Large Language Model-based System to Provide Immediate Feedback to
Students in Flipped Classroom Preparation Learning
- URL: http://arxiv.org/abs/2307.11388v1
- Date: Fri, 21 Jul 2023 06:59:53 GMT
- Title: Large Language Model-based System to Provide Immediate Feedback to
Students in Flipped Classroom Preparation Learning
- Authors: Shintaro Uchiyama, Kyoji Umemura and Yusuke Morita
- Abstract summary: This study aimed to solve challenges in the flipped classroom model, such as ensuring that students are emotionally engaged and motivated to learn.
Students often have questions about the content of lecture videos in the preparation of flipped classrooms, but it is difficult for teachers to answer them immediately.
The proposed system was developed using the ChatGPT API on a video-watching support system for preparation learning that is being used in real practice.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper proposes a system that uses large language models to provide
immediate feedback to students in flipped classroom preparation learning. This
study aimed to solve challenges in the flipped classroom model, such as
ensuring that students are emotionally engaged and motivated to learn. Students
often have questions about the content of lecture videos in the preparation of
flipped classrooms, but it is difficult for teachers to answer them
immediately. The proposed system was developed using the ChatGPT API on a
video-watching support system for preparation learning that is being used in
real practice. Answers from ChatGPT often do not align with the context of the
student's question. Therefore, this paper also proposes a method to align the
answer with the context. This paper also proposes a method to collect the
teacher's answers to the students' questions and use them as additional guides
for the students. This paper discusses the design and implementation of the
proposed system.
Related papers
- How Do Students Interact with an LLM-powered Virtual Teaching Assistant in Different Educational Settings? [3.9134031118910264]
Jill Watson, a virtual teaching assistant powered by LLMs, answers student questions and engages them in extended conversations on courseware provided by the instructors.
In this paper, we analyze student interactions with Jill across multiple courses and colleges.
We find that, by supporting a wide range of cognitive demands, Jill encourages students to engage in sophisticated, higher-order cognitive questions.
arXiv Detail & Related papers (2024-07-15T01:22:50Z) - Large Language Model-Driven Classroom Flipping: Empowering
Student-Centric Peer Questioning with Flipped Interaction [3.1473798197405953]
This paper investigates a pedagogical approach of classroom flipping based on flipped interaction in large language models.
Flipped interaction involves using language models to prioritize generating questions instead of answers to prompts.
We propose a workflow to integrate prompt engineering with clicker and JiTT quizzes by a poll-prompt-quiz routine and a quiz-prompt-discuss routine.
arXiv Detail & Related papers (2023-11-14T15:48:19Z) - Teacher Perception of Automatically Extracted Grammar Concepts for L2
Language Learning [66.79173000135717]
We apply this work to teaching two Indian languages, Kannada and Marathi, which do not have well-developed resources for second language learning.
We extract descriptions from a natural text corpus that answer questions about morphosyntax (learning of word order, agreement, case marking, or word formation) and semantics (learning of vocabulary).
We enlist the help of language educators from schools in North America to perform a manual evaluation, who find the materials have potential to be used for their lesson preparation and learner evaluation.
arXiv Detail & Related papers (2023-10-27T18:17:29Z) - A large language model-assisted education tool to provide feedback on
open-ended responses [2.624902795082451]
We present a tool that uses large language models (LLMs), guided by instructor-defined criteria, to automate responses to open-ended questions.
Our tool delivers rapid personalized feedback, enabling students to quickly test their knowledge and identify areas for improvement.
arXiv Detail & Related papers (2023-07-25T19:49:55Z) - PapagAI:Automated Feedback for Reflective Essays [48.4434976446053]
We present the first open-source automated feedback tool based on didactic theory and implemented as a hybrid AI system.
The main objective of our work is to enable better learning outcomes for students and to complement the teaching activities of lecturers.
arXiv Detail & Related papers (2023-07-10T11:05:51Z) - UKP-SQuARE: An Interactive Tool for Teaching Question Answering [61.93372227117229]
The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course.
We introduce UKP-SQuARE as a platform for QA education.
Students can run, compare, and analyze various QA models from different perspectives.
arXiv Detail & Related papers (2023-05-31T11:29:04Z) - MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties
Grounded in Math Reasoning Problems [74.73881579517055]
We propose a framework to generate such dialogues by pairing human teachers with a Large Language Model prompted to represent common student errors.
We describe how we use this framework to collect MathDial, a dataset of 3k one-to-one teacher-student tutoring dialogues.
arXiv Detail & Related papers (2023-05-23T21:44:56Z) - Teacher Perception of Automatically Extracted Grammar Concepts for L2
Language Learning [91.49622922938681]
We present an automatic framework that automatically discovers and visualizing descriptions of different aspects of grammar.
Specifically, we extract descriptions from a natural text corpus that answer questions about morphosyntax and semantics.
We apply this method for teaching the Indian languages, Kannada and Marathi, which, unlike English, do not have well-developed pedagogical resources.
arXiv Detail & Related papers (2022-06-10T14:52:22Z) - A literature survey on student feedback assessment tools and their usage
in sentiment analysis [0.0]
We evaluate the effectiveness of various in-class feedback assessment methods such as Kahoot!, Mentimeter, Padlet, and polling.
We propose a sentiment analysis model for extracting the explicit suggestions from the students' qualitative feedback comments.
arXiv Detail & Related papers (2021-09-09T06:56:30Z) - Real-Time Cognitive Evaluation of Online Learners through Automatically
Generated Questions [0.0]
The paper presents an approach to generate questions from a given video lecture automatically.
The generated questions are aimed to evaluate learners' lower-level cognitive abilities.
arXiv Detail & Related papers (2021-06-06T05:45:56Z) - Neural Multi-Task Learning for Teacher Question Detection in Online
Classrooms [50.19997675066203]
We build an end-to-end neural framework that automatically detects questions from teachers' audio recordings.
By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions.
arXiv Detail & Related papers (2020-05-16T02:17:04Z)
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.