Throwing Down the Visual Intelligence Gauntlet

In solid geometry needed the ability to visualize space that cannot be seen so differences in visual intelligence can trigger errors in solving geometry problems. Therefore, this study aimed to determine the types of errors made by students and the factors that cause errors in working on geometry problems based on Newman's Error Analysis (NEA) in terms of visual intelligence. This research chose the subjects of class XII high school who had studied the material distance points to lines in space with different levels of visual intelligence, namely high and low. The process of collecting data used visual intelligence test questions, geometry test questions, and interviews. The results of this study indicated that the level of students' visual intelligence affects the difference in location and the factor that causes errors in the stage of transformation especially on determining the distance in dimension 3. Students with high visual intelligence made mistakes at the stage of transformation and encoding that caused by the students’ carelessness. However, students with low visual intelligence made mistakes at the stage of comprehension, transformation, process skills, and encoding due to the concept of the point distance to the line and the concept of the Pythagorean theorem.

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Using Game-Based Learning to Improve Boys' Literacy

10.4018/978-1-7998-7271-9.ch013 ◽

2022 ◽

pp. 249-268

Author(s):

Minda M. B. Marshall ◽

Marinda Marshall

Keyword(s):

South African ◽

Reading Fluency ◽

Visual Processing ◽

Intervention Program ◽

Reading Speed ◽

Literacy Intervention ◽

Game Based Learning ◽

Perceptual Development ◽

Whole School ◽

Visual Intelligence

This chapter foregrounds an online gamified visual intelligence innovation (eyebraingym) developed to enhance visual processing skills, improve memory and vocabulary, and increase reading fluency. The explicit aim of the innovation is to improve comprehension towards visual intelligence. Ninety-eight Grade 8 learners at a South African Boy's School completed their online development during the 2021 academic year. These learners were part of a group of students participating in a whole school reading and literacy intervention program. The innovation is an integral part of this ongoing project. Their interaction with the innovation consists of 15 sessions completed once or twice a week for 20 – 40 minutes over five months. The results of the project are positive. It shows that most participating students improved their perceptual development and reading speed (VPF) and cognitive development and comprehension skills (CDF). In addition, these outcomes transferred to improved relative efficiency when working with information (AIUF).

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Deep Learning: The Good, the Bad, and the Ugly

Annual Review of Vision Science ◽

10.1146/annurev-vision-091718-014951 ◽

2019 ◽

Vol 5 (1) ◽

pp. 399-426 ◽

Cited By ~ 27

Author(s):

Thomas Serre

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Machine Vision ◽

Artificial Vision ◽

Vision Systems ◽

Comprehensive Overview ◽

Biological Vision ◽

Recent Developments ◽

Visual Intelligence ◽

Modern Machine

Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision—giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems.

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