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

Information Processing Theory

  •  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

♡2020 by Bhoomika Agarwal. Copying is an act of love. Love is not subject to law. Please copy.

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