementing adaptive technologies that can leverage such representations to provide tailored pedagogical support, and (3) authoring and evaluating LLM-based educational agents. For interpretability, we discuss approaches for explaining LLM behaviors in terms of their internal representations of learners; for adaptation, we examine how LLMs can be used to provide context-aware feedback and scaffold non-cognitive skills through natural language interactions; and for authoring, we highlight the opportunities and challenges involved in using natural language instructions to specify behaviors of educational agents. Addressing these challenges will enable personalized AI tutors that can enhance learning by accounting for each student's unique background, abilities, motivations, and socioemotional needs.

Enhancing Student Engagement with AI-Driven Personalized Learning Systems

This paper explores the impact of AI-driven personalized learning systems on enhancing student engagement in educational settings. With the increasing integration of artificial intelligence (AI) in various sectors, education is also experiencing a shift towards more adaptive and personalized learning environments. The study investigates how personalized learning paths, powered by AI algorithms, can address diverse learning needs and promote greater involvement from students. Through a comprehensive analysis of engagement metrics, pre-and post-implementation comparisons, and surveys from both students and educators, this research identifies key factors that contribute to improved student motivation, interaction, and academic performance. The findings suggest that AI-driven systems not only provide tailored learning experiences but also foster a deeper connection between students and their learning content. The paper concludes with recommendations for future research and practical applications in educational institutions to further optimize the use of AI for enhancing student engagement.

Tailoring Education: A Comprehensive Review of Personalized Learning Approaches Based on Individual Strengths, Needs, Skills, and Interests

Background and Aims: Personalized learning is important because it tailors educational experiences to each student's needs and strengths, resulting in more effective and engaging learning. This approach not only improves academic performance but also accommodates students' learning styles and interests, resulting in increased motivation and success. This paper investigates Tailoring Education: A Comprehensive Review of Personalized Learning Approaches Based on Individual Strengths, Needs, Skills, and Interests. Methodology: This paper conducted a thorough analysis of various personalized learning approaches, such as adaptive learning technologies, differentiated instruction, and project-based learning. It combined findings from empirical studies, case studies, and theoretical frameworks to assess the efficacy and impact of these approaches on individual student needs and academic outcomes. Results: The finding found that personalized learning approaches demonstrate their significant potential for meeting individual student needs and improving educational outcomes. Adaptive learning technologies and differentiated instruction have successfully tailored learning experiences to students' strengths, whereas competency-based and project-based learning provides flexible, student-centered pathways for meaningful engagement and academic progress. Collectively, these methodologies help to create a more individualized and responsive educational environment. Conclusion: The findings show that personalized learning approaches effectively meet individual student needs while improving educational outcomes. These methodologies foster a more responsive and personalized learning environment by tailoring experiences to students' strengths and providing flexible, student-centered pathways.

A Self-Determined Learning Approach: Enhancing Workplace Adaptability for Overcoming Inequality

A new era of student-centered learning, fueled by the power of digital tools and advanced teaching methods, means that classrooms are no longer entirely defined by four walls and a teacher at the leadership. At the same time, the educational landscape of African countries is very slowly adapting to these changes: the traditional face-to-face approach to learning dominates, student groups are overcrowded, and there is a significant gap in digital skills between students from urban and rural areas. Today, the development of online education becomes an important prerequisite for addressing the problem of educational disparity, a tool for meeting individual educational needs, increasing students’ motivation and responsibility for their learning outcomes. The Self-Determined Learning Approach allows for a personalized learning environment in which students can choose what, how and when they learn, work at their pace, choose or receive learning tasks in an order that makes sense to them, which allows for the individual interests of each student, learning style, cultural identity, life experience and personal challenges. The research is based on the results of in-depth interviews with students aged between 25 and 37 years with work (2‒5 years) and study experience, who fit the profile of self-determined learners, have part-time distance learning experience, are employed in different positions and are currently studying different programs, and are also registered with one of the three Distance Education Institutions. All participants are in their final year of study and have experienced digital transformation in distance education. This qualitative study is based on a constructivist paradigm employing a phenomenological strategy to explain the perceptions of students regarding how self-determined practices are applied at their institutions. The phenomenological approach aimed to capture the lived experiences of these students, focusing on their descriptions and interpretations of encountering self-determination practices in their educational environment. The results of the study confirmed the value of a self-determined approach to learning and its positive impact on enhancing students’ workplace adaptability. However, at the same time, the survey revealed that African higher education institutions are not ready for the effective implementation of self-determined learning technologies, the main reasons being the lack of technology and the unpreparedness of teachers.

How does generative AI promote autonomy and inclusivity in language teaching?

Abstract In recent years, artificial intelligence (AI) has increasingly made advances across various fields, including ELT. In this article we embrace an egalitarian dialogue framework to explore and understand the role of AI in language education. This format provides a useful backdrop for navigating the dual responses—enthusiasm and scepticism—by which AI’s influence in education has often been characterized. We critically assess both the advantages and limitations of AI in ELT, highlighting and praising how AI-powered tools can assist educators in differentiating instruction, promoting learner autonomy, and creating personalized and inclusive learning environments, thus making education more accessible to students with diverse and individual needs. However, the article also addresses the challenges posed by the digital divide, particularly the inequities in access to (AI) technologies in low-socioeconomic-status regions. We also reflect upon how AI can become an obstacle in developing learner autonomy due to the risks associated with overdependence on such technologies and the lack of critical skills. The article concludes that although AI presents exciting opportunities for advancing and facilitating language learning, its integration must be approached with a rational mindset to ensure that it serves to bridge educational gaps rather than intensify them.

Student perspectives on optimising AI tools to enhance personalised learning in higher education

This explanatory qualitative paper discusses students’ recommendations on how AI tools can be optimised to enhance personalised learning in higher education. There are several obstacles to the successful adoption and application of AI technology, two of which are user acceptance and striking a balance between AI-assisted and conventional teaching techniques. The Technology Acceptance Model is used in this research as a theoretical framework to analyse how users accept and use technology. It makes the case that users’ acceptance of technology is mostly influenced by their perceptions of its usefulness and ease of use, which can direct the creation of strategies to enhance the application and efficacy of AI technologies in individualised learning. Open-ended questionnaires were given to 40 University of the Free State students from different faculties as part of a qualitative explanatory case study methodology. The findings reveal that both students and lecturers need to be trained in using AI tools and that there should be a balance between using AI tools and traditional teaching methods to enhance personalised learning in higher education. Considering the findings, the study suggests that institutions and lecturers need to address the challenges posed by AI tools immediately and leverage AI to its full potential in creating an effective and personalised learning environment by establishing clear ethical guidelines and policies for AI usage in higher education and implementing comprehensive AI literacy programs for lecturers and students to ensure they understand the capabilities, limitations, and ethical considerations of AI tools.

The Effects of a Personal Learning Environment on Vietnamese EFL Undergraduate Students’ Speaking Skills

The personal learning environment (PLE) is a relatively new, learner-centred pedagogical approach using technology for learning. However, empirical research on PLEs remains in its infancy, especially in EFL contexts. This study therefore aimed to explore the effects of a PLE, designed by the researcher, on Vietnamese EFL undergraduates’ speaking skills and examine how these effects varied across different speaking proficiency levels. Forty students at a university in Vietnam participated in a research project lasting eight weeks utilizing the PLE for an English speaking course. Adopting a mixed-methods, quasi-experimental design, this research employed pre- and post-speaking tests, reflective journals, and semi-structured interviews to collect data. Data were analyzed using paired-samples t-tests, one-way ANOVA, and thematic analysis. The results revealed that the PLE enhanced students’ English speaking skills significantly. In addition, this enhancement varied across speaking proficiency groups, with the medium-level group experiencing the most considerable progress, followed by the low-and high-level groups. Qualitative findings from reflective journals and interviews highlighted students’ positive perceptions of using the PLE to enhance their speaking skills. In short, this research underscores the potential of using the PLE approach in developing students’ English proficiency and provides insights for educators and policymakers to employ autonomous language learning pedagogies.

INTEGRATING TECHNOLOGY IN ELEMENTARY EDUCATION: ENHANCING STUDENT ENGAGEMENT IN THE DIGITAL AGE

This paper explores integrating technology in elementary education to enhance student engagement and differentiated instruction. By leveraging digital tools such as tablets, laptops, and interactive software, educators can create dynamic and personalized learning environments that move beyond traditional methods. The study reviews various research findings, highlighting the benefits of technology in increasing student engagement, fostering flexible learning spaces, and promoting higher cognitive and interactive involvement. It also examines the role of technology in supporting differentiated instruction, enabling teachers to tailor their approaches to meet diverse student needs. Furthermore, the paper discusses the shift towards student-centered learning, emphasizing the importance of aligning curricula with digital resources to engage students actively in their learning process. Key strategies for designing digital-age learning experiences and assessments are presented, showcasing real-world applications that connect students with peers and experts globally. The paper concludes by emphasizing the need for ongoing professional development for teachers to adapt to the evolving digital landscape, ultimately preparing students for the demands of the digital age.

ADVANCING PERSONALISED LEARNING IN DISTANCE EDUCATION AT THE UNIVERSITY OF BUEA: A PATH FORWARD FOR A MASTERS PROGRAMME IN NURSERY AND PRIMARY EDUCATION

The realm of nursery and primary education in Cameroon has witnessed the introduction of novel curricula aimed at fostering competencies among learners, particularly accentuating Science, Technology, Engineering, and Mathematics (STEM) education. This paradigm shift underscores the pressing demand for educator’s adept at navigating and fully leveraging these curricular frameworks to facilitate the acquisition of knowledge, skills, and attitudes requisite for the learners' holistic development. Concurrently, the enactment of the National Development Strategy 2020-2030 (NDS30), geared towards propelling Cameroon towards emergent status by 2035, serves as a resounding call for concerted efforts across all sectors, including education, to realise the stipulated objectives. Consequently, there is an urgent imperative for a cadre of high-calibre educators equipped with the specific knowledge, skills, and competencies necessary to yield the desired educational outcomes. Such educator proficiency can only be cultivated through rigorous professional programmes that address contemporary challenges and foster avenues for lifelong learning. It is well-established that higher levels of education are intricately linked with elevated pedagogic standards (Howes et al., 2003; Siraj-Blatchford, 2010). In alignment with this premise, the Faculty of Education at the University of Buea has devised a master's programme in nursery and primary education employing a blended learning model, encompassing both face-to-face and online learning modalities. Nonetheless, to provide truly tailored educational experiences catering to the diverse talents, interests, and needs of each learner, a personalised learning approach is indispensable. The creation of a personalised distance learning environment holds paramount significance, particularly in light of the burgeoning prominence and acceptance of personalised learning within higher education circles. This initiative furnishes a roadmap for transitioning from conventional face-to-face instruction to online learning through the lens of personalised learning principles.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0736/a.php" alt="Hit counter" /></p>

Language learning through music: A critical review of Lirica https://www.lirica.io/

The past decade has been the heyday of mobile-assisted language learning (MALL), with increased use of mobile applications (apps) as learning platforms. Despite the central focus of mobile apps on particular language skills such as vocabulary (e.g., Duolingo) and pronunciation (e.g., BoldVoice), few have factored in both learners and teachers. Lirica is an engaging mobile app that fosters language learning through songs in five languages. Given the pedagogical and psychological affordances of songs and the personalized learning environment offered by mobile apps, this tech review seeks to present an overview of the Lirica app and its potential for language learning while tackling its limitations.

Using Innovative Reading Programs in Preschool Age and their Impact on Child Development

Introduction. The concept of innovation can take on various meanings, especially when apllied to preschool education. It involves the introduction of new ideas, methods, techniques, strategies, and practices within the educational setting to enhance and improve the teaching and learning process. This research focuses on the essential element needed for development and self-renewal, which includes being receptive to fresh ideas, exploring new approaches, and incorporating technological advancements into education. Preschool teachers play a vital role in implementing innovative methods and activities within preschool settings to support learners on their educational journey. There are numerous innovative learning models in preschool education, such as project-based learning, which prioritizes individualization, dedication, and autonomy in teaching. This includes utilizing reading programs that make lessons enjoyable while reinforcing acquired knowledge. One of the notable tools is Raz-kids, which enriches students’ lives and aids in their reading development. Goal. The aim of this text is to highlight the importance and multifaceted nature of innovation in preschool education. It emphasizes the need for introducing new ideas, methods, techniques, strategies, and practices to enhance teaching and learning processes. The text underscores the role of preschool teachers in implementing innovative methods and activities, showcasing various learning models such as project-based learning and specific tools like Raz-kids to support and enrich students' educational experiences. The Results. Innovative learning models, particularly Raz-kids reading, have allowed for greater individualization in teaching. Students are given more autonomy, fostering independence and personalized learning experiences that cater to their unique needs and interests. These strategies and the use of technology in the classroom have been shown to improve learning outcomes. The learners have demonstrated better retention of knowledge, enhanced reading skills, and overall academic performance. Conclusions. Embracing innovation in preschool education is essential for creating a stimulating, effective, and personalized learning environment. The continued exploration and implementation of innovative practices will support the development of young learners and contribute to the overall improvement of educational systems.

An Advance Learning Platform for Slow Learners

LearnIt is an innovative educational system crafted to identify and support slow learners through advanced technological solutions. It employs the MERN stack (MongoDB, Express.js, React.js, Node.js) to offer a personalized learning environment tailored to meet individual student needs. This system not only enhances the educational experience for students but also provides tools to facilitate effective communication between teachers and parents, ensuring a cohesive support network for the learner. Additionally, LearnIt empowers educators with data-driven insights, allowing for informed decision-making and targeted interventions. The traditional educational model often overlooks the unique needs of slow learners, resulting in disengagement and poor academic performance. LearnIt addresses this challenge by integrating comprehensive assessment tools and personalized learning strategies within its platform. By identifying slow learners early, the system provides timely interventions that can significantly improve their learning trajectory. The use of the MERN stack ensures that LearnIt is both scalable and responsive. The assessment and monitoring module is at the core of LearnIt’s functionality. The development of LearnIt involved a detailed analysis of the educational needs of slow learners, incorporating feedback from educators, parents, and the students themselves. This user-centered approach ensured that the system's design and functionality were closely aligned with real-world requirements. Key modules of LearnIt include user management, assessment and monitoring, and communication tool. The user management module handles registration, authentication, and role-based access control, ensuring secure and personalized experiences for all users. The assessment and monitoring module is at the core of LearnIt’s functionality. It includes tools for creating and administering assessments, tracking student performance, and generating detailed reports. This module helps in identifying slow learners by analyzing their performance data and providing actionable insights to educators. With these insights, teachers can develop customized learning plans that address the specific needs of each student, fostering an inclusive and effective educational environment. Communication is another critical component of LearnIt. The platform includes features that facilitate seamless communication between teachers, parents, and students. This ensures that parents are kept informed about their child's progress and can collaborate with teachers to provide consistent support. Effective communication helps in building a supportive learning ecosystem, which is essential for the success of slow learners. The implementation of LearnIt has shown promising results. Preliminary studies indicate that the system significantly improves the identification and support of slow learners. LearnIt represents a significant advancement in the field of educational technology. By focusing on the needs of slow learners and leveraging the capabilities of the MERN stack, it offers a comprehensive solution that enhances learning outcomes. The system's personalized approach, combined with robust data analytics and effective communication tools, ensures that every student receives the support they need to thrive. As we continue to refine and expand LearnIt, its impact on educational equity and student success will undoubtedly grow, offering new opportunities for learners who have been historically underserved by traditional educational models. Keywords: Slow learners, education, literacy, mental development, intelligence.

Crafting An AI-Powered Adaptive E-Learning Framework: Based on Kolb's Learning Style

In the contemporary educational realm, the convergence of pedagogical advancements with machine learning presents a profound opportunity for transformation. This in-depth exploration delves into the complexities of constructing an adaptive e-learning platform, seamlessly integrating Kolb's learning preferences with state-of-the-art machine learning techniques. At the core of this initiative lies the certification module, meticulously scrutinized to bolster its effectiveness in acknowledging student accomplishments. With a focus on personalized education driven by machine learning for error analysis, learner profiling, and content adaptation, this amalgamation reshapes the educational landscape, offering bespoke and dynamic learning journeys. By proposing an intelligent and dynamic adaptive learning system, this study addresses the constraints of passive and one-size-fits-all platforms, aiming to discern and furnish personalized learning environments tailored to the unique needs of individual learners within the hybrid teaching paradigm.

Direct Method in Islamic Religious Education: Conceptual Framework and Implementation in Indonesia

The Direct Method, characterized by a focus on oral communication and direct practice, has been widely discussed in the context of language learning. Its application in Islamic religious education, however, remains less explored. This study aims to fill this gap by examining the theoretical underpinnings of the Direct Method and its implementation in Islamic religious education in Indonesia. The primary objective of this research is to comprehensively explore the concept of the Direct Method and its application in the teaching of Islamic religious studies. This includes understanding the theoretical foundations of the method and assessing its practical application in various educational settings, such as schools and madrasahs in Indonesia. This study employs a qualitative research approach, specifically library research. Data were collected through an extensive review of literature from primary and secondary sources, including books, journal articles, and other relevant publications. Data analysis was conducted using document analysis and thematic analysis techniques. The findings reveal that the Direct Method is highly effective in enhancing students' understanding and skills in Islamic religious education. The method's emphasis on direct interaction and immediate feedback from teachers fosters a conducive and personalized learning environment. However, challenges such as limited time and resources were identified as potential barriers to its effective implementation. To maximize the benefits of the Direct Method, ongoing support and training for teachers are essential. Additionally, adequate resources and institutional support are necessary to overcome the identified challenges. Future research should continue to explore innovative strategies to enhance the effectiveness of the Direct Method in various educational contexts.

Metasynthesis of Strategies Mode for Activating Higher Order Thinking Skills (HOTs) of University Students

Countries around the world are increasingly emphasizing the research and cultivation of higher-order thinking abilities, and have proposed various teaching strategies and methods. Researchers have developed various methods, strategies, and related activities to cultivate higher-order thinking in response to this issue, such as "scaffolding teaching", "heuristic teaching", "project-based teaching", "building collaborative learning", and "diverse evaluation feedback". However, current research on teaching strategies mostly focuses on methodology or teaching principles, lacking a teaching strategy model to guide teachers in specific teaching processes and designs. This study conducted a comprehensive integration of previous scientific research, aiming to summarize the strategic models for activating college students' HOTs in the past three years. By using Scopus, Science Direct, and Springer search agents for in-depth filtering, a total of 6 articles were selected. Data screening, evaluation, and CBA use two keywords, and the article is published from around 2020 to 2023. The research results indicate that in addition to the aforementioned teaching strategies, some emerging teaching models are also being widely explored and applied. The flipped learning teaching model shifts the knowledge imparted in the classroom to independent learning after class, while using classroom time to guide students in discussions and practice. Smart classrooms utilize modern technological means to provide a highly interactive and personalized learning environment. The blended learning model combines online and offline learning, fully utilizing the advantages of technology. The evidence-based teaching method emphasizes guiding teaching decisions and practices through scientific research and empirical data. These teaching strategies can effectively improve the higher-order thinking ability of college students. Therefore, integrated research has investigated some specific methods and strategy models for activating higher-order thinking skills among college students in recent years. These methods and strategy models are of great significance to educational practitioners and can provide reference for the teaching of HOTs.

Optimizing Educational Efficiency: Exploring the Relationship Between Bulk and Clinical Rotation Group Size in Medical Education Students’ Perspective in Busitema University Uganda

Clinical rotation group size plays a pivotal role in shaping the quality, efficiency, and overall student experience in medical education. Despite its significance, determining the ideal group size remains a daunting task. This research endeavors to delve into the correlation between group size and what is termed as "bulk," representing the comprehensive efficiency and productivity of clinical rotation groups. By examining this relationship, the study aims to offer valuable insights into potential strategies for optimizing group size, thereby enhancing the educational experience for medical students. The study employed a mixed-methods approach, combining quantitative analysis of productivity metrics with qualitative assessments of student and faculty perceptions. Surveys, interviews, and observational data were utilized to capture the nuances of group dynamics, learning outcomes, and resource utilization across different group sizes. Statistical techniques such as regression analysis and ANOVA were employed to identify significant correlations and differences. Data were collected from medical students undergoing clinical rotations across multiple specialties. Group sizes varied from small (2-3) and large (4 and more). The study measured bulk using indicators such as patient encounters per student, learning outcomes, and faculty-student interaction. Results indicated a nonlinear relationship between bulk and group size. Smaller groups showed higher individual student engagement, personalized learning experiences, and increased faculty-student interaction. Conversely, larger groups exhibited greater overall productivity due to economies of scale and enhanced peer learning. However, beyond a certain threshold, further increases in group size led to diminishing returns, compromising individualized attention and educational quality. The findings underscore the importance of balancing group size to optimize educational outcomes in clinical rotations. While larger groups may offer efficiency gains, they must be carefully managed to mitigate potential drawbacks such as reduced individualized attention and faculty-student interaction. Smaller groups foster a more personalized learning environment but may lack the resource utilization efficiency of larger cohorts. Institutions should consider contextual factors such as specialty, learning objectives, and available resources when determining optimal group sizes. In conclusion, the relationship between bulk and clinical rotation group size is complex, with both benefits and limitations associated with different group sizes. Institutions should strive to strike a balance between efficiency and educational quality by tailoring group sizes to specific contexts and objectives. Further research is needed to explore additional factors influencing bulk and to develop targeted interventions for optimizing clinical rotation experiences.

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