Autonomous Robots as a Generic Teaching Tool

2006 ◽  
Author(s):  
Abraham Howell ◽  
Eileen Way ◽  
Roy McGrann ◽  
Robert Woods
Keyword(s):  
Autonomous Robots ◽  
Teaching Tool ◽  
Generic Teaching

Computers as a Teaching Tool for 5-Year-Old Preschool Children

2014 ◽  
Author(s):  
Celso S. Oliveira ◽  
Maria Fernanda C. Alvarez ◽  
Ana Beatriz N. Ferreira
Keyword(s):  
Preschool Children ◽  
Teaching Tool

Developing Awareness of Minority Cultures: A Teaching Tool

2006 ◽  
Author(s):  
Darrell W. Jones ◽  
Theodore Nnaji ◽  
Leon Vandecreek
Keyword(s):  
Teaching Tool ◽  
Minority Cultures

CAR SEAT SAFETY TEACHING TOOL

2001 ◽  
Vol 1 (1) ◽  
pp. 53-56 ◽  
Author(s):  
&NA;
Keyword(s):  
Teaching Tool ◽  
Car Seat

Evaluating The Effectiveness Of Problem-Based Learning (Pbl) Implemented In The Textile Engineering Course - A Case Study On Ahsanullah University Of Science And Technology, Bangladesh

2014 ◽  
Vol 1 (1) ◽  
pp. 111-114
Author(s):  
Lal Mohan Baral ◽  
Ramzan Muhammad ◽  
Claudiu Vasile Kifor ◽  
Ioan Bondrea
Keyword(s):  
Engineering Students ◽  
Science And Technology ◽  
Problem Based Learning ◽  
Professional Life ◽  
System Level ◽  
Teaching Tool ◽  
Technical Problems ◽  
Empirical Survey ◽  
Undergraduate Programs

AbstractProblem-based learning as a teaching tool is now used globally in many areas of higher education. It provides an opportunity for students to explore technical problems from a system-level perspective and to be self-directed life-long learner which is mandatory for equipping engineering students with the skill and knowledge. This paper presents a case study illustrating the effectiveness of implemented Problem-based learning (PBL) during five semesters in the undergraduate programs of Textile Engineering in Ahsanullah University of Science and Technology (AUST). An assessment has been done on the basis of feedback from the students as well as their employers by conducting an empirical survey for the evaluation of PBL impact to enhance the student's competencies. The Evaluations indicate that students have achieved remarkable competencies through PBL practices which helped them to be competent in their professional life.

Integrating E-Reading Into Traditional Classrooms Of Budding Engineers

Think India ◽  
2019 ◽  
Vol 22 (2) ◽  
pp. 458-462
Author(s):  
SABITHA ◽  
PREM CHANDRIKA
Keyword(s):  
Information Technology ◽  
Teaching Tool ◽  
Traditional Classrooms ◽  
Written Text ◽  
E Learning

The paper focuses on the need of E-Reading and integration of E-Reading into the classrooms with Information Technology Enabled Services (ITES).Computer has the potential to become the amazing teaching tool. Besides the written text the technical way of learning has quality, rhythm, tenor and pitch. These parameters vary as per the moods of the teacher in the traditional classrooms. But in the e-learning there is no mood swings to the inanimate objects like video or audio. So, it is the teachers’ ingenuity to select the suitable e-content to the students and make use of the instruments to the fullest extent for the benefit of the students. The ultimate goal of the teacher is to make the learners as independent readers.

LEARNING NAIVE PHYSICS BY VISUAL OBSERVATION: USING QUALITATIVE SPATIAL REPRESENTATIONS AND PROBABILISTIC REASONING

2001 ◽  
Vol 01 (03) ◽  
pp. 273-285 ◽  
Author(s):  
PAUL A. BOXER
Keyword(s):  
Bayesian Network ◽  
Probabilistic Reasoning ◽  
Autonomous Robots ◽  
Visual Observation ◽  
Spatial Representations ◽  
Physical Behavior ◽  
Naive Physics ◽  
Action At A Distance ◽  
General Physical ◽  
Test Scenarios

Autonomous robots are unsuccessful at operating in complex, unconstrained environments. They lack the ability to learn about the physical behavior of different objects through the use of vision. We combine Bayesian networks and qualitative spatial representation to learn general physical behavior by visual observation. We input training scenarios that allow the system to observe and learn normal physical behavior. The position and velocity of the visible objects are represented as qualitative states. Transitions between these states over time are entered as evidence into a Bayesian network. The network provides probabilities of future transitions to produce predictions of future physical behavior. We use test scenarios to determine how well the approach discriminates between normal and abnormal physical behavior and actively predicts future behavior. We examine the ability of the system to learn three naive physical concepts, "no action at a distance", "solidity" and "movement on continuous paths". We conclude that the combination of qualitative spatial representations and Bayesian network techniques is capable of learning these three rules of naive physics.

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