Assessment Analytics in Education (eBook)

Designs, Methods and Solutions
eBook Download: PDF
2024 | 2024
XIV, 412 Seiten
Springer International Publishing (Verlag)
978-3-031-56365-2 (ISBN)

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This book is about the current state of research in online assessment. 
The growth of this field is set to accelerate exponentially with emerging opportunities for automatic data collection and analysis. Yet, the future of online assessment faces major challenges including, perhaps most importantly, the extent to which assessments, when enabled by technology, can serve simultaneously the needs of learners, teachers and those of the enterprise of education. This book details, specifically, the multiple ways in which online assessment can be utilized, such as:
  • Providing virtual coaching or tutoring
  • Offering appropriate scaffolding
  • Allowing analysis of student decision-making  
  • Providing the mechanism for students to review and comment on each others digital creations
  • Creating a space for online discussion
  • Providing expert coaching for modeling and animation work
With the increased availability of vast and highly varied amounts of data from learners, teachers, learning environments, and administrative systems within educational settings, further opportunities arise for advancing pedagogical assessment practice (Ifenthaler et al., 2018). 
This book fully details these opportunities, as well as privileges and constraints of analytics-enhanced assessment,  harnessing formative as well as summative data from learners and their contexts in order to facilitate learning processes in near real-time and help decisionmakers to improve learning environments. 


Muhittin Sahin is Assoc. Prof. and project manager at Presidency of the Republic of Türkiye Human Resources Office in Ankara, Türkiye and visiting researcher at University of Mannheim, Germany. His research interests relate with instructional technology, learning analytics, educational data mining, multi-criteria decision making, statistic, sequential analysis, e-assessment, artificial intelligence in education, and intelligent learning management systems. He also collaborates in a project about developing an adaptive mastery testing system. He is the Associate Editor of the Technology, Knowledge and Learning  and Journal of the Knowledge Economy journals.


Dirk Ifenthaler is Professor and Chair of Learning, Design and Technology at University of Mannheim, Germany and UNESCO Deputy Chair on Data Science in Higher Education Learning and Teaching at Curtin University, Australia.

His previous roles include Professorand Director, Centre for Research in Digital Learning at Deakin University, Australia, Manager of Applied Research and Learning Analytics at Open Universities, Australia, and Professor for Applied Teaching and Learning Research at the University of Potsdam, Germany. He was a 2012 Fulbright Scholar-in-Residence at the Jeannine Rainbolt College of Education, at the University of Oklahoma, USA. At the University of Latvia, Latvia, he was involved in the Faculty of Education, Psychology and Art as a Visiting Senior Researcher. In 2023, Dirk was a Visiting Professor in the Dipartimento di Pedagogia, Psicologia, Filosofia at the University of Cagliari, Italy.

Dirk's research focuses on the intersection of cognitive psychology, educational technology, data analytics, and organisational learning. His research outcomes include numerous co-authored books, book series, book chapters, journal articles, and international conference papers, as well as successful grant funding in Australia, Germany, and USA.

He is the Editor-in-Chief of the Technology, Knowledge and Learning and Editor-in-Chief of Educational Technology & Society.

Erscheint lt. Verlag 7.5.2024
Reihe/Serie Advances in Analytics for Learning and Teaching
Zusatzinfo XIV, 412 p. 93 illus., 54 illus. in color.
Sprache englisch
Themenwelt Sozialwissenschaften Pädagogik Schulpädagogik / Grundschule
Schlagworte assessment analytics • assessment theory • Feedback Design • Feedback theory • Intervention • Learning Analytics • learning theories
ISBN-10 3-031-56365-4 / 3031563654
ISBN-13 978-3-031-56365-2 / 9783031563652
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