Educational Technologies: Conceptual model

Group 3

Kanya Paramita
Bhoomika Agarwal

April 1, 2020

Conceptual model

• Problem Definition
• Learning Objectives and Outcomes
• Learning Paradigm
• Learning Activity
• Performance Measures
• Toolings

Problem Definition

Eigen-Value Decomposition at University level

Eigen-values & Eigen-vectors??

Problem statement

Lacking in contextual and visual learning of Eigenvectors and Eigenvalues concepts

Aims of our learning framework

• Visualize the Eigenvalues and Eigenvectors and learn the concepts related to them
• Find solutions to linear algebra problems using a procedural and algorithmic approach
• Learn and retain the math principles associated with these concepts

Learning Objectives and Outcomes

Apply basic concepts of Eigenvalues and Eigenvectors

Learning objectives

• Define Eigenvalues and Eigenvectors problems
• Find Eigenvalues and Eigenvectors geometrically and numerically
• Understand Eigenvalues and Eigenvectors theorems
• Give examples of Eigenvalues and Eigenvectors application in Computer Science

Learning Paradigm

1. Information Processing Theory

2. Domain-specific Learning

•  Meaningful encoding - Information should be meaningful and related to existing knowledge
  
• Retrieval structure -  Cues should also be stored for easy retrieval in the future with connections
  
• Speedup - Extensive practice improves encoding and retrieval
• Chunking- human brain can only chunk knowledge into the brain with 7 parts, plus or minus two

Domain-specific Learning

• Students learn better when the curriculum is tailored according to the their domain of study
• We should design curriculum based on domain-specific knowledge
• Implement this paradigm by following an algorithmic approach for computer science majors

Learning Activity

Our solution proposal

Will domain-specific learning for Computer Science using a contextual and algorithmic approach help students learn better?

Modules

•  Module 1: Introduction to Eigenvalues and Eigenvectors
• Module 2: Properties of Eigenvalues and Eigenvectors
• Module 3: Finding Eigenvalues and Eigenvectors
• Module 4: Eigenvalues and Eigenvectors Theorems
• Module 5: Computing Eigenvalues and Eigenvectors in Python
• Module 6: Application of Eigenvalues and Eigenvectors in Computer Science

Proposed learning activities

•  Step-wise learning with examples
• Flashcards for learning
• Programming challenges
• Quizzes
•  Application based learning

Performance Measures

Quantification of learning

Assessment

• Quizzes - calculations, multiple choice questions, fill in the blanks, true and false
• Mini programming challenges - mainly using Python programming language
• Flash card based puzzles - during and after the video lessons

Toolings

Enabling technology for learning

Toolings

• Video player
• Transcripts
• Work-space
• IDE
• Learning Assistant
• Course Explorer

Thank you for your attention!

Bhoomika Agarwal

Debadeep Basu

Ramya Praneetha

Kanya Paramita

Paras Kumar

Reinier Koops

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