Foreword	6
	What Will They Think of Next?	6
	Preface	8
	Contents	14
	About the Editors	17
	Part IMobile Learning Design	19
	1 Framework for Designing Mobile Learning Environments	20
	Abstract	20
	1.1 Introduction	20
	1.2 RASE Learning Design	21
	1.2.1 Resources	24
	1.2.2 Activity	25
	1.2.3 Evaluation	26
	1.2.4 Support	26
	1.3 Affordances of Mobile Learning Technologies and the RASE Learning Design	27
	1.4 A Case for an App that Integrates Multiple Affordances of Mobile Technology and Supports the RASE Learning Design	32
	1.5 Conclusion	38
	References	39
	2 Conceptualising Authentic Mobile Learning	43
	Abstract	43
	2.1 Introduction	43
	2.2 Why Is Authentic Learning Important?	45
	2.3 Defining Authenticity	46
	2.4 Authentic Learning and Mobile Technologies	46
	2.5 Unpacking Authentic Learning	47
	2.5.1 Participatory Contexts	47
	2.5.2 Simulated Contexts	48
	2.5.3 Hybrid Contexts	49
	2.6 Is Authentic Mobile Learning Predefined or Emergent?	49
	2.6.1 Personal Commitment of Learners	51
	2.7 Discussion and Implications	52
	2.7.1 How Does the Model Work?	53
	2.7.2 Returning to Research Questions	54
	2.8 Conclusion	55
	References	56
	3 Mobile Social Media: Redefining Professional Development and Collaborative Scholarship	59
	Abstract	59
	3.1 Introduction	59
	3.1.1 Why Mobile Social Media?	60
	3.1.2 Communities of Practice and Social Scholarship	61
	3.1.3 An MSM Framework for New Pedagogies	62
	3.2 Methodology	64
	3.2.1 Method	64
	3.2.2 Establishing the COPs	66
	3.2.3 MSM Tools	66
	3.2.4 Social Scholarship	68
	3.2.5 Scholarship of Design	68
	3.2.6 Scholarship of Integration	69
	3.2.7 Scholarship of Teaching and Learning	69
	3.2.8 Scholarship of Application	69
	3.2.9 Establishing a Technology Infrastructure	69
	3.3 Discussion	70
	3.3.1 Journalism and Law	71
	3.3.2 Communication Studies	72
	3.3.3 Designing an Appropriate Technology Support Infrastructure	73
	3.3.4 Future Research	74
	3.4 Conclusions	74
	References	75
	4 Interactivity and Mobile Technologies: An Activity Theory Perspective	78
	Abstract	78
	4.1 Background	78
	4.2 Theoretical Framework	79
	4.3 Review of Literature	81
	4.3.1 Pedagogy	81
	4.3.2 Professional Learning	82
	4.3.3 Digital Resources	83
	4.4 Methods	84
	4.5 Findings/Discussion	85
	4.5.1 Teacher-Centred Interactivity	87
	4.5.2 Learner-Centred Interactivity	88
	4.5.3 Blended Interactivity	88
	4.5.4 Juxtaposition of Teacher- and Learner-Centred Interactivity	89
	4.6 Conclusion	91
	References	93
	5 Educational Apps Ontology	98
	Abstract	98
	5.1 Introduction	98
	5.2 What Is an App/What Is an Educational App?	99
	5.3 Educational App Typologies	100
	5.4 Educational Apps from Different Perspectives	102
	5.4.1 The Learner's View	103
	5.4.2 The Teacher's View	104
	5.4.3 The Developer's View	105
	5.4.4 The Distributor's View	106
	5.5 Why so Many Educational Apps Are Games?	107
	5.6 Conclusion	108
	References	109
	6 Augmented Learning with Augmented Reality	112
	Abstract	112
	6.1 Introduction	112
	6.2 Defining Augmented Reality	113
	6.3 Evolution of Educational Augmented Realty	113
	6.4 Augmented Reality in Education	114
	6.4.1 Educational Affordances	115
	6.4.2 Learning with Others	116
	6.4.3 Tangible Interface Metaphor	116
	6.4.4 Transition	116
	6.5 Augmented Reality Programs for Education	117
	6.5.1 AR Educational Gaming	117
	6.5.2 Augmented Realty Books	117
	6.5.3 Book Applications	118
	6.6 Teaching with Augmented Reality	119
	6.6.1 Student-Centered Learning	119
	6.6.2 Designing for Learning	120
	6.7 The Future of Augmented Reality in Education	120
	6.8 Recommendations	121
	6.9 Conclusion	121
	References	122
	Part IIMobile Learning Adoption and StudentPerception	124
	7 Mobile Technology and Interactive Lectures: The Key Adoption Factors	125
	Abstract	125
	7.1 Introduction	125
	7.2 Background Study and Hypotheses Development	127
	7.2.1 Technology Acceptance Model (TAM, TAM2, TAM3)	127
	7.2.2 Intrinsic Motivator Theory	128
	7.2.3 Delone and McLean Information System Success Model	129
	7.2.4 Uncertainty Avoidance	130
	7.3 Research Methods	130
	7.4 Results	131
	7.4.1 Principal Component Analysis	131
	7.4.2 Pearson Product-Moment Correlation	132
	7.4.3 Standard Multiple Regression	134
	7.5 Discussions	135
	7.6 Conclusion and Future Works	136
	Acknowledgments	137
	References	137
	8 University Student Conceptions of M-learning in Bangladesh	141
	Abstract	141
	8.1 Introduction	141
	8.2 M-Learning and Related Literature to the Research Problem	142
	8.3 Methodology	144
	8.3.1 Sample	144
	8.3.2 Data Collection	145
	8.3.3 Data Analysis	145
	8.4 Results	145
	8.4.1 Categories of Description	146
	8.4.1.1 Category A: Storing Learning Materials	146
	8.4.1.2 Category B: Accessing Information and Knowledge	146
	8.4.1.3 Category C: Effective Learning Tool	147
	8.4.1.4 Category D: Effective Tool for Collaboration	148
	8.5 Discussion and Conclusions	148
	Acknowledgments	150
	References	150
	9 Mobile Learning, Student Concerns and Attitudes	153
	Abstract	153
	9.1 Introduction	153
	9.2 State of the Art	154
	9.3 The Present Study	158
	9.3.1 Current Situation in Serbia in General	158
	9.3.2 Specific Situation at the Department of Mathematics and Informatics, Novi Sad, Serbia	159
	9.4 Methodology	159
	9.4.1 Instrument	159
	9.5 Conclusion	164
	References	166
	10 Access Moodle Using Mobile Phones: Student Usage and Perceptions	168
	Abstract	168
	10.1 Introduction	169
	10.2 Related Work	169
	10.2.1 Use of LMS	169
	10.2.2 Mobile Learning	170
	10.3 Methodology	171
	10.3.1 The LMS and the Courses	171
	10.3.2 Participants and Procedure	172
	10.3.2.1 The Survey	172
	10.3.2.2 The Interview	172
	10.3.3 Instruments	174
	10.4 Results	175
	10.4.1 Questionnaire Responses	175
	10.4.2 Themes from Interviews	178
	10.5 Discussion	179
	10.6 Conclusion and Future Work	181
	Acknowledgments	182
	Appendix 1: Questionnaire	182
	References	182
	Part IIIMobile Learning Analytics	185
	11 An Effective Cloud-Based Simulator Facilitating Learning Analytics on Mobile Devices	186
	Abstract	186
	11.1 Introduction	187
	11.2 Previous Work	188
	11.3 System Design of Our Cloud-Based COMPAD+ Simulator	189
	11.3.1 The Adaptive User Interface of the COMPAD+ Simulator	191
	11.3.2 The Improved Learning Path Optimization Algorithm for Learning Analytics	192
	11.4 Our Empirical Evaluation	193
	11.4.1 The Adaptive User Interface of the COMPAD+ Simulator	194
	11.4.2 The Improved Learning Path Optimization Algorithm for Learning Analytics	196
	11.5 Concluding Remarks	198
	Acknowledgments	198
	References	198
	12 A New Wave of Innovation Using Mobile Learning Analytics for Flipped Classroom	200
	Abstract	200
	12.1 Introduction	200
	12.2 Conceptual Framework	201
	12.2.1 Formative Assessment and Feedback	202
	12.2.2 Mobile Learning Analytics	203
	12.2.3 Flipped Learning Approach	205
	12.2.4 Unified Conceptual Model for Active Learning	206
	12.3 Research Design and Methodology	207
	12.3.1 Research Objectives	207
	12.3.2 Context of Study	209
	12.3.3 Learning Management System	209
	12.3.4 Participants	210
	12.3.5 Instruments and Procedure	210
	12.3.6 Data Analysis	211
	12.4 Results and Analysis	212
	12.4.1 Access to Course Notes and Learning Activities	212
	12.4.2 Access to Video Lectures	214
	12.4.3 Access Through Mobile Platforms	215
	12.4.4 Students' Perceptions on Mobile Learning Analytics	216
	12.4.5 Discussions and Implications	221
	12.5 Conclusions and Limitations	223
	Acknowledgments	224
	References	224
	Part IVMobile Learning Across Curriculum	230
	13 Mobile Learning in K-12 Education: Personal Meets Systemic	231
	Abstract	231
	13.1 Introduction	231
	13.2 Defining Mobile Learning	232
	13.2.1 Mobile Learning in Schools	233
	13.2.2 The Role of the Teacher	234
	13.3 A Short History of Mobile Learning in a School	235
	13.4 Evaluating Mobile Learning	237
	13.5 School Case Reviews	238
	13.5.1 Study One: Grade 11 Parent Conferencing	238
	13.5.2 Study Two: Grade 8 EBook and Process Journal	240
	13.5.3 Study Three: Grade 6 Digital Literacy	241
	13.5.4 Study Four: Grade 1 iPad as iFolio	243
	13.6 Discussion	244
	13.7 Conclusion	245
	References	246
	14 Overcoming Teachers' Concerns---Where Are We in the Harnessing of Mobile Technology in K-12 Classrooms in Hong Kong?	249
	Abstract	249
	14.1 Introduction	249
	14.2 Stages of Concern (SoC)	250
	14.3 Research Question	251
	14.4 Participants	252
	14.5 Research Design	252
	14.6 Results and Data Analysis	254
	14.7 Preliminary Discussions and Implications for Practice	255
	14.8 Conclusions	257
	References	257
	15 Exploring the Suitability of the Book Creator for iPad App for Early Childhood Education	259
	Abstract	259
	15.1 Introduction	259
	15.2 Literature Review	260
	15.3 Creating to Learn	261
	15.4 Methods	261
	15.4.1 Early Childhood Setting	262
	15.4.2 Ethical Considerations	262
	15.4.3 Selection and Evaluation of App	263
	15.4.4 Procedure	263
	15.4.5 Data Collection Procedure and Analysis	264
	15.5 Findings and Discussion	266
	15.5.1 Post-study Evaluation of the Book Creator for ECE	267
	15.5.2 Suitability of the Book Creator for ECE	267
	15.5.3 Children's Artefacts	274
	15.6 Conclusion	278
	References	279
	16 Text Messaging for Out-of-Class Communication: Impact on Immediacy and Affective Learning	281
	Abstract	281
	16.1 Introduction	281
	16.2 Instructor Immediacy	282
	16.3 Text Messaging in Education	283
	16.4 Methodology	284
	16.4.1 Text Messaging Service	284
	16.4.2 Categories of Text Messages	285
	16.5 Data Collection	286
	16.6 Results and Discussion	286
	16.6.1 Levels of Participation	287
	16.6.2 Quantitative Results	287
	16.7 Qualitative Results	288
	16.8 Reflections and Conclusions	289
	16.8.1 Concerns with Instructor-Student Text Messaging	289
	16.8.2 Guidelines for Instructor-Student Text Messaging	291
	16.8.3 Conclusion	291
	References	292
	17 Implementing a Mobile App as a Personal Learning Environment for Workplace Learners	295
	Abstract	295
	17.1 Introduction	295
	17.2 Theoretical Framework	296
	17.2.1 Personal Learning Environments (PLE)	297
	17.2.2 A Personal Informal Learning Framework in the Context of Mobile Web 2.0	297
	17.3 Design and Development of MobLearn@Work	299
	17.3.1 An Agile Design Approach	300
	17.3.2 Functions of MobLearn@Work	301
	17.3.3 UI Improvements of MobLearn@Work	303
	17.4 A Novel Method for Quantifying the Data	305
	17.4.1 The Log System	305
	17.4.2 Measure of Actual Learning Time via MobLearn@Work	307
	17.5 Recommendations for Future Study	308
	17.6 Conclusion	309
	References	309
	Part VMobile Learning in Subject Domains	311
	18 The Theory of Context-Aware Ubiquitous Learning and the Affordances of This Approach for Geometry Learners	312
	Abstract	312
	18.1 Introduction	312
	18.2 Mobile Learning	313
	18.3 Context-Aware Ubiquitous Learning	315
	18.3.1 Sensory	318
	18.3.2 Ambient	319
	18.4 Geometry: Measure of the Earth	320
	18.5 The Future	320
	18.6 Conclusion	321
	19 Three Phases of Mobile Learning State of the Art and Case of Mobile Help Seeking Tool for the Health Care Sector	324
	Abstract	324
	19.1 Introduction	324
	19.2 Three Phases of Mobile Learning State of the Art	325
	19.3 Design Research in the Learning Layers Project	329
	19.3.1 Design Research	329
	19.3.2 Vygotsky	329
	19.3.3 Learning Layers	330
	19.4 Help Seeking Tool	331
	19.4.1 Overview of WP2 `Networked Scaffolding---Interacting with People'	331
	19.4.2 Early Co-design of the Help Seeking Tool	333
	19.4.3 Help Seeking: Follow-up Empirical Co-design Study	334
	19.5 Innovation in Context: Help Seeking Using the Social Semantic Server	336
	19.5.1 Social Semantic Server	337
	19.5.2 Layers Social Semantic Server and Help Seeking Tool in Healthcare Sector	337
	19.6 Conclusions	340
	Acknowledgments	340
	References	341
	20 Mobile-Assisted Language Learning in China's College English Education: The Reality and Research	343
	Abstract	343
	20.1 Introduction	343
	20.2 Definitions of Key Terms	345
	20.3 College English Education in China	346
	20.3.1 Historical Context	346
	20.3.2 Problems	347
	20.4 Mobile Technologies	349
	20.5 MALL Research in China's Higher Education	350
	20.6 Recommendation	353
	20.7 Conclusion	354
	Acknowledgments	355
	References	355
	21 Enacting App-Based Learning Activities with Viewing and Representing Skills in Preschool Mathematics Lessons	358
	Abstract	358
	21.1 Introduction	358
	21.2 Literature Review	361
	21.2.1 Designing a Creative Reasoning and Collaborative Learning Environment	361
	21.2.2 Viewing and Representing Skills with Digital Texts	361
	21.3 Research Design	363
	21.4 Observation of Participants' Enacting the App with Viewing and Representing Skills	367
	21.4.1 Participant One: Peter	367
	21.4.2 Participant Two: Mary	369
	21.4.3 Participant Three: Ben	370
	21.4.4 Participant Four: Nicole	371
	21.5 Discussions	371
	21.6 Conclusions	376
	References	376
	22 Effects of Prior Knowledge on Mathematics Different Order Thinking Skills in Mobile Multimedia Environments	380
	Abstract	380
	22.1 Introduction	380
	22.2 Literature Review	381
	22.2.1 Multimedia Learning and Prior Knowledge	381
	22.2.2 Instructional Design in Mathematics	382
	22.2.3 The Present Study	383
	22.3 Method	384
	22.3.1 Participants	384
	22.3.2 Design	384
	22.3.3 Materials	385
	22.3.4 Procedure	385
	22.4 Result	386
	22.4.1 Graphical Property and Concept Association	386
	22.4.2 Evaluation of Solution and Written Explanation	387
	22.4.3 Procedural Knowledge	388
	22.5 Discussions	388
	22.5.1 Prior Knowledge and Low Order Thinking Skill in Conceptual Knowledge	388
	22.5.2 Design Approach and High Order Thinking Skill in Conceptual Knowledge	389
	22.5.3 Design Approach and Procedural Skill	389
	22.6 Conclusions	390
	References	390
	23 An Investigation of the Effects of Individual Differences on Mobile-Based Augmented Reality English Vocabulary Learning	394
	Abstract	394
	23.1 Introduction	395
	23.2 Literature Review	396
	23.2.1 English Vocabulary Learning	396
	23.2.2 Situated Learning	398
	23.2.3 The Present Study	400
	23.3 Methods	400
	23.3.1 Participants	401
	23.3.2 Mobile-Based AR Simulations Learning System Overview	401
	23.3.3 Procedure	402
	23.3.4 Variables and Instruments	404
	23.3.5 Data Analysis	406
	23.4 Results	406
	23.4.1 Research Question 1	406
	23.4.2 Research Question 2	407
	23.4.3 Research Question 3	408
	23.4.4 Research Question 4	408
	23.4.5 Findings from the Interviews	409
	23.4.5.1 Fun	409
	23.4.5.2 Effectiveness	409
	23.4.5.3 Satisfaction	410
	23.4.5.4 Confidence	410
	23.5 Conclusion	410
	23.5.1 Summary and Discussion of Major Findings	410
	23.5.2 Pedagogical Implications	412
	23.5.3 Limitations of the Study and Suggestions for Future Research	413
	Acknowledgments	413
	Appendix A: Motivation Questionnaire	414
	References	415
	Part VIFuture Developments	418
	24 Future Directions in Mobile Learning	419
	Abstract	419
	24.1 Introduction	419
	24.2 Present and Future Mobile Learning	420
	24.3 The Technological Context	421
	24.4 The Educational Context	425
	24.4.1 Contextualisation of Learning (E.G., Through Augmented Reality)	426
	24.4.2 Personalisation of Learning (E.G., Through Big Data and Learning Analytics)	427
	24.4.3 Diversification of Learning (E.G., Through MOOCs)	428
	24.4.4 Student Support (E.G., Through Virtual Assistants)	429
	24.4.5 Student Engagement (E.G., Through Gaming and Gamification)	430
	24.4.6 Student Creativity (E.G., Through Makerspaces)	430
	24.4.7 Wider Collaboration (E.G., Through Digital Networking)	431
	24.5 Conclusion	432
	Acknowledgments	432
	References	433