Adaptive learning system using big data based machine learning

Adaptive learning system using big data based machine learning
Intelligent scaffolding system to provide adaptive hints.

Over the past few decades, many studies conducted in the field of learning science have reported that scaffolding plays an important role in human learning. To scaffold a learner efficiently, a teacher should predict how much support a learner must have to complete tasks and then decide the optimal degree of assistance to support the learner's development. Nevertheless, it is difficult to ascertain the optimal degree of assistance for learner development.

In this study, we assumed that optimal scaffolding is based on a probabilistic decision rule: given a teacher's assistance to facilitate the learner development, an optimal exists for a learner to solve a task. To ascertain the optimal probability, we developed a scaffolding system that provides adaptive hints to adjust the predictive probability of the learner's successful to the previously determined certain value, using a statistical machine learning technology.

Furthermore, using the scaffolding system, we compared learning performances by changing the predictive probability. Our results showed that scaffolding to achieve 0.5 learner success probability provides the best performance. Also experiments demonstrated that a scaffolding system providing 0.5 probability decreases the number of hints (amount of support) automatically as a fading function according to the learner's growth capability.


Explore further

A new dynamic ensemble active learning method based on a non-stationary bandit

More information: Maomi Ueno and Yoshimitsu Miyazawa, IRT-Based Adaptive Hints to Scaffold Learning in Programming, IEEE Transactions on Learning Technologies, IEEE computer Society, 11, No.4, 415-428, (2018).
Citation: Adaptive learning system using big data based machine learning (2019, March 15) retrieved 19 July 2019 from https://phys.org/news/2019-03-big-based-machine.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.
7 shares

Feedback to editors

User comments

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more