logo
ResearchBunny Logo
AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning

Education

AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning

J. J. Williams, J. Kim, et al.

Discover AXIS, a groundbreaking system that enhances online learning by generating and refining explanations through learner engagement and machine learning. This innovative approach demonstrates comparable effectiveness to traditional instruction, led by a team of renowned researchers including Joseph Jay Williams, Juho Kim, and others.

00:00
00:00
Playback language: English
Abstract
This paper introduces AXIS (Adaptive eXplanation Improvement System), a system designed to generate and improve explanations for online learning materials. AXIS leverages learners to generate, revise, and evaluate explanations, employing machine learning to dynamically select the most helpful ones for future learners. A case study and randomized experiment demonstrate that AXIS elicits effective explanations that enhance learning, comparable to those created by experienced instructors.
Publisher
L@S 2016 Flipped Session
Published On
Apr 25, 2016
Authors
Joseph Jay Williams, Juho Kim, Anna Rafferty, Samuel Maldonado, Walter S. Lasecki, Neil Heffernan, Krzysztof Z. Gajos
DOI
https://doi.org/10.1145/2876034.2876042
Tags
adaptive learning
explanation generation
machine learning
online education
learner engagement

Related Publications

Explore these studies to deepen your understanding of the subject.

A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders
Medicine and Health

A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders

A. Zadka, N. Rabin, et al.

On responsible machine learning datasets emphasizing fairness, privacy and regulatory norms with examples in biometrics and healthcare
Computer Science

On responsible machine learning datasets emphasizing fairness, privacy and regulatory norms with examples in biometrics and healthcare

S. Mittal, K. Thakral, et al.

Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations
Veterinary Science

Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations

M. Wardeh, M. S. C. Blagrove, et al.

Neural sampling machine with stochastic synapse allows brain-like learning and inference
Engineering and Technology

Neural sampling machine with stochastic synapse allows brain-like learning and inference

S. Dutta, G. Detorakis, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny