Demonstrating REACT: a Real-time Educational AI-powered Classroom Tool
- URL: http://arxiv.org/abs/2108.07693v1
- Date: Fri, 30 Jul 2021 03:09:59 GMT
- Title: Demonstrating REACT: a Real-time Educational AI-powered Classroom Tool
- Authors: Ajay Kulkarni and Olga Gkountouna
- Abstract summary: We present a new Real-time Educational AI-powered Classroom Tool that employs EDM techniques for supporting the decision-making process of educators.
ReACT is a data-driven tool with a user-friendly graphical interface.
It analyzes students' performance data and provides context-based alerts as well as recommendations to educators for course planning.
- Score: 0.9899017174990579
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a demonstration of REACT, a new Real-time Educational AI-powered
Classroom Tool that employs EDM techniques for supporting the decision-making
process of educators. REACT is a data-driven tool with a user-friendly
graphical interface. It analyzes students' performance data and provides
context-based alerts as well as recommendations to educators for course
planning. Furthermore, it incorporates model-agnostic explanations for bringing
explainability and interpretability in the process of decision making. This
paper demonstrates a use case scenario of our proposed tool using a real-world
dataset and presents the design of its architecture and user interface. This
demonstration focuses on the agglomerative clustering of students based on
their performance (i.e., incorrect responses and hints used) during an in-class
activity. This formation of clusters of students with similar strengths and
weaknesses may help educators to improve their course planning by identifying
at-risk students, forming study groups, or encouraging tutoring between
students of different strengths.
Related papers
- 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) - 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) - Revisiting Self-supervised Learning of Speech Representation from a
Mutual Information Perspective [68.20531518525273]
We take a closer look into existing self-supervised methods of speech from an information-theoretic perspective.
We use linear probes to estimate the mutual information between the target information and learned representations.
We explore the potential of evaluating representations in a self-supervised fashion, where we estimate the mutual information between different parts of the data without using any labels.
arXiv Detail & Related papers (2024-01-16T21:13:22Z) - CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update [69.59482029810198]
CLOVA is a Closed-Loop Visual Assistant that operates within a framework encompassing inference, reflection, and learning phases.
Results demonstrate that CLOVA surpasses existing tool-usage methods by 5% in visual question answering and multiple-image reasoning, by 10% in knowledge tagging, and by 20% in image editing.
arXiv Detail & Related papers (2023-12-18T03:34:07Z) - Empowering Private Tutoring by Chaining Large Language Models [87.76985829144834]
This work explores the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs)
The system is into three inter-connected core processes-interaction, reflection, and reaction.
Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules.
arXiv Detail & Related papers (2023-09-15T02:42:03Z) - CLGT: A Graph Transformer for Student Performance Prediction in
Collaborative Learning [6.140954034246379]
We present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students.
The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets.
arXiv Detail & Related papers (2023-07-30T09:54:30Z) - Distantly-Supervised Named Entity Recognition with Adaptive Teacher
Learning and Fine-grained Student Ensemble [56.705249154629264]
Self-training teacher-student frameworks are proposed to improve the robustness of NER models.
In this paper, we propose an adaptive teacher learning comprised of two teacher-student networks.
Fine-grained student ensemble updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise.
arXiv Detail & Related papers (2022-12-13T12:14:09Z) - Mitigating Biases in Student Performance Prediction via Attention-Based
Personalized Federated Learning [7.040747348755578]
Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability.
We propose a methodology for predicting student performance from their online learning activities that optimize inference accuracy over different demographic groups such as race and gender.
arXiv Detail & Related papers (2022-08-02T00:22:20Z) - Information-Theoretic Odometry Learning [83.36195426897768]
We propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation.
The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language.
arXiv Detail & Related papers (2022-03-11T02:37:35Z) - Towards Explainable Student Group Collaboration Assessment Models Using
Temporal Representations of Individual Student Roles [12.945344702592557]
We propose using simple temporal-CNN deep-learning models to assess student group collaboration.
We check the applicability of dynamically changing feature representations for student group collaboration assessment.
We also use Grad-CAM visualizations to better understand and interpret the important temporal indices that led to the deep-learning model's decision.
arXiv Detail & Related papers (2021-06-17T16:00:08Z) - The Wits Intelligent Teaching System: Detecting Student Engagement
During Lectures Using Convolutional Neural Networks [0.30458514384586394]
The Wits Intelligent Teaching System (WITS) aims to assist lecturers with real-time feedback regarding student affect.
A CNN based on AlexNet is successfully trained and which significantly outperforms a Support Vector Machine approach.
arXiv Detail & Related papers (2021-05-28T12:59:37Z)
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.