Results 261 to 270 of about 1,166,366 (314)

Improving the prediction accuracy in blended learning environment using synthetic minority oversampling technique

Information Discovery and Delivery, 2019
Purpose This paper aims to deal with the previously unknown prediction accuracy of students’ activity pattern in a blended learning environment. Design/methodology/approach To extract the most relevant activity feature subset, different feature-selection methods were applied.
Gabrijela Dimic, Dejan Rancic, Nemanja Macek, Petar Spalevic, Vida Drasute   +4 more
openaire   +2 more sources

Collaborative Virtual Learning Environment Using Synthetic Characters

International Conference on Advances in Web-Based Learning, 2005
This research work not only proposes a deep insight to initiative and vivid concept modeling which makes use of several techniques like flash animation, rolling image-based introduction, virtual experiment, etc., but also explores the potential integration of synthetic characters with virtual learning environment to better simulate the social ...
Zhigeng Pan, Jiejie Zhu, Mingming Zhang, Weihua Hu   +3 more
openaire   +2 more sources

Machine learning using synthetic images for detecting dust emissions on construction sites

Smart and Sustainable Built Environment, 2021
PurposeAutomated dust monitoring in workplaces helps provide timely alerts to over-exposed workers and effective mitigation measures for proactive dust control.
Ruoxin Xiong, P. Tang
semanticscholar   +1 more source

Synthetic learning agents in game-playing social environments

Adaptive Behavior, 2016
This paper investigates the performance of synthetic agents in playing and learning scenarios in a turn-based zero-sum game and highlights the ability of opponent-based learning models to demonstrate competitive playing performances in social environments.
Chairi Kiourt, Dimitris Kalles
openaire   +1 more source

Optic Flow Helps Humans Learn to Navigate through Synthetic Environments

Perception, 2000
Self-movement through an environment generates optic flow, a potential source of heading information. But it is not certain that optic flow is sufficient to support navigation, particularly navigation along complex, multi-legged paths. To address this question, we studied human participants who navigated synthetic environments with and without salient
M P, Kirschen, M J, Kahana, R, Sekuler, B, Burack   +3 more
openaire   +2 more sources

Using Synthetic Datasets to Hone Intuitions Within an Adaptive Learning Environment

2021
Adaptive learning typically focuses on the challenge of adjusting the presentation of instructional material based on an automated assessment of real-time student performance. The goal is to move away from a one-size-fits-all approach to learning toward a more personalized experience.
Walter Warwick, Rod Ford, Matt Funke
openaire   +1 more source

TALES: Text Adventure Learning Environment Suite

arXiv.org
Reasoning is an essential skill to enable Large Language Models (LLMs) to interact with the world. As tasks become more complex, they demand increasingly sophisticated and diverse reasoning capabilities for sequential decision-making, requiring ...
Christopher Cui, Xingdi Yuan, Zhangyu Xiao, Prithviraj Ammanabrolu, Marc-Alexandre Côté   +4 more
semanticscholar   +1 more source

Machine learning for synthetic gene circuit engineering.

Current Opinion in Biotechnology
Synthetic biology leverages engineering principles to program biology with new functions for applications in medicine, energy, food, and the environment.
Sebastian Palacios, James J. Collins, Domitilla Del Vecchio   +2 more
semanticscholar   +1 more source

E-Learning Environment Based on Intelligent Synthetic Characters

2005
This paper introduces our approach to apply synthetic characters to behavior training in primary schools. Project groups include research centers from UK, Portugal, and China. Research members include artificial intelligent group, graphical group, educational group and psychological group.
Lu Ye, Jiejie Zhu, Mingmin Zhang, Ruth Aylett, Lifeng Ren, Guilin Xu   +5 more
openaire   +1 more source

Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use

arXiv.org
Reinforcement learning has been shown to improve the performance of large language models. However, traditional approaches like RLHF or RLAIF treat the problem as single-step.
Anna Goldie, Azalia Mirhoseini, Hao Zhou, Irene Cai, Christopher D. Manning   +4 more
semanticscholar   +1 more source