ACognitive Approach to Game Usability and Design: Mental Model Development in Novice Real-Time Strategy Gamers

2006 ◽  
Vol 9 (3) ◽  
pp. 361-366 ◽  
Author(s):  
John Graham ◽  
Liya Zheng ◽  
Cleotilde Gonzalez
Keyword(s):  
Real Time ◽  
Mental Model ◽  
Model Development

Antecedents of learners' mental model development

2008 ◽  
Author(s):  
Nicole Kohari ◽  
Robert Lord ◽  
Joelle Elicker ◽  
Steven Ash ◽  
Bryce Hruska
Keyword(s):  
Mental Model ◽  
Model Development

scholarly journals Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 357
Author(s):  
Dae-Hyun Jung ◽  
Na Yeon Kim ◽  
Sang Ho Moon ◽  
Changho Jhin ◽  
Hak-Jin Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Model Development ◽  
Classification Model ◽  
Intelligence Analysis ◽  
Noise Filtering ◽  
Meat Industry ◽  
Mel Frequency Cepstral Coefficients ◽  
Web Based ◽  
The Status

The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.

Technology-Mediated Collaboration, Shared Mental Model and Task Performance

2012 ◽  
Vol 24 (1) ◽  
pp. 64-81 ◽  
Author(s):  
Hayward P. Andres
Keyword(s):  
Social Influence ◽  
Direct Observation ◽  
Mental Model ◽  
Social Impact ◽  
Model Development ◽  
Research Approach ◽  
Team Process ◽  
Shared Mental Model ◽  
Team Productivity ◽  
The Impact

This study takes a direct observation research approach to examine how the impact of collaboration mode on team productivity and process satisfaction is mediated by shared mental model. Team cognition and social impact theories are integrated to provide a framework for explaining how technology-mediated collaboration constrains or enhances team shared mental model development and its subsequent impact on task outcomes. Partial least squares analysis revealed that technology-mediated collaboration impacts shared mental model development. The results also demonstrate that timely and accurate development of shared mental model facilitates increases in both productivity and team process satisfaction. Direct observation of team process behaviors suggests that collaboration modes differ not only in their impact on communication facilitation but efficacy-based, motivational, and social influence factors (e.g., self-efficacy and team-efficacy, perceived salience and credibility of contributions, social influence on action, etc.) as well. Shared mental model development requires quality communication among team members that are motivated to participate by a positive team climate that promotes idea convergence.

ANALISIS MODEL MENTAL SISWA DALAM PENERAPAN MODEL PEMBELAJARAN LEARNING CYCLE 8E PADA MATERI HIDROLISIS GARAM

2017 ◽  
Vol 7 (1) ◽  
pp. 38-51
Author(s):  
Waskitarini Darmiyanti ◽  
Yuli Rahmawati ◽  
Fera Kurniadewi ◽  
Achmad Ridwan
Keyword(s):  
Learning Environment ◽  
Mental Model ◽  
Chemistry Education ◽  
Model Development ◽  
Deep Understanding ◽  
Learning Cycle ◽  
First Semester ◽  
Before And After ◽  
Academic Year ◽  
Depth Interviews

This study aims to analyse the students’ mental model of students in SMA 42 Jakarta in the implementation of Learning Cycle 8E learning model on salt hydrolysis learning. This research was conducted in first semester of 2016/2017 academic year. There are 36 students of year XI in MIPA 5 was involved in the study. This qualitative research was conducted with the data collection of writing-drawing technique, in-depth interviews, classroom observation, student journal reflective. The Miles and Huberman data analysis was employed with quality standards of data reduction, display data, and verification. Class observation conducted before and after the learning Cycle 8E was implemented for understanding, the characteristics of students and classroom learning environment. The interview technique was conducted to have deep understanding the students’ mental model. The result showed that the students developed understanding on the concept of hydrolysis reaction process and pH of salt solution which were generated categories of understanding, not understanding, and misconception. The three categories are seen from the four stages of the learning Cycle 8E which are explore, elaborate, extend, and explain. students’ understanding and mental model development are influenced by the learning environment and prior knowledge. This model also has developed students’ soft skills of empathy communication, critical thinking and collaboration skills. Keyword: Chemistry Education, Mental Model, Salt Hydrolysis, Learning Cycle 8E

Mental Model Development Using Collaborative 3D Virtual Environments

2016 ◽  
pp. 279-290 ◽  
Author(s):  
Ali Asghar Nazari Shirehjini ◽  
Farideh Soltani Nejad ◽  
Gazelle Saniee-Monfared ◽  
Azin Semsar ◽  
Shervin Shirmohammadi
Keyword(s):  
Virtual Environments ◽  
Mental Model ◽  
Model Development ◽  
3D Virtual Environments

Surrogate-Based Correlation Models in View of Real-Time Control of Ozonation of Secondary Treated Municipal Wastewater—Model Development and Dynamic Validation

2017 ◽  
Vol 51 (24) ◽  
pp. 14233-14243 ◽  
Author(s):  
Michael Chys ◽  
Wim T. M. Audenaert ◽  
Emma Deniere ◽  
Séverine Thérèse F. C. Mortier ◽  
Herman Van Langenhove ◽  
...  
Keyword(s):  
Real Time ◽  
Municipal Wastewater ◽  
Model Development ◽  
Real Time Control ◽  
Time Control ◽  
Correlation Models ◽  
Treated Municipal Wastewater
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