Demetrios Sampson,Dirk Ifenthaler,J. Michael Spector,Pedro Isaías (eds.) - Digital Technologies_ Sustainable Innovations for Improving Teaching and Learning-Springer International Publishing (201

292 Y. Atif et al.

In order to measure the amount of power that is being consumed from home
appliances (i.e., energy disaggregation), nonintrusive IoT enablement via smart
plugs can be utilized (see Fig. 16.1). The collected data is passed through power-­
load monitoring services to a cloud platform (similar to Fig.  16.3). In this way,
energy disaggregation can measure appliances with high energy consumption and
push personalized learning scenarios. Such scenarios can lead to better sustainabil-
ity awareness or even offer reward incentives, through a client mobile or web-based
app. A simulation tool evaluates the operations of a smart home under various hypo-
thetical “what if” scenarios to generate insights that can support customers to build
develop their “smart-grid” competences for efficient energy usage.

In Sweden, energy providers offer retail consumers electricity contracts based on
real-time pricing following Demand-Response schemes which is a form of supply-­
demand price adjustments in energy markets [40]. As the current infrastructures do
not support storage of electrical energy in large quantities, the balance between
supply and demand needs to be dynamically and continuously monitored and
adjusted. Real-time pricing has emerged as a flexible scheme over a rigid time-of-­
day use scheme which projects peak-demand trends. However, real-time tariffs have
to be accompanied by appropriate home automation technologies on the one hand
and the necessary digital smart competences in order for consumers to understand
and modify their daily consumption behavior.

In this context, we have conducted a proof-of-concept case study, in which we
used smart plugs to augment home appliances with IoT capabilities. These enhance-
ments can be used to continuously inform home inhabitants about the cost of a
kiloWatt hour (kWh) at the time of use. Furthermore, the home is also equipped
with a smart meter that collects these data to monitor and visualize power usage at
real time. Next, we illustrate the competence-driven CPL model discussed through-
out this chapter in the context of a smart-home energy management. This case study
demonstrates the envisaged transition from conventional centralized energy man-
agement infrastructure towards autonomous cyber-physical energy systems. The
advocated CPL scenarios discussed next and further illustrated in Fig. 16.7 contrib-
ute to the progressive development of digital smart-citizenship competences.

As aforementioned, a CPL learning path comprises a structured set of CPL envi-
ronments. In the case study we describe, the initial instance of this environment
consists of monitoring energy consumption data from a washing machine. The
objective of this situated-learning process is to improve the awareness of learners
for energy-data value, as well as to build their capacity to self-assess their energy
consumption patterns. Below are presented indicative models in this case study,
each addressing different competences.

16.4.1  Competence 1: Energy Use Data

16.4.1.1  Model 1: Washing Machine

This model includes the washing machine as a CPL environment and targets to
build Competence 1 through the following scenarios.

16  Digital Smart Citizenship Competence Development with a Cyber-Physical… 293

Competence

Cyber-physical Cyber-physical
environments control social influence

Cyber-physical Cyber-physical Interaction with
social systems communities CPS

Scenario 4 Scenario 5

Cyber-physical Visual Analytics Intimacy with Intervening role
data Analytics Scenario 1 CPS in CPS

Scenario 2 Scenario 3 Learning pattern

Fig. 16.7  CPL path buildup in the proposed case study

Scenario 1—Visualization of current power data. In this scenario, data collected
from the washing machine as well as the energy retailer are continuously shown to
the learner, regarding energy consumption and associated costs. These data are
benchmarked against a “model consumption schema” (which can be derived from
other households) so as to generate energy savings. In this way, statistically relevant
data from typical communities in terms of washing machine power usage can be
collected and visualized to the learner. Based on this data, the learners are intro-
duced to the use and influence of visual analytics.

Scenario 2—“What-if” appliance is started at an alternative time. In this scenario,
learners are introduced to the use of energy data for selecting optimal time sched-
ules to use their washing machine. In particular, the scenario uses a graphical view
where learners can define their custom time schedules for using their w­ ashing
machine, supported by a wizard. The wizard provides insights on how to optimally
define the schedule to exploit off-peak hours (when the cost of energy is lower) also
supported by rewarding incentives. This scenario develops active digital footprints
whereby user data is released deliberately through an enhanced intimacy between
users and physical assets.

294 Y. Atif et al.

Scenario 3—“What-if” appliance uses alternative programs. This scenario exploits
predictive analytics so as to train learner in more optimal use of their washing
machine. In particular, predictive analytics are used with various wash/rinse tem-
perature settings to heat the water, so as to engage learners in energy-saving alterna-
tives by changing the temperature setting. To visualize the impact of different
temperature settings, the scenario visualizes the energy consumption and associated
cost for each setting, Furthermore, a target learner behavior (i.e., selection of appro-
priate settings) is proposed to incite the learners to experience a new energy-saving
setting for the washing activity.

16.4.2  C ompetence 2: Energy Use Footprint

The learner is provided with information regarding their global home energy con-
sumption. The smart meter data source is added to the CPL environment, which
includes instant power data. This data could be dispatched periodically or upon
request. The smart meter provides its data to the cloud-based model library
through a meter service module where the meter records its measurements in the
form of index (one per tariff-period type). It also exposes a service interface for
delivering information (index, tariff period, etc.) to devices in CPL community
(currently limited to washing machine and consumer members). This information
may be directly received, treated, stored, and displayed on a dedicated display or
user’s mobile app. It may also be collected by a cloud-hosted Web application
aggregating data from community members to contrast them against individual
energy-footprint contribution.

16.4.2.1  Model 2: Smart Meter and Washing Machine

This model enhances the CPL environment with a smart meter in addition to the
washing machine and addresses Competence 2 through the following scenarios.

Scenario 4—Display global and per appliance energy consumption and costs,
either realized and/or forecasted. This scenario aims to inform learners on the cur-
rent consumption for their home and also for information regarding individual
appliances. The information is provided by a dedicated service, which collects the
data from the smart meter and also from the smart plug of the washing machine. At
the same time, energy cost data are retrieved from the energy retailer for different
times during the day. The scenario exploits all these data in c­ ombination, in order to
educate learners on a more efficient and rational use of energy. The targeted behav-
ioral change in this scenario is the reduction of global energy by leveraging smart
appliance energy footprints. The learners are trained to select the parameters of the
appliances (in this case washing machine cycles) by providing feedback on fore-
casted costs and energy consumption. This scenario introduces users into CPL com-
munities, which are poised to shape future smart cities.

16  Digital Smart Citizenship Competence Development with a Cyber-Physical… 295

Scenario 5: Devices monitoring: This scenario expands learners’ capacity to man-
age connected devices in their CPL community so as to monitor their status and
functionalities. The smart-citizen learner can receive remotely on their mobile or a
Web app the status of the devices; in this case, their washing machine, to let them
perform monitoring operations. This scenario is applicable for those devices that
have remotely accessible operations, such as the current phase of the device (e.g.,
for a washing machine Heating, Rinse, Spin), the selected program, the selected
options (temperature, duration, spin, etc.), special functions (prewash, extra-rinse,
etc.), and the remaining time to end the current cycle. Furthermore, on–off options
can be provided via smart plugs on devices which do not provide elaborated remote-
monitoring services.

16.4.3  Competence 3: Energy Use Control

This competence empowers self-discipline in maintaining a threshold of energy use
across a range of appliances.

16.4.3.1  M odel 3: Smart Meter, Washing Machine, and Dryer

This model exploits three physical elements as a CPL environment: a washing
machine, a dryer, and a smart meter. This model targets Competence 3 through the
following scenario.

Scenario 6—“What-if” total power exceeds a preset threshold. In this scenario, a
learner selects predefined thresholds of global energy use in their home. Each of
the smart appliance in the house uses statistical analyses on past consumption
data, in order to estimate the maximum energy that would be consumed during its
next cycle. Then, it determines whether the predicted consumption is likely to
exceed learner-defined user threshold. This threshold trains the learners on the
potential gains from contracted subscribed power. In case of mismatches between
the predicted and warranted consumption levels, a warning is emitted and is dis-
played on the appliance or other interface(s) such as a mobile app. The appliance
works in tandem with other appliances in the house to define collaborative set-
tings that will bring energy use below the predefined threshold for the house. In
this case, dryer and washing machine negotiate their programs to calibrate jointly
their energy use. For example, the heating temperature is set in both devices
accordingly to optimize the washing and drying operations while still complying
with the overall power-use threshold. The learner is presented with options to
combine both programs and select adequate options. This scenario allows learner-
citizens to influence CPL communities adopting a desired pattern of effective
energy use that drives environmental sustainability and builds values of resource
efficiency for citizens.

296 Y. Atif et al.

16.4.4  Experimental Study Methodology

A preliminary experiment to identify learners’ views to employ CPL environments
for learning and enhance their level of digital smart citizenship competences was
conducted. The above case study with related scenarios was shown as an illustration
to participants of the survey. The aim of the survey was whether CPL environments
have a positive effect on users’ learning towards digital smart citizenship build-up.

The study was conducted in a residential area of a medium size city in Sweden.
The participants were 65 Computer Science students who are aware about contem-
porary smart-city developments. The research instrument was a questionnaire com-
prising four-point Likert scales, with 1 being the negative end of the scale (strongly
disagree) and 4 being the positive end of the scale (strongly agree). From the 65
initially collected questionnaires, 55 were valid and were included in this prelimi-
nary study. All questionnaires were anonymized.

The results of the questionnaire analysis are displayed in Fig. 16.8. As Fig. 16.8
depicts, the stated hypothesis is supported, with a positive effect on users’ attitude
towards embracing CPL learning environments to foster digital smart competences.
In particular, the results from the two last questions of this preliminary survey show
the benefits of data analytics to empower future digital smart citizens, and they also
reveal an existing awareness on the prominent role that IoT are poised to play in
alleviating challenges towards smart-city functions’ adoption.

16.5  C onclusion

Smart cities raise the expectations for citizens’ engagement and increase their digi-
tal footprint. As city leaders and policy makers brainstorm, envision, and plan smart
city developments, one needs to keep in mind that the success of smart cities relies
on citizens’ engagement into these spaces enacted by an assemblage of smart tech-
nologies and data analytics. We presented a computational representation of these
spaces using advances in Internet of Things (IoT) that shape cyber-physical learning
environments. We also advocated progressive learning processes within these envi-
ronments, following a model-based learning scheme that integrates connected phys-
ical objects as sources of learning. The proposed algorithm asserts digital
competencies within specific smart city application domains. Multidimensional
data that is collected from connected devices is contrasted against a standard scale
of digital smart citizenship competences, where a spider chart reflects citizen’s indi-
vidual attainment, which can be aggregated to provide city authorities or
mentorship services a dashboard view to infer a global index of digital smart citi-
zenship. A case study illustrating the proposed cyber-physical learning approach
was discussed and analyzed along contemporary smart-grid developments, where
smart homes augmented or fitted with IoT technologies empower inhabitants to
feedback on their energy usage and behavioral changes that expand their digital
smart citizenship competences.

16  Digital Smart Citizenship Competence Development with a Cyber-Physical… 297

Fig. 16.8  Survey results on CPL environments support for digital smart citizenship.

Future work is envisaged to span two core dimensions. First, it will aim at dem-
onstrating the proposed cyber-physical learning approach within a prototype imple-
mentation of a connected home. The aim of such work will be to measure and
showcase incremental advancements in smart digital citizenship competences.
Second, future work could aim at defining and deploying a more robust evaluation
protocol. This protocol should provide evidence on the capacity of the proposed
approach to cultivate the spectrum of Smart Citizenship competences and provide
an effective way to prepare the citizens in the cities of the future.

Acknowledgments  The first author’s contribution in this work has been partially funded by
Västra Götaland Region, as part of the research project on smart grid Kraftsamling Smarta Nät
2015–2016 (dnr MN 39-2015), and partially supported by SP Sveriges Tekniska Forskningsinstitut
AB as well as ELIQ AB (energy management company). The second and third authors’ contribu-
tion in this work has been partially funded by the Greek General Secretariat for Research and
Technology, under the Matching Funds 2014–2016 for the EU project “Inspiring Science: Large
Scale Experimentation Scenarios to Mainstream eLearning in Science, Mathematics and
Technology in Primary and Secondary Schools” (Project Number: 325123). Finally, the third
author’s contribution in this work is part of Curtin’s contribution to the “STORIES—Stories of
Tomorrow: Students Visions on the Future of Space Exploration” under the European Commission’s
Horizon 2020 Program, H2020-ICT-22-2016–2017 “Information and Communication
Technologies: Technologies for Learning and Skills” (Project Number: 731872). This document
reflects the views only of the authors and it does not represent the opinion of the Sveriges Tekniska
Forskningsinstitut AB, ELIQ AB, Greek General Research Secretariat, the European Commission,
or Curtin University. The Sveriges Tekniska Forskningsinstitut AB, ELIQ AB, Greek General
Research Secretariat, the European Commission, and Curtin University cannot be held responsible
for any use that might be made of its content.

298 Y. Atif et al.

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Index

A data collection tools
Academic motivation scale, 178 group interview, 137
Academic retention student-designed questionnaire and
response schedule, 136
academic motivation scale, 178 video footage, 136
job planning support, 180 voice recordings, 136
online laboratory, 179, 180
participants, 180 educational technology/ICT, 132
self-concept and self-determined environment, 132
experience design, 133
motivation, 183 methodology, 134–136
self-description questionnaire III, 179 online software applications, 133
self-regulation, 178, 181, 183 scientific open inquiry, 132
self-regulation questionnaire, 179 self-motivational and self-efficacy, 143
socioeconomic status, 181 self-regulation (see Self-regulated learning)
STPI, 179 student conversations and comments, 141
variables, 181, 182, 184 student perceptions, 132
Academic self-concept, 175 student self-regulation, 132
Academic writing, 112 student voices, 133
Adaptive training teacher-centered issues, 132
BCI (see Brain computer interface (BCI)) theoretical framework, 133–134
learner’s cognitive load, 162, 163 3D–human body system model, 133
performance measures, 162 translanguaging, 141, 144, 145
Analysis of Moment Structure (AMOS), 102 vocabulary training website score
Attending classes, 47
Australasian Journal of Educational comparison, 143, 144
Biotechnology, 90
Technology, 31 Blended-learning classroom, 28
Average Variance Extract (AVE), 102 Blended-learning design, 28
Blooms’ cognitive taxonomy, 96
B Brain computer interface (BCI)
Baden-Wuerttemberg Cooperative State
adaptive training, 170
University Mannheim (DHBW), 49 EEG devices, 163, 169, 170
Behavioral pattern, 209 Helicopter Control Training Game, 166, 168
Bilingual online learning Mindwave attention level, 168, 170
task difficulty, 164
classification, 141 time interval vs. mental state triggered, 165
CLIL, 132, 133

© Springer International Publishing AG 2018 301
D. Sampson et al. (eds.), Digital Technologies: Sustainable Innovations for
Improving Teaching and Learning, https://doi.org/10.1007/978-3-319-73417-0

302 Index

Brain computer interface (BCI) (cont.) Chicago Public Education Fund, 7
time-based condition, 167 Child-adapted technologies, 88–90
tooling, 166 Child-friendly devices, 84
training task, 165 Children technologies

British Columbia Principals’ & Vice-­ adult professionals, 84
Principals’ Association, 10 child-friendly devices, 84
color inkjet printers, 85
British Journal of Educational Technology, 31 computer professionals, 84
Broad-Based Technology Education Fortran programming, 84
incremental approach, 83
Curriculum (BBTE), 201 pattern in, 85
Building community relationships, 8 pick-and-place device, 91–93
professional, 90–91
C tools and materials, 84
California School Leadership Academy, 6 transition from professional to
Central5—A Central European View on
child-adapted technologies, 88–90
Competencies for School consumer technologies, 87–88
Leaders, 9, 10 creative technologies, 87–88
Chemistry education fantasy (imitative) technologies, 88–90
assessment tools, 62–67 leisure-oriented technologies for adults,
attitudes, 67
cognitive multimedia theory, 73 86–87
computer technology, 58 Children’s pick-and-place device, 91–93
dual coding theory, 73 Classroom and school-based data, 8
educational context, 60, 67 Classroom learning, 104, 107
enrichment, 75 Coaching Others Cluster of Competences, 12
humanities and social sciences, 58 Coding tasks’ characteristics, 210
inscribed tetrahedron, 76 Co-educational school students
learning effectiveness, 72
learning outcomes, 67 human endeavour, 275
macro-level refers, 75 learning in virtual world, 274, 275
methodology, 59–60 nanotechnology concepts, 275
micro-level, 75 students’ interest, 275, 276
particulate and structural, 72 Cognitive domain, 97
pedagogical approaches, 62 Cognitive presence, 117
pedagogical use, 72 Collaboration axis
perceptions, 67 distributed leadership, 251
positive learning outcomes, 74 group reflection, 251
primary and secondary, 58 mutual engagement, 251
research axes, 62 Collaborative creativity
research method, 62–67 challenge, 244
research objectives, 62–67 collaboration, 247
reviewed papers, 72 convergence phase, 248
sample characteristics, 62–67 creative process, 245, 247
skills, 75 divergent phase, 248
symbolic level, 75 exploration phase, 248
technological approach, 61, 62 solution, 245
tetrahedral model, 75 Color inkjet printers, 85
topics in papers reviewed, 60, 61 Comments to previous posts, 112
TPCK, 76 Community bonding, 6
traditional teaching, 58 Community building with families, 11
type of assessment activities, 73 Community leadership, 11
types of representations, 72 Community of Inquiry
visualizations, 75
(CoI) model, 117, 118, 123
Computational thinking (CT)

Index 303

assessment tools, 194, 195 Computer-Assisted Instruction, 58
Bebras tasks, 208 Computer-based games
in classrooms
research, 262
actionable feedback, 231 transformational (see Transformational
algorithms/computational models, 231
science and engineering practices, games)
Consumer technologies, 87–88
231–237 Content and language integrated learning (CLIL)
students’ CT skills, 231
teachers, 231 approach, 133
CompéTICA network, 217 biology classroom, 133, 134
complementary learning, 217 online learning environment, 139–142
components, 226 Content posts, 112
computational practices, 226 Content-textual approach, 112
concept, 224, 226 Council of Chief State School Officers, 11
cultural dimension, 197 Course blog, 113–115, 119–121, 126, 127
data flow (DF) CPL community, 287, 289, 296, 297
pre- and posttesting, 203 Creating a community of equity, 11
visual programming, 205, 206 Creative technologies, 87–88
Vizwik testing, 203, 205 Creativity (C), 292
DBR, 198, 199 Creativity axis
debugging and systematic error detection, 226 convergent processes, 250
defining skills, 196, 197 divergent processes, 250
definitions, 194, 226 Creativity, designing technology-enhanced
educational implications, 237, 238
innovative practices, 217 pedagogy
learning tool design, 217 cultural and symbolic tool, 243
measurement, 198, 200 educational researchers, 243
mental process, 194 sociocultural psychology’s
novice learners, 227
personal component, 197 conceptualization, 243
practices and description, 227 Critical Appraisal Skills Programme (CASP), 32
primary and secondary school students, 223 Critical Thinking (CT), 292
problem-solving approaches, 223, 224 Cultural leadership, 7
process, 226 Culture and continuous push, 8
researchers, 227 Culture and equity leadership, 10
selection criteria, 202 Curriculum and instruction, 11
skill assessment, 211, 212, 214 Curriculum design and planning, 11
skill types and score, 208 Cyber-physical learning system architecture
social dimension, 197
SRL (see Self-regulated learning (SRL)) community, 287–289
stage 1, 201 digital smart citizenship, 291
stage 2, 201, 202 existing devices, 285
stage 3, 202, 203 participates, 286
STEM classroom, 199, 200 progressive learning, 289–293
students’ and teacher’s perceptions, 204, smart citizenship competence-disposition

208, 211, 215 scores, 291
tangible dimension, 197 smart physical objects, 286
techno-instrumental level, 218 smart-object virtualization, 286, 287
training modules, 228
TRE, 195 D
UI, 201 Data-driven decision making, 11
visual programming, 200 Data-driven evidence-based accountability, 4
Computer professionals, 84 Denver Public Schools, 10
Department of Education in New York City, 11
Department of Social

and Educational Policy, 118

304 Index

Design-based research (DBR), 198, 199 developments in open education and
Digital citizenship, 8 technologies, 29
Digital learning technologies, 57
education studies, 28
chemistry education (see Chemistry Google trends, 30, 35
education) implications, 40
in-class part, 41
Digital smart citizenship, 280, 282, 284, 286, K-12 education, 41
289, 291–293, 298, 299 learning styles, 29
lecture-based approach, 28
Digital tools limitations, 39, 41
focus group interview, 137 literature review (see Literature review,
student-designed questionnaire and
response schedule, 136 flipped/inverted classroom
video footage, 136 approaches)
voice recordings, 136 literature reviews, 31
methodology
Distributed leadership model, 5 analysis, 32–34
Dropout, 174, 185, 186 articles collection, 31, 32
Dual system, 47 identified published studies, 33, 34
quality assessment checklist, 32
E type of, 36
EdStudio, 135–139 methods, 40
Education Perfect, 133 research questions, 28
Educational 3D printing, 85, 86, 88, research type, 36
sample distribution, 35
89, 91, 92 student engagement, 39
Educational blogs, 112–115, 118–121, 123, students’ opportunities to learn, 39
subject area, 35, 36
125–127 technology used, 36, 37
Educational excellence, 9 video-based learning platforms, 29
Educational policies Formative and summative assessment methods
and tools, 8
in local, state and national level, 8 Formative assessment
Effective learning and learning definition, 150
LMS, 151
preference, 100–101 Fortran programming, 84
E-learning, 106, 111, 114, 118, 119, 121 Freshmen, 180, 185
Electroencephalography (EEG), 163, 164, Fundamental competences, 9

169, 170 G
Electron microscopes, 84 Gagné’s theory of instruction, 96
e-moderator, 119 Game-based learning
Enabling cluster, 7
e-Readers, 46–54 flow principle, 162
Ethical principles and professional norms, 11
Excellence in professional practice, 8 H
Executive leadership model, 5 Hedonized technologies, 86
External development leadership, 7 Helicopter Control Training Game, 166, 168
Heteroglossic (individual discourse)
F
Face-to-face instruction, 29 language, 134
Fantasy (imitative) technologies, 88–90 Hewlett-Packard’s, 84
Flipped/inverted classroom approaches High-speed camera, 84
Human resource leadership, 7, 10
active learning, 39
benefits, 39
blended-learning design, 28
concepts of, 41
definition, 29–30
description, 41

Index 305

I J
Incremental approach, 83 Jung’s theory of personality, 96
Information behavior, 98, 101, 102, 104, 106, 107
Information Communication Technology K
Kolb Learning Style Inventory (LSI), 98
(ICT) options, 97
Information Communications Technology L
Leadership Standards for Principals and
Learning (ICTL), 98, 99,
101, 102, 107 Vice-Principals in British
Information seeking, 101, 102, 104, 107 Columbia, 10
Information sharing, 101, 102, 104, 107 Leading and managing change dimension, 10
Innovative practices, 217–219 Leading and managing learning and teaching
Institutional Review Board, 101 competence dimension, 9
Instructional design theory, 162 Leading and managing the institution, 10
Instructional leadership, 7, 10 Leading change, 9
Instructional leadership model, 5, 22 Leading for Results, 9
Instructional planning, 11 Leading in Context, 9
Intelligent Tutoring System (ITS) Leading people, 9
technology, 37 Leading teaching and learning, 12
International Society for Technology in Leading teams cluster of competences, 12
Education (ISTE), 8 Learner characteristics, 99
Internet of Things technologies Learning analytics (LA)
citizen’s individual attainment, 298 digitization in education, 149, 150
competence-driven CPL model, 294 formative assessment, 150, 151
core dimensions, 299 Learning domains, 98
cyber-physical learning system architecture Learning log, 150, 151
(see Cyber-physical learning system Learning management systems (LMS), 37, 51
architecture) Learning objective, 151
Demand-Response schemes, 294 Learning options, 99
Digital Smart Citizenship competences, Learning outcomes, 96
280 Learning place, 135
experimental study, 298 Learning preference, high school students
power-load monitoring services, 294 academic achievements, 106
situated-learning process, 294 affective domain of learning outcomes, 96
smart cities Blooms’ cognitive taxonomy, 96
CPL, 283, 284 classroom learning, 107
digital smart citizenship learning, cognitive and affective domains, 96
281–283 cognitive domain, 97
economy, 281 domains, 96, 105
environment, 281 educational implications, 97
governance, 281 and effective learning, 100–101
human capital, 281 e-learning, 106
living, 281 formal learning, 97
mobility, 281 Gagné’s theory of instruction, 96
people, 281 ICT options, 97
smart cities plans, 280 ICTL, 98, 99, 107
Smart City functions, 280 in relationship, 97
smart meter and washing machine, 296, 297 inform educators and instructional
smart meter, washing machine and dryer, 297 designers, 97
washing machine, 294–296 information behavior, 106, 107
Internet-mediated virtual spaces, 106 information seeking, 107
Interstate School Leaders Licensure
Consortium Standards, 11
Italian context, 174, 185, 186

306 Index

Learning preference, high school students individual efforts and group dynamics, 118
(cont.) pedagogical notions, 118
schema, 113, 126
information sharing, 107 self-directed and social nature, 118
internet-mediated virtual spaces, 106 self-regulated learning, 117
Jung’s theory of personality, 96 social presence, 117
learner characteristics, 99–100 students’ engagement and interaction, 113
learning domains, 98 theoretical approach, 118
learning options, 99 within learning community, 113
learning style, 106 Learning Relationships (LR), 292
learning-related activities, 97 Learning style, 96, 97, 106
LSI, 98 Learning-related activities, 97
MBTI, 98 Lecture-based approach, 28
MOOCs, 107 Leisure-oriented technologies for adults, 86–87
online and e-learning, 97 Literature review, flipped/inverted classroom
outcomes, 96
Pew survey analysis, 106 approaches
psychomotor domain, 96 benefits, 37, 38
research methods challenges, 38

analysis, 104–105 M
average variance extract and Mahara e-portfolio, 119
Mahara platform area, 119
discriminant validity, 102, 103 Managerial leadership, 7
by gender, 104, 105 Managerial leadership model, 5, 22
by STEM major, 104, 105 Massive online open courses (MOOCs), 107
CFA measurement model, 102, 103 MBTI, 98
CFA model fit indices, 102 Microcomputer-Based Laboratory (MBL), 74
limitations, 105 Micropolitical leadership, 7
measurement and instruments, 102–104 Mobile device usage
participants, 101
reliability, 102, 103 advanced infrastructure, 46
research questions, 101 applications, 53
STEM, 107 comparative study, 49
styles, 96, 97 data analysis, 50, 53
technology-mediated environments, 106 design, 49, 50
technology-mediated learning, 98 DHBW Mannheim, 46
validated survey instruments, 98 educational institutions, 45
virtual learning spaces, 98, 106 e-Readers, 46
Learning presence (LP) frequency, 50, 51
analysis of student publications increased difference over time, 51
classification, 123–125 instrument, 50
CoI model, 123 learning technology (see Mobile learning
in course blog, 124, 125
indicators of monitoring category, 124 technology)
knowledge transformation, 123 participants, 50
online writing in blogs, 126 students’ tasks in higher education, 46–47
peer interactions, 123 Mobile learning technology
self-regulated learning, 123 e-Readers, 47–49
students’ participation, 125 and features, 51
teaching and social and cognitive increased difference over time, 52
rating the usability, 52
presence, 123 tablets, 47–49
categories, 126 Modeling-based learning, 58
cognitive presence, 117 Monoglossic (scientific discourse) language, 134
CoI model, 117, 118
forms, 113

Index 307

Moral stewardship, 10 Online laboratory, 179, 180
Motivation behavior, 174, 175 Online learning, 176–178
Multiple analysis of variance (MANOVA), 104 Online learning communities, 127
Online software applications, 133
N Openness to Challenge (OC), 292
NanoCity game Operating cluster, 7
Operational management, 9
computer game, 265 Organizational leadership, 10, 11
evaluation, 270, 271
Future Protection Initiative, 265 P
gameflow and game elements, 266, 267 Page flip
Nanoscientist Interest scale, 276
nanotechnology research, 269, 270 assignments and learning
PDA (see Personal Digital Assistant materials, 156–158

(PDA)) chi-squared test, 156, 157
post-gameplay interviews, 271 data acquisition, 152
STEM curriculum, 276 formative assessment, 158
students, 270, 271 grade on assignment, 154–156, 158
transformational games, 265 page flip count, 153, 155
Unity3D supports, 265 target courses, 152
Nanotechnology Interest scale, 273 target materials, 153
National Assessment of Academic Ability target unit, 152
transactional logs, 151
(NAAA), 151 Particulate nature of matter, 72
National Child Welfare Workforce Institute, 9 Path analysis model, 185
National College for School Leadership Pedagogical approach, 62–64
Personal code of ethics, 6
(NCSL), 6, 9 Personal Digital Assistant (PDA)
National Computer Conference meeting, 84 biography log, 267
National Professional Qualification for city status, 268
information log, 267
Headship Competence Framework, map, 268
9 objectives, 267
Next Generation Science Standards (NGSS), Personal leadership, 11
232, 233, 236 Pew survey analysis, 106
North Carolina State Board of Education, 6 Preparing for exams, 47
Nvivo software, 250 Problem-Solving Practices, 7
Professional culture for teachers and staff, 11
O Professional standards
Old-fashioned technologies, 86
Online academic writing for educational leaders, 6
Professional technologies for children
and Content 2.0
and peer feedback, 115 biotechnology, 90
blended and distance learning sensorimotor extension
formats, 114
blogs, 115, 116 and augmentation, 90, 91
cognitive benefits of peer interaction, 117 tools for fashioning/outputting/customizing
e-learning, 114
instruction, 114 novel materials, 91
learner-centered and personalized Promote context relative to politics, 6
forms, 114 Psychomotor domain, 96
skills, 114
student performance, 115 Q
subject areas, 114 Qualitative methodology, 134
Quasi-experimental studies, 73
students’ engagement, 120–122

308 Index

R instructional, managerial, distributed
Reciprocal effects model, 175 and transformational, 20
Relating cluster, 7
Relational leadership, 10 leading self, 15, 20
Relevance of Science Education Project leading the external processes of the

(ROSE), 271 school organization, 15, 21
Resilience (R), 292 leading the internal processes of the
Responsibility for Learning (RL), 292
school organization, 15, 16
S leading the staff and students, 15, 17–19
School autonomy, 4 managerial, distributed and
School community, 11
School improvement, 5 transformational, 20
School leadership proposed, 14
National Professional Qualification for
academic research, 5
competence frameworks (see School Headship Competence Framework, 9
operating cluster, 7
leadership competences) Principals and Vice-Principals in British
concept of, 4
constitute learning ecosystems, 3 Columbia, 10
definition, 4 professional standards for educational
educational policies, 4
educational policy, 5 leaders, 6
executive, 5 relating cluster, 7
instructional, 5, 22 standards for administrators, 8
international academic and educational standards for school administrators, 6, 7
sustaining cluster, 8
policy literature, 4 Teacher Leader Competence Framework, 12
K12 school leaders, 22 teacher leader model standards, 8
managerial, 5, 22 The Australian Professional Standard for
meta-framework, 4
NCSL, 6 Principals, 12
perceived and operationalized, 21 turnaround leaders, 7
school improvement, 5 School vision, 8
single leader models, 22 Science and engineering practices (SEP)
system, 6 application, SRL strategies, 234
teaching, learning and Benchmarks for Scientific Literacy, 232
in chemistry and Earth science, 235
assessment, 3 experimental design, 232
transactional, 5 instructional and assessment, 236
transformational, 5 integrated models, SRL, CT and SEP, 234
School leadership competences learning domains, 232
Central5—A Central European NGSS, 232, 233
SEP, 233
View on Competencies SRL theory, 233
for School Leaders, 9, 10 student, 236
classification, 12, 13 Science and Engineering Practices (SEPs), 232
competence continuum, 11 Science, technology, engineering and
enabling cluster, 7
framework, 9–11 mathematics (STEM), 261, 262, 276
Interstate School Leaders Licensure Secondary education
Consortium Standards, 11
meta-framework creativity, 242
executive, distributed/transformational data collection and analyses, 250, 251
leadership models, 15, 20 description, 242
frequencies, 15 dialogical interactions, 256
female language teachers and students, 249
female secondary teachers and students, 249
objectives, 248–249
orchestrate students’ creativity, 256
procedure, 249

Index 309

sociocultural psychology, 242, 243 Students’ self-regulation practices
students’ perception, collaborative and academic writing, 112
challenge educators and institutions, 111
creative processes, 251–256 comments to previous posts, 112
technology-enhanced pedagogical Content 2.0, 114–117
content posts, 112
framework, 244–248, 256 content-textual approach, 112
twenty-first century society, 242 course blog, 126
web 2.0 tools (Cacoo/Drive), 256 course design and research method
Self leading and management competence, 10 analysis framework, 120
Self-cluster of competences, 12 context and participants, 118–119
Self-recognition, 7 course workflow, 119
Self-regulated learning (SRL) students’ performance, 119
aspects, 225 educational blogs, 112
in CLIL online learning e-learning programs, 111
investigation and empirical testing, 127
environment, 139–142 LP (see Learning presence (LP))
foundations, 225, 226 online academic writing, 127 (see Online
researchers, 224 academic writing)
and students’ technology use, 137–139 online environments, 113
theoretical paradigms, 224 online learning communities, 127
Self-regulation, 176, 177 online writing environment, 126
Sensorimotor extension participants, 113
pivotal role, 127
and augmentation, 90, 91 qualitative analysis, 113
Shared vision, 6 research objectives, 113
Single leader models, 22 research regarding online learning, 112
Skill assessment students’ engagement in online writing,
120–122
answer categories, 211–214 Web 2.0 applications, 111
Bebras task, 216 Web 2.0 learning environments, 112
cognitive abilities, 214
DD, 216 Supercollider, 84
development and validation, 216 Sustaining cluster, 8
innovation practices, 217 System leadership model, 6
pre–post test, 212, 214 Systemic improvement, 8
software-based approach, 213
T-Test, 214 T
Smart citizenship competence-disposition Tablets, 46–55
Task difficulty, 164
scores, 291 Task Engagement Index, 164
Smart-object virtualization, 287 Teacher Leader Competence Framework, 12
Social presence, 117 Teacher leader model standards, 8
Socio-economics, 6 Teacher Leadership Exploratory Consortium, 8
Sphere of digital learning technologies, 76 Technological Pedagogical Content
Staff’s professional development, 6
Standards for Administrators, 8 Knowledge (TPCK), 76
Standards for school administrators, 6, 7 Technology-mediated environments, 106
Stanford Time Perspective Inventory (STPI), 179 Technology-mediated learning, 98
State-of-the-art industrial technologies, 86 Technology-rich learning environment (TRE),
STEM career interest, 101, 104, 105, 107
Strategic leadership, 7, 9 195, 197, 200, 219
Strategic leadership establishes systems, 11 Temporal perspective, 176
Student surveys data Tetrahedral model, 75
3D–human body system model, 133
closed-response items, 272, 273
intervention, 271–272
open-response items, 273, 274
Student voices, 132–136, 141
Student-centered learning, 6

310 Index

Transactional leadership model, 5 Video-based learning platforms, 29
Transformational games Virtual experiments, 58
Virtual learning spaces, 98, 106
computer-based games, 263, 264 Visual programming, 205, 206
NanoCity (see NanoCity game) Vizwik techniques, 195, 200, 201, 204–206,
situated learning, 264, 265
Transformational leadership model, 5 216, 219
Translanguaging, 134, 136, 140–142, 144, 145 Vocabulary training website score comparison,

U 143, 144
Urban School Leadership Center, 7 Voice recordings, 136

V W
Validated survey instruments, 98 Web 2.0 applications, 111
Video footage, 136 Web 2.0 learning environments, 112
What the Dormouse Said, 84
Working on written papers, 47