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This book presents different approaches for answering the question:
How do we assess computational thinking? The result is a snapshot
of the current state of the field for assessing computational
thinking. The last decade has seen rapid growth in the presence of
computational thinking (CT) in educational contexts. Those working
to advance CT argue that the concepts and skills associated with CT
are essential to succeed in an increasingly computational world. As
a result of these efforts, there has been tremendous growth in
curricula, learning environments, and innovations around CT
education in K-12 classrooms and beyond. As CT grows in prominence,
so too does the need to be able to effectively and equitably assess
learners CT abilities. This volume is a collection of chapters
pursuing different approaches for answering the question: How do we
assess computational thinking? The answers provided span age
ranges, formal and informal contexts, conceptual aspects of CT, and
varying methodological and evaluative strategies. Collectively, the
volume captures the current state of the field for assessing
computational thinking and lays the groundwork for future CT
assessment innovation. Assessing Computational Thinking will be a
key resource for academics, researchers, and advanced students of
Education, Educational Assessment, Educational Research, Psychology
and Research Methods. The chapters included in this book were
originally published as a special issue of Computer Science
Education.
Learning Analytics Goes to School presents a framework for engaging
in education research and improving education practice through the
use of newly available data sources and analytical approaches. The
application of data-intensive research techniques to understanding
and improving learning environments has been growing at a rapid
pace. In this book, three leading researchers convey lessons from
their own experiences-and the current state of the art in
educational data mining and learning analytics more generally-by
providing an explicit set of tools and processes for engaging in
collaborative data-intensive improvement.
Learning Analytics Goes to School presents a framework for engaging
in education research and improving education practice through the
use of newly available data sources and analytical approaches. The
application of data-intensive research techniques to understanding
and improving learning environments has been growing at a rapid
pace. In this book, three leading researchers convey lessons from
their own experiences-and the current state of the art in
educational data mining and learning analytics more generally-by
providing an explicit set of tools and processes for engaging in
collaborative data-intensive improvement.
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