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Challenges and Strategies in Teaching Linear Algebra

Overview of attention for book
Cover of 'Challenges and Strategies in Teaching Linear Algebra'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 The Learning and Teaching of Linear Algebra Through the Lenses of Intellectual Need and Epistemological Justification and Their Constituents
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    Chapter 2 Learning Linear Algebra Using Models and Conceptual Activities
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    Chapter 3 Moving Between the Embodied, Symbolic and Formal Worlds of Mathematical Thinking with Specific Linear Algebra Tasks
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    Chapter 4 Conceptions About System of Linear Equations and Solution
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    Chapter 5 Rationale for Matrix Multiplication in Linear Algebra Textbooks
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    Chapter 6 Misconceptions About Determinants
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    Chapter 7 Dealing with the Abstraction of Vector Space Concepts
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    Chapter 8 Stretch Directions and Stretch Factors: A Sequence Intended to Support Guided Reinvention of Eigenvector and Eigenvalue
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    Chapter 9 Examining Students’ Procedural and Conceptual Understanding of Eigenvectors and Eigenvalues in the Context of Inquiry-Oriented Instruction
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    Chapter 10 Mental Schemes of: Linear Algebra Visual Constructs
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    Chapter 11 How Does a Dynamic Geometry System Mediate Students’ Reasoning on 3D Linear Transformations?
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    Chapter 12 Fostering Students’ Competencies in Linear Algebra with Digital Resources
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    Chapter 13 Linear Algebra—A Companion of Advancement in Mathematical Comprehension
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    Chapter 14 A Computational Approach to Systems of Linear Equations
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    Chapter 15 Nonnegative Factorization of a Data Matrix as a Motivational Example for Basic Linear Algebra
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    Chapter 16 Motivating Examples, Meaning and Context in Teaching Linear Algebra
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    Chapter 17 Holistic Teaching and Holistic Learning, Exemplified Through One Example from Linear Algebra
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    Chapter 18 Using Challenging Problems in Teaching Linear Algebra
Attention for Chapter 15: Nonnegative Factorization of a Data Matrix as a Motivational Example for Basic Linear Algebra
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About this Attention Score

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  • Good Attention Score compared to outputs of the same age and source (68th percentile)

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Citations

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Chapter title
Nonnegative Factorization of a Data Matrix as a Motivational Example for Basic Linear Algebra
Chapter number 15
Book title
Challenges and Strategies in Teaching Linear Algebra
Published in
arXiv, January 2018
DOI 10.1007/978-3-319-66811-6_15
Book ISBNs
978-3-31-966810-9, 978-3-31-966811-6
Authors

Barak A. Pearlmutter, Helena Šmigoc, Pearlmutter, Barak A., Šmigoc, Helena

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 40%
Professor 1 20%
Researcher 1 20%
Student > Master 1 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 20%
Computer Science 1 20%
Social Sciences 1 20%
Unknown 2 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 May 2020.
All research outputs
#14,084,370
of 23,852,579 outputs
Outputs from arXiv
#239,340
of 990,619 outputs
Outputs of similar age
#225,463
of 446,688 outputs
Outputs of similar age from arXiv
#6,056
of 21,090 outputs
Altmetric has tracked 23,852,579 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 990,619 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 446,688 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 21,090 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.