Review Ecosystems to access Educational XR Experiences: a Scoping Review

• URL: http://arxiv.org/abs/2403.17243v1
• Date: Mon, 25 Mar 2024 22:44:28 GMT
• Title: Review Ecosystems to access Educational XR Experiences: a Scoping Review
• Authors: Shaun Bangay, Adam P. A. Cardilini, Sophie McKenzie, Maria Nicholas, Manjeet Singh,
• Abstract summary: This paper identifies best practices for developing a new review ecosystem. It focuses on the form and format of these reviews, as well as the mechanisms for sharing information about experiences. The strategies and opportunities for developing an educational XR (eduXR) review ecosystem include methods for measuring properties such as quality metrics.
• Score: 0.4177651846674218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Educators, developers, and other stakeholders face challenges when creating, adapting, and utilizing virtual and augmented reality (XR) experiences for teaching curriculum topics. User created reviews of these applications provide important information about their relevance and effectiveness in supporting achievement of educational outcomes. To make these reviews accessible, relevant, and useful, they must be readily available and presented in a format that supports decision-making by educators. This paper identifies best practices for developing a new review ecosystem by analyzing existing approaches to providing reviews of interactive experiences. It focuses on the form and format of these reviews, as well as the mechanisms for sharing information about experiences and identifying which ones are most effective. The paper also examines the incentives that drive review creation and maintenance, ensuring that new experiences receive attention from reviewers and that relevant information is updated when necessary. The strategies and opportunities for developing an educational XR (eduXR) review ecosystem include methods for measuring properties such as quality metrics, engaging a broad range of stakeholders in the review process, and structuring the system as a closed loop managed by feedback and incentive structures to ensure stability and productivity. Computing educators are well-positioned to lead the development of these review ecosystems, which can relate XR experiences to the potential opportunities for teaching and learning that they offer.

Related papers

• Identifying Aspects in Peer Reviews [61.374437855024844]
  We develop a data-driven schema for deriving fine-grained aspects from a corpus of peer reviews. We introduce a dataset of peer reviews augmented with aspects and show how it can be used for community-level review analysis.
  arXiv  Detail & Related papers  (2025-04-09T14:14:42Z)
• Human-Centered Design for AI-based Automatically Generated Assessment Reports: A Systematic Review [4.974197456441281]
  This study emphasizes the importance of reducing teachers' cognitive demands through user-centered and intuitive designs. It highlights the potential of diverse information presentation formats such as text, visual aids, and plots and advanced functionalities such as live and interactive features to enhance usability. The framework aims to address challenges in engaging teachers with technology-enhanced assessment results, facilitating data-driven decision-making, and providing personalized feedback to improve the teaching and learning process.
  arXiv  Detail & Related papers  (2024-12-30T16:20:07Z)
• 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. Our approach adapts document content and structure based on insights from analyzing digital reading traces-interactions between readers and content. Our framework enables authors to receive tailored content revision recommendations through an interactive dashboard.
  arXiv  Detail & Related papers  (2024-12-16T16:26:20Z)
• DK-PRACTICE: An Intelligent Educational Platform for Personalized Learning Content Recommendations Based on Students Knowledge State [2.249916681499244]
  This study introduces DK-PRACTICE, an intelligent online platform that leverages machine learning to provide personalized learning recommendations. The system dynamically selects the next question for each student based on the correctness and accuracy of their previous answers. After the test is completed, DK-PRACTICE analyzes students' interaction history to recommend learning materials.
  arXiv  Detail & Related papers  (2024-12-13T18:35:37Z)
• Personalised Feedback Framework for Online Education Programmes Using Generative AI [0.0]
  This paper presents an alternative feedback framework which extends the capabilities of ChatGPT by integrating embeddings. As part of the study, we proposed and developed a proof of concept solution, achieving an efficacy rate of 90% and 100% for open-ended and multiple-choice questions.
  arXiv  Detail & Related papers  (2024-10-14T22:35:40Z)
• AERA Chat: An Interactive Platform for Automated Explainable Student Answer Assessment [12.970776782360366]
  AERA Chat is an interactive platform to provide visually explained assessment of student answers. Users can input questions and student answers to obtain automated, explainable assessment results from large language models.
  arXiv  Detail & Related papers  (2024-10-12T11:57:53Z)
• Leveraging Large Language Models for Actionable Course Evaluation Student Feedback to Lecturers [6.161370712594005]
  We have 742 student responses ranging over 75 courses in a Computer Science department. For each course, we synthesise a summary of the course evaluations and actionable items for the instructor. Our work highlights the possibility of using generative AI to produce factual, actionable, and appropriate feedback for teachers in the classroom setting.
  arXiv  Detail & Related papers  (2024-07-01T13:29:55Z)
• Evaluating and Optimizing Educational Content with Large Language Model Judgments [52.33701672559594]
  We use Language Models (LMs) as educational experts to assess the impact of various instructions on learning outcomes. We introduce an instruction optimization approach in which one LM generates instructional materials using the judgments of another LM as a reward function. Human teachers' evaluations of these LM-generated worksheets show a significant alignment between the LM judgments and human teacher preferences.
  arXiv  Detail & Related papers  (2024-03-05T09:09:15Z)
• In-Memory Learning: A Declarative Learning Framework for Large Language Models [56.62616975119192]
  We propose a novel learning framework that allows agents to align with their environment without relying on human-labeled data. This entire process transpires within the memory components and is implemented through natural language. We demonstrate the effectiveness of our framework and provide insights into this problem.
  arXiv  Detail & Related papers  (2024-03-05T08:25:11Z)
• Improving the Validity of Automatically Generated Feedback via Reinforcement Learning [50.067342343957876]
  We propose a framework for feedback generation that optimize both correctness and alignment using reinforcement learning (RL) Specifically, we use GPT-4's annotations to create preferences over feedback pairs in an augmented dataset for training via direct preference optimization (DPO)
  arXiv  Detail & Related papers  (2024-03-02T20:25:50Z)
• A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
  This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews. The newly emerging AI-generated literature reviews are also appraised. This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
  arXiv  Detail & Related papers  (2024-02-20T11:28:50Z)
• 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)
• Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach [77.61131357420201]
  We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs) We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date. We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
  arXiv  Detail & Related papers  (2022-11-07T16:19:42Z)
• KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting [52.623349754076024]
  We provide an overview of the recommendation approaches integrated in KnowledgeCheckR. Examples thereof are utility-based recommendation that helps to identify learning contents to be repeated in the future, collaborative filtering approaches that help to implement session-based recommendation, and content-based recommendation that supports intelligent question answering.
  arXiv  Detail & Related papers  (2021-02-15T20:06:28Z)
• Canvas Adoption Assessment and Acceptance of the Learning Management System on a Web-Based Platform [0.0]
  This study aims to assess student adoption of Canvas as a new learning management system and its potential as a web-based platform in the e-learning programme of the University of the East. The students perceived ease of use has a significant effect on their perceived usefulness but has no significant effects on their attitude towards the use of Canvas.
  arXiv  Detail & Related papers  (2021-01-29T01:30:55Z)
• Code Review in the Classroom [57.300604527924015]
  Young developers in a classroom setting provide a clear picture of the potential favourable and problematic areas of the code review process. Their feedback suggests that the process has been well received with some points to better the process. This paper can be used as guidelines to perform code reviews in the classroom.
  arXiv  Detail & Related papers  (2020-04-19T06:07:45Z)

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.