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E-grāmata: Adoption of Data Analytics in Higher Education Learning and Teaching

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Adoption of Data Analytics in Higher Education Learning and TeachingPalielināt
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The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets while learning analytics emphasizes insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms.

This volume provides insight into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organizational transformation toward implementation of educational data mining and learning analytics. It features current research exploring the (a) theoretical foundation and empirical evidence of the adoption of learning analytics, (b) technological infrastructure and staff capabilities required, as well as (c) case studies that describe current practices and experiences in the use of data analytics in higher education.


Part I Focussing the Organisation in the Adoption Process
1 Adoption of Learning Analytics
3(18)
David Gibson
Dirk Ifenthaler
1.1 Introduction
3(1)
1.2 Innovation Diffusion
4(3)
1.2.1 Six Characteristics of an Innovation
4(2)
1.2.2 Communication Channels
6(1)
1.2.3 Encompassing Social Systems
6(1)
1.2.4 Summary of Innovation Diffusion
7(1)
1.3 Improving Higher Education with the Adoption of Learning Analytics
7(7)
1.3.1 Acquiring Students
8(1)
1.3.2 Promoting Learning
8(1)
1.3.3 Offering Timely Relevant Content
9(1)
1.3.4 Delivery Methods
10(1)
1.3.5 Supporting Alumni Networks
10(1)
1.3.6 Cases
11(3)
1.4 Discussion and Outlook
14(7)
References
16(5)
2 The Politics of Learning Analytics
21(18)
Reem Al-Mahmood
2.1 Unfolding Scenarios
21(1)
2.2 The Promises and Challenges of Big Data and Learning Analytics for Higher Education
22(2)
2.3 Ethical and Legal Frameworks of Big Data and Learning Analytics
24(1)
2.4 Knowledge Production: Algorithmic and Datafied Education and Its Consequences
25(6)
2.4.1 On Algorithms and Data
26(2)
2.4.2 On Interpretation and Contextualisation
28(1)
2.4.3 On Communicating and Adopting a Learning Analytics Culture
29(1)
2.4.4 On Data Reduction and Knowledge Production
30(1)
2.5 Ecologies of Learning: Towards a Measured Learning Analytics
31(1)
2.6 Implications for Universities Adopting Learning Analytics
31(8)
References
35(4)
3 A Framework to Support Interdisciplinary Engagement with Learning Analytics
39(14)
Stephanie J. Blackmon
Robert L. Moore
3.1 Introduction
39(3)
3.1.1 What We Mean by Interdisciplinary
40(1)
3.1.2 Big Data and Learning Analytics
40(2)
3.2 Learning Analytics in Higher Education
42(2)
3.2.1 Organizational Drivers
42(1)
3.2.2 Classroom-Level Use of Learning Analytics
43(1)
3.3 An Interdisciplinary Approach
44(5)
3.3.1 Awareness -- What Is Being Collected and Why
45(1)
3.3.2 Access -- Who Can Get to the Data
46(1)
3.3.3 Resources -- Where Is the Data Stored
47(2)
3.4 Future Directions
49(1)
3.5 Conclusion
50(3)
References
50(3)
4 The Framework of Learning Analytics for Prevention, Intervention, and Postvention in E-Learning Environments
53(18)
Muhittin Sahin
Halil Yurdugiil
4.1 Introduction
53(6)
4.1.1 Prevention
55(1)
4.1.2 Intervention
56(1)
4.1.3 Postvention
57(1)
4.1.4 Differences Between Prevention, Intervention, and Postvention
58(1)
4.2 Proposed Framework
59(5)
4.2.1 Dropout
60(2)
4.2.2 Avoidance of Learning Activities
62(1)
4.2.3 Failing Learning Performance
62(1)
4.2.4 Locus of Control
63(1)
4.2.5 Academic Procrastination
63(1)
4.3 Conclusion and Discussion
64(7)
References
66(5)
5 The LAVA Model: Learning Analytics Meets Visual Analytics
71(24)
Mohamed Amine Chatti
Arham Muslim
Manpriya Guliani
Mouadh Guesmi
5.1 Introduction
71(1)
5.2 Human-Centered Learning Analytics
72(1)
5.3 Visual Analytics
73(1)
5.4 The LAVA Model
74(2)
5.5 The LAVA Model in Action
76(11)
5.5.1 Learning Activities
78(1)
5.5.2 Data Collection
78(1)
5.5.3 Data Storage and Pre-processing
78(1)
5.5.4 Analysis
79(1)
5.5.5 Visualization
79(1)
5.5.6 Perception and Knowledge
79(1)
5.5.7 Exploration
80(7)
5.5.8 Action
87(1)
5.6 Evaluation
87(4)
5.6.1 Method
88(1)
5.6.2 Usefulness
89(1)
5.6.3 Usability
90(1)
5.7 Conclusion
91(4)
References
91(4)
6 See You at the Intersection: Bringing Together Different Approaches to Uncover Deeper Analytics Insights
95(18)
David Paul Fulcher
Margaret Wallace
Maarten de Laat
6.1 Introduction
95(1)
6.2 The Story So Far
96(4)
6.2.1 Centralized Support
98(1)
6.2.2 System Generated Reports
99(1)
6.3 Research Sprints
100(6)
6.3.1 The First Year Chemistry Curriculum
102(1)
6.3.2 The French Language Curriculum
103(2)
6.3.3 The Analysis of Student Course Progress
105(1)
6.4 Conclusion
106(7)
6.4.1 Future Directions
107(2)
References
109(4)
7 "Trust the Process!": Implementing Learning Analytics in Higher Education Institutions
113(24)
Armin Egetenmeier
Miriam Hommel
7.1 Introduction
113(1)
7.2 Adoption of Learning Analytics
114(6)
7.2.1 Issues and Challenges of LA Adoption
115(2)
7.2.2 Leadership of LA Adoption
117(1)
7.2.3 Models of LA Adoption
118(2)
7.3 Adapted Roma Model for Bottom-Up Adoption
120(1)
7.4 Adoption of Learning Analytics at Aalen UAS
121(6)
7.4.1 A Small Project as Starting Point
121(2)
7.4.2 Closing the Gap Between Teachers and Learners
123(2)
7.4.3 Extension to Higher Levels
125(1)
7.4.4 Summary of the Adoption Process
126(1)
7.5 Outlook and Conclusion
127(10)
References
131(6)
Part II Focussing the Learner and Teacher in the Adoption Process
8 Students' Adoption of Learner Analytics
137(22)
Carly Palmer Foster
8.1 Introduction
137(3)
8.2 Methodology
140(2)
8.3 Results
142(10)
8.3.1 Implementation of a Learner Analytics Platform
143(1)
8.3.2 Adoption of Connect Analytics in the Live Pilot
144(6)
8.3.3 Students' Feedback on Connect Analytics After the Live Pilot
150(2)
8.4 Discussion: Understanding Students' Adoption of Learner Analytics
152(3)
8.5 Conclusions
155(4)
References
156(3)
9 Learning Analytics and the Measurement of Learning Engagement
159(18)
Dirk Tempelaar
Quan Nguyen
Bart Rienties
9.1 Introduction
159(1)
9.2 This Study
160(4)
9.2.1 Context
161(1)
9.2.2 Instrument and Procedure
162(2)
9.2.3 Data Analysis
164(1)
9.3 Results
164(7)
9.3.1 Descriptive Statistics of Survey-Based Measures
164(1)
9.3.2 Cluster-Based Learning Profiles
165(2)
9.3.3 Learning Profiles and Course Performance
167(1)
9.3.4 Bivariate Relationships Between Engagement Indicators and Course Performance
168(1)
9.3.5 Multivariate Relationships Between Engagement Indicators and Course Performance
169(1)
9.3.6 Bivariate Relationships Between Survey-Based Engagement Scores and Log-Based Engagement Indicator
170(1)
9.4 Findings and Discussion
171(4)
9.5 Conclusion
175(2)
References
175(2)
10 Stakeholder Perspectives (Staff and Students) on Institution-Wide Use of Learning Analytics to Improve Learning and Teaching Outcomes
177(24)
Ann Luzeckyj
Deborah S. West
Bill K. Searle
Daniel P. Toohey
Jessica J. Vanderlelie
Kevin R. Bell
10.1 Introduction and Context
177(2)
10.2 Approach
179(1)
10.3 Staff Perspectives on LA
180(5)
10.4 Students' Perspectives on LA
185(6)
10.5 Comparing Responses from Staff and Students -- the `Standout' Messages
191(6)
10.5.1 Awareness of Learning Analytics and Data Collection
191(2)
10.5.2 How LA Might Be Used to Support Learning
193(1)
10.5.3 Concerns
194(2)
10.5.4 Practical Actions for More Effective Use of LA
196(1)
10.6 Conclusion
197(4)
References
198(3)
11 How and Why Faculty Adopt Learning Analytics
201(20)
Natasha Arthars
Danny Y.-T. Liu
11.1 Introduction
201(1)
11.2 Background
202(4)
11.2.1 Learning Analytics Implementation and Adoption: Institutions
202(1)
11.2.2 Learning Analytics Implementation and Adoption: Teachers
203(2)
11.2.3 Theoretical Framework-Diffusion of Innovations
205(1)
11.3 Methods
206(5)
11.3.1 Research Questions
206(1)
11.3.2 The SRES as a LA Platform
207(3)
11.3.3 Data Collection
210(1)
11.4 Findings
211(6)
11.4.1 Perceived Attributes of the Innovation
211(1)
11.4.2 Relative Advantage
212(4)
11.4.3 Communication Channels
216(1)
11.5 Discussion & Conclusions
217(4)
References
219(2)
12 Supporting Faculty Adoption of Learning Analytics within the Complex World of Higher Education
221(20)
George Rehrey
Marco Molinaro
Dennis Groth
Linda Shepard
Caroline Bennett
Warren Code
Amberly Reynolds
Vicki Squires
Doug Ward
12.1 Introduction
221(4)
12.1.1 Background
222(2)
12.1.2 The Bay View Alliance
224(1)
12.1.3 The Learning Analytics Research Collaborative
224(1)
12.2 The Cycle of Progress for Sustainable Change
225(3)
12.2.1 Awareness
226(1)
12.2.2 Understanding
227(1)
12.2.3 Action
227(1)
12.2.4 Reflection
227(1)
12.3 Methodolgy
228(1)
12.4 Results
229(4)
12.4.1 Vignettes
229(2)
12.4.2 Cultural Change Indicators
231(1)
12.4.3 Program Support
232(1)
12.5 Discussion
233(4)
12.5.1 Commonalities and Contrasts
233(1)
12.5.2 Theoretical Framework
234(1)
12.5.3 Implications and Limitations
235(1)
12.5.4 Future Directions
236(1)
12.6 Conclusion
237(4)
References
238(3)
13 It's All About the Intervention: Reflections on Building Staff Capacity for Using Learning Analytics to Support Student Success
241(16)
Ed Foster
Rebecca Siddle
Pete Crowson
Pieterjan Bonne
13.1 Introduction
241(1)
13.2 Learning Analytics
242(1)
13.3 How Tutors Support Students
243(2)
13.4 Enhancing the Tutoring/Advising Process Using Learning Analytics
245(3)
13.4.1 Methodology
246(1)
13.4.2 Case Study: Using Learning Analytics to Support Students at Nottingham Trent University
247(1)
13.5 Trigger/Prompt
248(1)
13.6 Communication
249(1)
13.7 Intervention
250(2)
13.7.1 Summary for Building Staff Capacity to Support Students Using Learning Analytics
252(1)
13.8 Institutional Recommendations
252(1)
13.9 Conclusions
253(4)
References
254(3)
14 Experiences in Scaling Up Learning Analytics in Blended Learning Scenarios
257(28)
Vlatko Lukarov
Ulrik Schroeder
14.1 Introduction
257(2)
14.2 Methodology
259(3)
14.2.1 Collecting Learning Analytics Requirements
260(1)
14.2.2 Evaluation Strategies
261(1)
14.3 Scaling Up Learning Analytics
262(10)
14.3.1 Building the Requirements
262(4)
14.3.2 Institutional Regulation Preparation
266(1)
14.3.3 Learning Analytics Services Implementation
267(1)
14.3.4 Data Management
268(2)
14.3.5 Analytics Engine
270(1)
14.3.6 Results Visualization
271(1)
14.4 Evaluation Strategies for LA
272(5)
14.4.1 Study Setting
272(3)
14.4.2 Evaluation Findings
275(2)
14.5 Lessons Learned and Conclusions
277(8)
References
279(6)
Part III Cases of Learning Analytics Adoption
15 Building Confidence in Learning Analytics Solutions: Two Complementary Pilot Studies
285(20)
Armelle Brun
Benjamin Gras
Agathe Merceron
15.1 Introduction
285(1)
15.2 Related Works
286(2)
15.3 1st Pilot Study: Mining Academic Data
288(7)
15.3.1 Context and Goals
289(1)
15.3.2 Graduating Versus Dropping out
290(2)
15.3.3 Typical Completing Behaviors
292(2)
15.3.4 Discussion
294(1)
15.4 2nd Pilot Study
295(6)
15.4.1 Context and Goals
295(1)
15.4.2 Design of a Student-Centered Dashboard
296(3)
15.4.3 Usability of the Dashboard
299(1)
15.4.4 Discussion
300(1)
15.5 Conclusion
301(4)
References
302(3)
16 Leadership and Maturity: How Do They Affect Learning Analytics Adoption in Latin America?
305(22)
Isabel Hilliger
Mar Perez-Sanagustin
Ronald Perez-Alvarez
Valeria Henriquez
Julio Guerra
Miguel Angel Zuniga-Prieto
Margarita Ortiz-Rojas
Yi-Shan Tsai
Dragan Gasevic
Pedro J. Munoz-Merino
Tom Broos
Tinne De Laet
16.1 Introduction
305(2)
16.2 Methods
307(4)
16.2.1 Research Design
307(1)
16.2.2 Research Context
308(1)
16.2.3 Data Collection
309(1)
16.2.4 Data Analysis
310(1)
16.3 Case Descriptions
311(10)
16.3.1 Adoption of NoteMyProgress in PUC-Chile
311(1)
16.3.2 Adoption of TrAC in UACh
312(3)
16.3.3 Adoption of the Redesigned Academic Counseling System in ESPOL
315(3)
16.3.4 Adoption of Dashboards in UCuenca
318(3)
16.4 Findings of Cross-Case Analysis
321(2)
16.4.1 Leadership
321(1)
16.4.2 Organizational Maturity
322(1)
16.5 Lessons Learned and Conclusion
323(4)
References
324(3)
17 Adoption of Bring-Your-Own-Device Examinations and Data Analytics
327(22)
Robyn Fitzharris
Simon Kent
17.1 Introduction
327(2)
17.2 The Evolution of Digital Examinations
329(1)
17.3 BYOD Examination Implementation Case Study
330(8)
17.3.1 Infrastructure
331(1)
17.3.2 Human Factors
332(6)
17.4 Bring-Your-Own-Device Examinations Data Analysis Case Study
338(7)
17.4.1 Methodology
339(1)
17.4.2 Results and Discussion
339(4)
17.4.3 Areas for Consideration
343(2)
17.5 Conclusions and the Future of Exam Analytics
345(4)
References
347(2)
18 Experiential Learning in Labs and Multimodal Learning Analytics
349(26)
Anke Pfeiffer
Vlatko Lukarov
Giovanni Romagnoli
Dieter Uckelmann
Ulrik Schroeder
18.1 Introduction
349(1)
18.2 Theoretical Background
350(6)
18.2.1 Lab-Based Learning
351(1)
18.2.2 Experiential Learning in Laboratory-Based Learning Scenarios
352(2)
18.2.3 Multimodal Learning Analytics
354(2)
18.3 Learning Scenario Descriptions and Their Connection to Experiential Learning
356(13)
18.3.1 RFID Measuring Cabinet at the Hochschule fur Technik Stuttgart (HFT Stuttgart)
356(2)
18.3.2 RFID Lab at University of Parma: Experimental Construction of RSSI Curves
358(4)
18.3.3 Connecting Experiential Learning to Lab Learning Scenarios
362(1)
18.3.4 Enhancing Lab Learning Activities with Learning Analytics
363(3)
18.3.5 Technical Infrastructure for Lab-Based Learning and MLA
366(3)
18.4 Discussion and Conclusion
369(6)
References
371(4)
19 Web Analytics as Extension for a Learning Analytics Dashboard of a Massive Open Online Platform
375(16)
Philipp Leitner
Karin Maier
Martin Ebner
19.1 Introduction
375(2)
19.2 Related Work
377(1)
19.3 Concept of the LA Cockpit
378(4)
19.3.1 Activity Measurement
379(1)
19.3.2 Web Analytics
380(2)
19.3.3 Metrics and Visualization
382(1)
19.4 Implementation
382(2)
19.4.1 Device Statistics
382(1)
19.4.2 Activity Calendar
383(1)
19.4.3 Heatmap
384(1)
19.5 Discussion
384(3)
19.5.1 First Evaluation Results
385(1)
19.5.2 Limitations
386(1)
19.6 Conclusion
387(4)
References
389(2)
20 A Dimensionality Reduction Method for Time Series Analysis of Student Behavior to Predict Dropout in Massive Open Online Courses
391(16)
Eric G. Poitras
Reza Feyzi Behnagh
Francois Bouchet
20.1 Introduction
391(4)
20.1.1 Research on Student Attrition Prediction in MOOCS
392(2)
20.1.2 Clickstream Data for Prediction of Student Attrition
394(1)
20.2 Related Works
395(1)
20.3 Experiment
396(3)
20.4 Results
399(1)
20.5 Discussion
400(3)
20.6 Conclusions and Implications
403(4)
References
405(2)
21 Evidence-Based Learning Design Through Learning Analytics
407(18)
Esin Caglayan
O. Osman Demirbas
Ali Burak Ozkaya
Mehmet Sahin
21.1 Introduction
407(3)
21.1.1 Learning Design and Learning Analytics
408(1)
21.1.2 Course Design Archetypes
409(1)
21.2 Methodology
410(2)
21.3 Findings
412(8)
21.3.1 Distribution of Archetypes at the Local Institution
413(1)
21.3.2 Comparison Between the Analysis of the Original Data and Local Data
413(4)
21.3.3 Consistency Between Archetypes Extracted from Analytics and Instructors' Predictions
417(3)
21.4 Discussion
420(5)
References
422(3)
Index 425
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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