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This book introduces the reader to evidence-based non-formal and
informal science learning considerations (including technological
and pedagogical innovations) that have emerged in and empowered the
information and communications technology (ICT) era. The
contributions come from diverse countries and contexts (such as
hackerspaces, museums, makerspaces, after-school activities) to
support a wide range of educators, practitioners, and researchers
(such as K-12 teachers, learning scientists, museum curators,
librarians, parents, hobbyists). The documented considerations,
lessons learned, and concepts have been extracted using diverse
methods, ranging from experience reports and conceptual methods to
quantitative studies and field observation using qualitative
methods. This volume attempts to support the preparation, set-up,
implementation, but also evaluation of informal learning activities
to enhance science education.
This book introduces the reader to evidence-based non-formal and
informal science learning considerations (including technological
and pedagogical innovations) that have emerged in and empowered the
information and communications technology (ICT) era. The
contributions come from diverse countries and contexts (such as
hackerspaces, museums, makerspaces, after-school activities) to
support a wide range of educators, practitioners, and researchers
(such as K-12 teachers, learning scientists, museum curators,
librarians, parents, hobbyists). The documented considerations,
lessons learned, and concepts have been extracted using diverse
methods, ranging from experience reports and conceptual methods to
quantitative studies and field observation using qualitative
methods. This volume attempts to support the preparation, set-up,
implementation, but also evaluation of informal learning activities
to enhance science education.
Educational Data Analytics (EDA) have been attributed with
significant benefits for enhancing on-demand personalized
educational support of individual learners as well as reflective
course (re)design for achieving more authentic teaching, learning
and assessment experiences integrated into real work-oriented
tasks. This open access textbook is a tutorial for developing,
practicing and self-assessing core competences on educational data
analytics for digital teaching and learning. It combines
theoretical knowledge on core issues related to collecting,
analyzing, interpreting and using educational data, including
ethics and privacy concerns. The textbook provides questions and
teaching materials/ learning activities as quiz tests of multiple
types of questions, added after each section, related to the topic
studied or the video(s) referenced. These activities reproduce
real-life contexts by using a suitable use case scenario
(storytelling), encouraging learners to link theory with practice;
self-assessed assignments enabling learners to apply their attained
knowledge and acquired competences on EDL. By studying this book,
you will know where to locate useful educational data in different
sources and understand their limitations; know the basics for
managing educational data to make them useful; understand relevant
methods; and be able to use relevant tools; know the basics for
organising, analysing, interpreting and presenting
learner-generated data within their learning context, understand
relevant learning analytics methods and be able to use relevant
learning analytics tools; know the basics for analysing and
interpreting educational data to facilitate educational decision
making, including course and curricula design, understand relevant
teaching analytics methods and be able to use relevant teaching
analytics tools; understand issues related with educational data
ethics and privacy. This book is intended for school leaders and
teachers engaged in blended (using the flipped classroom model) and
online (during COVID-19 crisis and beyond) teaching and learning;
e-learning professionals (such as, instructional designers and
e-tutors) of online and blended courses; instructional
technologists; researchers as well as undergraduate and
postgraduate university students studying education, educational
technology and relevant fields.
Educational Data Analytics (EDA) have been attributed with
significant benefits for enhancing on-demand personalized
educational support of individual learners as well as reflective
course (re)design for achieving more authentic teaching, learning
and assessment experiences integrated into real work-oriented
tasks. This open access textbook is a tutorial for developing,
practicing and self-assessing core competences on educational data
analytics for digital teaching and learning. It combines
theoretical knowledge on core issues related to collecting,
analyzing, interpreting and using educational data, including
ethics and privacy concerns. The textbook provides questions and
teaching materials/ learning activities as quiz tests of multiple
types of questions, added after each section, related to the topic
studied or the video(s) referenced. These activities reproduce
real-life contexts by using a suitable use case scenario
(storytelling), encouraging learners to link theory with practice;
self-assessed assignments enabling learners to apply their attained
knowledge and acquired competences on EDL. By studying this book,
you will know where to locate useful educational data in different
sources and understand their limitations; know the basics for
managing educational data to make them useful; understand relevant
methods; and be able to use relevant tools; know the basics for
organising, analysing, interpreting and presenting
learner-generated data within their learning context, understand
relevant learning analytics methods and be able to use relevant
learning analytics tools; know the basics for analysing and
interpreting educational data to facilitate educational decision
making, including course and curricula design, understand relevant
teaching analytics methods and be able to use relevant teaching
analytics tools; understand issues related with educational data
ethics and privacy. This book is intended for school leaders and
teachers engaged in blended (using the flipped classroom model) and
online (during COVID-19 crisis and beyond) teaching and learning;
e-learning professionals (such as, instructional designers and
e-tutors) of online and blended courses; instructional
technologists; researchers as well as undergraduate and
postgraduate university students studying education, educational
technology and relevant fields.
Digital Education is recognised as a key transformative innovation
for K-12 school and university teaching and learning, as well as,
for professional development and vocational training. As a result,
blended and online courses are nowadays widely deployed to meet the
needs of K-12, higher education and vocational training students,
as well as, the needs for professional development of in-service
professionals. In this context, important professional roles in
digital education and training, such as, the Instructional
Designers, who design and develop online and blended courses, and
the Trainers or Tutors who support the delivery of these online and
blended courses, require new professional competences compared to
those assumed at the traditional face to face education and tra
ining programs.This is particularly relevant today, in the post
Covid-19 era, where educational organisations, leaders and teachers
are challenged with reinventing their teaching and learning
environments to offer higher quality, more accessible and inclusive
teaching, learning and assessment. Educational Data Literacy (EDL)
is a core competence for all education professionals, including
school teachers, instructional designers and tutors of online and
blended learning courses, as well as educational institutions'
leaders. Nevertheless, existing professional competence frameworks
for educators pay little attention to EDL, missing out the
potential of using emerging EDL methods and tools in online and
blended teaching and learning - thus there is a need for extending
existing professional competence frameworks for educators with new
competences to accommodate the emerging field of EDL. To this end,
this brief monograph presents a comprehensive proposal of an
Educational Data Literacy Competence Profile (EDL-CP) framework for
education professionals, as well as, exemplary learning outcomes
for the proposed EDL-CP framework, and use-case examples for
indicative target groups, namely instructional designers,
e-Trainers and K-12 school teachers. The work of this book has been
produced within the project "Learn2Analyze - An Academia-Industry
Knowledge Alliance for enhancing Online Training Professionals'
(Instructional Designers and e-Trainers) Competences in Educational
Data Analytics" which is co-funded by European Commission through
the Erasmus+ Program (Cooperation for innovation and the exchange
of good practices - Knowledge Alliances).
This handbook is the first book ever covering the area of
Multimodal Learning Analytics (MMLA). The field of MMLA is an
emerging domain of Learning Analytics and plays an important role
in expanding the Learning Analytics goal of understanding and
improving learning in all the different environments where it
occurs. The challenge for research and practice in this field is
how to develop theories about the analysis of human behaviors
during diverse learning processes and to create useful tools that
could augment the capabilities of learners and instructors in a way
that is ethical and sustainable. Behind this area, the CrossMMLA
research community exchanges ideas on how we can analyze evidence
from multimodal and multisystem data and how we can extract meaning
from this increasingly fluid and complex data coming from different
kinds of transformative learning situations and how to best feed
back the results of these analyses to achieve positive
transformative actions on those learning processes. This handbook
also describes how MMLA uses the advances in machine learning and
affordable sensor technologies to act as a virtual observer/analyst
of learning activities. The book describes how this "virtual
nature" allows MMLA to provide new insights into learning processes
that happen across multiple contexts between stakeholders, devices
and resources. Using such technologies in combination with machine
learning, Learning Analytics researchers can now perform text,
speech, handwriting, sketches, gesture, affective, or eye-gaze
analysis, improve the accuracy of their predictions and learned
models and provide automated feedback to enable learner
self-reflection. However, with this increased complexity in data,
new challenges also arise. Conducting the data gathering,
pre-processing, analysis, annotation and sense-making, in a way
that is meaningful for learning scientists and other stakeholders
(e.g., students or teachers), still pose challenges in this
emergent field. This handbook aims to serve as a unique resource
for state of the art methods and processes. Chapter 11 of this book
is available open access under a CC BY 4.0 license at
link.springer.com.
This book is about the ways in which experiments can be employed in
the context of research on learning technologies and child-computer
interaction (CCI). It is directed at researchers, supporting them
to employ experimental studies while increasing their quality and
rigor. The book provides a complete and comprehensive description
on how to design, implement, and report experiments, with a focus
on and examples from CCI and learning technology research. The
topics covered include an introduction to CCI and learning
technologies as interdisciplinary fields of research, how to design
educational interfaces and visualizations that support experimental
studies, the advantages and disadvantages of a variety of
experiments, methodological decisions in designing and conducting
experiments (e.g. devising hypotheses and selecting measures), and
the reporting of results. As well, a brief introduction on how
contemporary advances in data science, artificial intelligence, and
sensor data have impacted learning technology and CCI research is
presented. The book details three important issues that a learning
technology and CCI researcher needs to be aware of: the importance
of the context, ethical considerations, and working with children.
The motivation behind and emphasis of this book is helping
prospective CCI and learning technology researchers (a) to evaluate
the circumstances that favor (or do not favor) the use of
experiments, (b) to make the necessary methodological decisions
about the type and features of the experiment, (c) to design the
necessary "artifacts" (e.g., prototype systems, interfaces,
materials, and procedures), (d) to operationalize and conduct
experimental procedures to minimize potential bias, and (e) to
report the results of their studies for successful dissemination in
top-tier venues (such as journals and conferences). This book is an
open access publication.
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