Muutke küpsiste eelistusi

E-raamat: Innovative Learning Analytics for Evaluating Instruction: A Big Data Roadmap to Effective Online Learning [Taylor & Francis e-raamat]

, , ,
  • Formaat: 132 pages, 20 Tables, black and white; 20 Line drawings, black and white; 7 Halftones, black and white; 27 Illustrations, black and white
  • Ilmumisaeg: 20-Jul-2021
  • Kirjastus: Routledge
  • ISBN-13: 9781003176343
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 58,15 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 83,08 €
  • Säästad 30%
Innovative Learning Analytics for Evaluating Instruction: A Big Data Roadmap to Effective Online LearningSuurem pilt
  • Formaat: 132 pages, 20 Tables, black and white; 20 Line drawings, black and white; 7 Halftones, black and white; 27 Illustrations, black and white
  • Ilmumisaeg: 20-Jul-2021
  • Kirjastus: Routledge
  • ISBN-13: 9781003176343
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003176343

Innovative Learning Analytics for Evaluating Instruction covers the application of a forward-thinking research methodology that uses big data to evaluate the effectiveness of online instruction. Analysis of Patterns in Time (APT) is a practical analytic approach that finds meaningful patterns in massive data sets, capturing temporal maps of students’ learning journeys by combining qualitative and quantitative methods. Offering conceptual and research overviews, design principles, historical examples, and more, this book demonstrates how APT can yield strong, easily generalizable empirical evidence through big data; help students succeed in their learning journeys; and document the extraordinary effectiveness of First Principles of Instruction. It is an ideal resource for faculty and professionals in instructional design, learning engineering, online learning, program evaluation, and research methods.



Innovative Learning Analytics for Evaluating Instruction covers the application of a research methodology that uses big data to evaluate the effectiveness of online instruction. It is an ideal resource for faculty and professionals in instructional design, learning engineering, online learning, program evaluation, and research methods.

Foreword ix
Preface xi
Chapter Summaries xvii
List of Tables, Figures, and Maps
xx
1 Learning Journeys in Education
1(8)
Metaphor of a Journey: The Oregon Trail
1(1)
The State-Trait Approach to Measurement: Quantitative Methods
2(1)
Individual Episodic Stories: Qualitative Methods
3(1)
Qualitative Temporal Mapping that is Quantifiable and Generalizable: A Third Alternative for Educational Research Methods
4(1)
The Larger Problem in Educational Research
5(1)
References
6(3)
2 Overview of the Big Study
9(19)
A Tale of Two Learning Journeys
9(1)
Learning Journey #1: Sam's Case
9(4)
Learning Journey #2: Melinda's Case
13(5)
Design and Structure of IPTAT Using First Principles of Instruction
18(1)
Problem-Centered Principle
18(1)
Activation Principle
18(1)
Demonstration Principle
19(1)
Application Principle
19(1)
Integration Principle
19(1)
Results from 936, 417 Learning Journeys through IPTAT in the Big Study in 2019 and 2020
20(2)
Activation Results
22(1)
Demonstration Results
23(1)
Application Principle
24(1)
Integration Principle
25(1)
Overall Unique Page views
25(1)
What Does All This Mean?
25(2)
References
27(1)
3 The Indiana University Plagiarism Tutorials and Tests
28(18)
Introduction
28(2)
Major Redesign of IPTAT in 2015
30(1)
Authentic Problems Principle
31(1)
Activation Principle
31(3)
Demonstration Principle
34(1)
Application Principle
34(1)
Integration Principle
35(1)
Certification Tests
35(3)
Summary of Changes to the Newly Designed IPTAT in 2015
38(1)
What's the Same?
39(1)
Usage of IPTAT from 2019 and 2020
40(1)
Minimalists
41(1)
Traditionalists
42(1)
Dabblers
42(1)
Registrants
42(1)
User-Developer Feedback Loop
43(1)
What About COVID?
44(1)
References
45(1)
4 More Details of the Big Study
46(17)
Discovery of Google Analytics for Doing APT
46(2)
Important Concepts for Doing Analysis ofPatterns in Time
48(1)
Two Fundamental Ways of Temporal Segmenting: Prediction and Retrodiction
48(1)
Temporal Segmenting by Quarters One at a Time
49(1)
Retrodictive APT Queries
50(1)
Endpoint Condition Defined
50(1)
Users Who Passed
50(1)
Users Who Have Not Passed
50(1)
Apply the Endpoint Condition to the GA Audience Reporting Tool (UA)
50(1)
Using GA to Find Matches of Page views within Segments
51(2)
Wash, Rinse, and Repeat, then Combine
53(1)
Some Key Issues We Identified and Resolved to do APT of IPTAT Data Streams Created by GA
53(3)
Can You Do APT with GA4?
56(3)
Who Are the Registered IPTAT Users in 2019 and 2020 (from Our MySQL Database at IU)?
59(3)
Summary
62(1)
References
62(1)
5 Analysis of Patterns in Time as a Research Methodology
63(15)
Introduction
63(1)
APT of Direct Instruction and Academic Learning Time: Joint Event Occurrences
64(1)
Linear Models Approach
65(1)
APT Approach
65(1)
APT of Teacher-Student Interaction in Class: Frequency of Sequential Events
66(2)
APT of Asynchronous Online Discussion: Sequential Patterns of Comments
68(4)
APT Outside of Education
72(1)
Moneyball
72(2)
Google Analytics
74(1)
References
75(3)
6 Using Analysis of Patterns in Time for Formative Evaluation of a Learning Design
78(15)
Introduction
78(1)
Simulation Fidelity
79(1)
Evaluation of Fidelity
80(1)
Using APT for Model Verification
81(1)
The Diffusion Simulation Game
82(1)
Applying the APT Procedure to the DSG
83(3)
Data Analysis and Results
86(3)
Conclusion
89(1)
References
90(3)
7 Analysis of Patterns in Time with Teaching and Learning Quality Surveys
93(11)
APT of Course Evaluations
93(1)
The Goal: Creating a Table from a Spreadsheet
94(1)
Formation of TALQ Scales
95(2)
Transferring MOO-TALQ Survey Responses to a Spreadsheet
97(1)
Creating Spreadsheet Formulas for Each TALQ Scale
98(1)
Creating Further Derived Scores for Scale Agreement (Yes or No)
99(1)
Creating a Table for the Combinations of Categories
100(2)
Summary
102(1)
References
103(1)
8 Analysis of Patterns in Time as an Alternative to Traditional Approaches
104(11)
Making Inductive Inferences with APT
104(3)
Big Data in Education
107(1)
Approaches to Big Data
108(1)
Methods Used to Analyze Big Data
108(1)
Learning Analytics and Instructional Design
109(1)
The Value of Theory to Guide Educational Research
110(2)
Extending APT
112(1)
References
112(3)
Epilogue 115(10)
Abbreviations and Symbols 125(4)
Index 129
Theodore W. Frick is Professor Emeritus in the Department of Instructional Systems Technology in the School of Education at Indiana University Bloomington, USA.

Rodney D. Myers is Instructional Consultant in the School of Education at Indiana University Bloomington, USA.

Cesur Dagli is Research Analyst in the Office of Analytics & Institutional Effectiveness at Virginia Polytechnic Institute and State University, USA.

Andrew F. Barrett is Co-founder of ScaleLearning, Inc. and leads the Learning Technology team at Shopify, Inc., Canada
Püsilink: https://www.kriso.ee/db/9781003176343_pe.html
Märksõnad: