Mentoring Software in Education and Its Impact on Teacher Development: An Integrative Literature Review

• URL: http://arxiv.org/abs/2502.12515v1
• Date: Tue, 18 Feb 2025 04:01:45 GMT
• Title: Mentoring Software in Education and Its Impact on Teacher Development: An Integrative Literature Review
• Authors: Ramiro Pesina,
• Abstract summary: This study explores the transformative potential of digital mentoring platforms.<n>The research synthesizes findings from existing literature to assess the effectiveness of key features.<n>Financial constraints, limited institutional support, and data privacy concerns remain significant challenges.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mentoring software is a pivotal innovation in addressing critical challenges in teacher development within educational institutions. This study explores the transformative potential of digital mentoring platforms, evaluating their impact on enhancing traditional mentoring practices through scalable, data-driven, and accessible frameworks. The research synthesizes findings from existing literature to assess the effectiveness of key features, including structured goal setting, progress monitoring, and advanced analytics, in improving teacher satisfaction, retention, and professional growth. Using an integrative literature review approach, this study identifies both the advantages and barriers to implementing mentoring software in education. Financial constraints, limited institutional support, and data privacy concerns remain significant challenges, necessitating strategic interventions. Drawing insights from successful applications in healthcare and corporate sectors, the review highlights adaptive strategies such as leveraging open-source tools, cross-sector collaborations, and integrating mentoring software with existing professional development frameworks. The research emphasizes the necessity of integrating digital mentoring tools with institutional objectives to create enduring support systems for teacher development. Mentoring software not only enhances traditional mentorship but also facilitates broader professional networks that contribute to collective knowledge sharing.

Related papers

• Beyond the Classroom: Bridging the Gap Between Academia and Industry with a Hands-on Learning Approach [2.8123958518740544]
  Self-adaptive software systems have emerged as a critical focus in software design and operation. A survey among practitioners identified that the lack of knowledgeable individuals has hindered its adoption in the industry. We present our experience teaching a course on self-adaptive software systems that integrates theoretical knowledge and hands-on learning with industry-relevant technologies.
  arXiv  Detail & Related papers  (2025-04-14T21:32:25Z)
• Design of AI-Powered Tool for Self-Regulation Support in Programming Education [4.171227316909729]
  Large Language Model (LLM) tools have demonstrated their potential to deliver high-quality assistance. However, many of these tools operate independently from institutional Learning Management Systems. This isolation limits the ability to leverage learning materials and exercise context for generating tailored, context-aware feedback.
  arXiv  Detail & Related papers  (2025-04-03T22:47:33Z)
• An Online Integrated Development Environment for Automated Programming Assessment Systems [4.618037115403291]
  This research contributes to the field of programming education by extracting and defining requirements for an online IDE. The usability of the new online IDE was assessed using the Technology Acceptance Model (TAM), gathering feedback from 27 first-year students.
  arXiv  Detail & Related papers  (2025-03-17T12:50:51Z)
• LLM Agents for Education: Advances and Applications [49.3663528354802]
  Large Language Model (LLM) agents have demonstrated remarkable capabilities in automating tasks and driving innovation across diverse educational applications. This survey aims to provide a comprehensive technological overview of LLM agents for education, fostering further research and collaboration to enhance their impact for the greater good of learners and educators alike.
  arXiv  Detail & Related papers  (2025-03-14T11:53:44Z)
• MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors [76.1634959528817]
  We present MathTutorBench, an open-source benchmark for holistic tutoring model evaluation. MathTutorBench contains datasets and metrics that broadly cover tutor abilities as defined by learning sciences research in dialog-based teaching. We evaluate a wide set of closed- and open-weight models and find that subject expertise, indicated by solving ability, does not immediately translate to good teaching.
  arXiv  Detail & Related papers  (2025-02-26T08:43:47Z)
• LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System [54.71619734800526]
  GenMentor is a multi-agent framework designed to deliver goal-oriented, personalized learning within ITS.<n>It maps learners' goals to required skills using a fine-tuned LLM trained on a custom goal-to-skill dataset.<n>GenMentor tailors learning content with an exploration-drafting-integration mechanism to align with individual learner needs.
  arXiv  Detail & Related papers  (2025-01-27T03:29:44Z)
• IntelliChain: An Integrated Framework for Enhanced Socratic Method Dialogue with LLMs and Knowledge Graphs [1.810537720642316]
  This study explores how to optimize the design and collaboration of a multi-agent system tailored for Socratic teaching.<n>By incorporating knowledge graphs, this research has bolstered the capability of LLMs to handle specific educational content.
  arXiv  Detail & Related papers  (2025-01-07T03:32:29Z)
• User-Centered Course Reengineering: An Analytical Approach to Enhancing Reading Comprehension in Educational Content [0.0]
  This study presents an analytical framework to help course designers enhance educational content to better support learning outcomes.<n>Our approach adapts document content and structure based on insights from analyzing digital reading traces-interactions between readers and content.<n>Our framework enables authors to receive tailored content revision recommendations through an interactive dashboard.
  arXiv  Detail & Related papers  (2024-12-16T16:26:20Z)
• Agent-Driven Automatic Software Improvement [55.2480439325792]
  This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation. We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
  arXiv  Detail & Related papers  (2024-06-24T15:45:22Z)
• Impermanent Identifiers: Enhanced Source Code Comprehension and Refactoring [43.5512514983067]
  This article introduces an innovative approach to code augmentation centered around Impermanent Identifiers. The primary goal is to enhance the software development experience by introducing dynamic identifiers that adapt to changing contexts. This study rigorously evaluates the adoption and acceptance of Impermanent Identifiers within the software development landscape.
  arXiv  Detail & Related papers  (2024-06-13T12:54:02Z)
• Charting a Path to Efficient Onboarding: The Role of Software Visualization [49.1574468325115]
  The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools. This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
  arXiv  Detail & Related papers  (2024-01-17T21:30:45Z)
• Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education [0.2812395851874055]
  This research study explores the conceptualization, development, and deployment of an innovative learning analytics tool. By analyzing critical data points such as students' stress levels, curiosity, confusion, agitation, topic preferences, and study methods, the tool provides a comprehensive view of the learning environment. This research underscores AI's role in shaping personalized, data-driven education.
  arXiv  Detail & Related papers  (2023-12-15T06:00:26Z)
• Towards Goal-oriented Intelligent Tutoring Systems in Online Education [69.06930979754627]
  We propose a new task, named Goal-oriented Intelligent Tutoring Systems (GITS) GITS aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment. We propose a novel graph-based reinforcement learning framework, named Planning-Assessment-Interaction (PAI)
  arXiv  Detail & Related papers  (2023-12-03T12:37:16Z)
• Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach [40.06500618820166]
  This paper presents an approach to deriving a learner model directly from an assessment rubric. We illustrate how the approach can be applied to automatize the human assessment of an activity developed for testing computational thinking skills.
  arXiv  Detail & Related papers  (2022-09-07T10:09:12Z)
• Key principles for workforce upskilling via online learning: a learning analytics study of a professional course in additive manufacturing [2.014343808433054]
  This study combines learning objective analysis and visual learning analytics to examine the relationships among learning trajectories, engagement, and performance. The study also emphasizes broader strategies for course designers and instructors to align course assignments, learning objectives, and assessment measures with learner needs and interests.
  arXiv  Detail & Related papers  (2020-08-15T00:30:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.