The Preference Matrix As A Course Design Tool

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The Preference Matrix As A Course Design Tool John Paxton Montana State University Universität

The Preference Matrix As A Course Design Tool John Paxton Montana State University Universität Leipzig Koli Calling Saturday, November 11, 2006

Outline I. III. IV. Introduction Application Evaluation Discussion

Outline I. III. IV. Introduction Application Evaluation Discussion

I. Introduction

I. Introduction

Preference Matrix • Developed by Stephen and Rachel Kaplan at The University of Michigan

Preference Matrix • Developed by Stephen and Rachel Kaplan at The University of Michigan • Based on evolutionary psychology • Each individual must build a cognitive map in order to survive • A cognitive map allows recognition, prediction and evaluation

Familiarity Matrix Low Preference High Preference Low Familiarity Strange Fascinating High Familiarity Boring Comfortable

Familiarity Matrix Low Preference High Preference Low Familiarity Strange Fascinating High Familiarity Boring Comfortable

Preference Matrix Makes Sense Present Future Involvemen t Coherence Complexity Legibility Mystery

Preference Matrix Makes Sense Present Future Involvemen t Coherence Complexity Legibility Mystery

II. Application

II. Application

Preference Matrix Pedagogy • Connect new knowledge to existing knowledge “makes sense” • Don’t

Preference Matrix Pedagogy • Connect new knowledge to existing knowledge “makes sense” • Don’t overwhelm short-term memory “makes sense” • Material should engage learner “involvement” • Background of learner must be roughly understood “involvement”

CS 436 • A senior level course that introduces artificial intelligence • Making sense:

CS 436 • A senior level course that introduces artificial intelligence • Making sense: clear objectives, clear syllabus, all graded work is related to the objectives, clear presentation • Involvement: engaging assignments, classroom participation

III. Evaluation

III. Evaluation

Evaluation • Fall 2004 – Spring 2006 • Senior level computer science courses at

Evaluation • Fall 2004 – Spring 2006 • Senior level computer science courses at Montana State University • 2 offerings of CS 436 (33 students) • 17 other offerings (225 students)

Evaluation 1. How does this course compare with similar technical courses? 2. What is

Evaluation 1. How does this course compare with similar technical courses? 2. What is your level of interest in taking an advanced course? 3. Did you find this course challenging? 4. Were the objectives of the course clearly stated?

Evaluation 5. Were the objectives of the course met? 6. How important were the

Evaluation 5. Were the objectives of the course met? 6. How important were the lectures? 7. How important were the assignments/programs? 8. How important were the tests/quizzes?

Evaluation Question ¬AI mean σ Improve 1 2. 19 1. 58 0. 96 51%

Evaluation Question ¬AI mean σ Improve 1 2. 19 1. 58 0. 96 51% 2 2. 53 1. 67 1. 29 56% 3 1. 92 1. 52 0. 84 43% 4 1. 83 1. 24 0. 95 71%

Evaluation Question ¬AI mean σ Improve 5 1. 93 1. 33 0. 91 65%

Evaluation Question ¬AI mean σ Improve 5 1. 93 1. 33 0. 91 65% 6 1. 79 1. 52 0. 99 34% 7 1. 82 1. 33 0. 91 60% 8 2. 04 1. 94 0. 98 10%

IV. Discussion • What are appropriate research methodologies for measuring the impact of the

IV. Discussion • What are appropriate research methodologies for measuring the impact of the preference matrix in a convincing manner?