Perinatal Influences on the Behavior and Learning Problems of Children

1977 ◽  
pp. 119-160 ◽  
Author(s):  
Rosalyn A. Rubin ◽  
Bruce Balow
Keyword(s):  
Learning Problems

Frequent TV Linked to Attention, Learning Problems in Adolescents

2007 ◽  
Vol 41 (6) ◽  
pp. 25
Author(s):  
MARY ANN MOON
Keyword(s):  
Learning Problems

Learning problems and learning disabilities

2008 ◽  
Author(s):  
Howard Adelman ◽  
Linda Taylor
Keyword(s):  
Learning Disabilities ◽  
Learning Problems

Interrelation of Pattern Recognition, Machine Thinking and Learning Problems

2020 ◽  
Vol 52 (5) ◽  
pp. 51-78
Author(s):  
Vladimir I. Gritsenko ◽  
Mikhail I. Schlesinger
Keyword(s):  
Pattern Recognition ◽  
Learning Problems

Análisis de las dificultades que enfrentan los alumnos en relación a la enseñanza del tema los efectos de la Presión Atmosférica

2018 ◽  
Vol 16 (4) ◽  
pp. 167
Author(s):  
Guillermo Salgado ◽  
José Navarrete ◽  
Juan Gabriel Morcillo Ortega ◽  
Manuela Martín Sánchez ◽  
María Teresa Martín Sánchez ◽  
...  
Keyword(s):  
Primary School ◽  
Atmospheric Pressure ◽  
Teaching And Learning ◽  
Learning Problems ◽  
Historical Study

<span>This paper is an analysis of student's problems to understand the concept atmospheric pressure and its influence in different experimental processes. We present an historical study of the topic, a bibliographical revision of research relative to teaching and learning problems in the case of fluids and the results of our investigation with two groups of students one of 11 - 12 years old and other of training teachers of primary school.</span>

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

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