Educational Data Literacy

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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).

Author(s): Demetrios Sampson, Zacharoula Papamitsiou, Dirk Ifenthaler, Michail Giannakos, Sofia Mougiakou, Dimitra Vinatsella
Series: Advances in Analytics for Learning and Teaching
Publisher: Springer
Year: 2022

Language: English
Pages: 123
City: Cham

Contents
List of Figures
List of Tables
About the Authors
Abbreviations
Chapter 1: Introduction
Chapter 2: Educational Data Literacy and Educational Data Literacy Competence Frameworks: An Environmental Scan
2.1 Scope
2.2 Educational Data Literacy Conceptual Definitions
2.2.1 Educational Data and Data-Driven Decision-Making in Educational Settings
2.2.2 Data Literacy and Educational Data Literacy
2.3 Educational Data Literacy Competence Frameworks
2.3.1 Educational Data Literacy Competence Frameworks: Environmental Scan
2.3.2 Analysis of Educational Data Literacy Competence Frameworks in Relation to Their Capacity to Support Digital Education Professionals
2.3.2.1 EDL-CF1: Strategies and Best Practices for Data Literacy Education (Ridsdale et al. (2015))
2.3.2.2 EDL-CF2: Conceptual Framework for Data Literacy for Teachers DLFT (Mandinach & Gummer, 2016)
2.3.2.3 EDL-CF3: Teachers’ Ability to Use Data to Inform Instruction (Means et al., 2011)
2.3.2.4 EDL-CF4: Incorporating Data Literacy into Information Literacy Programs (Prado & Marzal, 2013)
2.3.3 Analysis of Existing Data-Related Ethics Frameworks
2.4 Educational Data Literacy Professional Development and University Courses
2.4.1 Educational Data Literacy Courses: Environmental Scan
Chapter 3: The Learn2Analyse Educational Data Literacy Competence Profile
3.1 Scope
3.2 A Working Definition of Educational Data Literacy
3.3 Educational Data Literacy Competence Dimensions
3.4 Synthesis of Initial Learn2Analyse Educational Data Literacy Competence Profile
3.5 Experts’ Validation of the Initial L2A Educational Data Literacy Competence Profile
Chapter 4: Learn2Analyse Educational Data Literacy Competence Profile: From Theory to Practice
4.1 Scope
4.2 Exemplary Learning Outcomes
4.3 Use-Case Examples for Selected Target Groups
4.3.1 Use-Case Example for Instructional Designer in Industry Context
4.3.1.1 Background Story for Instructional Designer Use-Case
4.3.1.2 Use-Case Activities for Instructional Designer in Industry Context
4.3.2 Use-Case Example for e-Trainer in Higher Education Context
4.3.2.1 Background Story for e-Trainer Use-Case
4.3.2.2 Use-Case Activities for e-Trainer in Higher Education Context
4.3.3 Use-Case Example for the School Teacher of Blended Learning Courses in the K-12 Education Context
4.3.3.1 Background Story for the School Teacher Use-Case
4.3.3.2 Use-Case Activities for the School Teacher in K-12 (Primary and Secondary) Blended Teaching and Learning Courses
Chapter 5: Conclusions and Future Direction
Appendices
Appendix A: The Generic Competence Dimensions and Competence Statements of Existing Educational Data Literacy Competence Frameworks
EF1: Data Science Competence Framework (CF-DS) (Demchenko & Belloum, 2017)
EF2: Ten Simple Rules for Responsible Big Data Research (Zook et al., 2017)
EF3: Guidelines for the Ethical Use of Digital Data in Human Research (Clark et al., 2015)
EF4: Data Ethics Framework (Hancock, 2018)
EF5: A Unified Ethical Frame for Big Data Analysis—Big Data Ethics Initiative (IAF, 2015)
EF6: The Data Ethics Canvas (ODI, 2017)
Appendix B: Educational Data Literacy Professional Development and University Courses
Appendix B.1—Courses’ Overview
Appendix B.2—Learning Objectives and Syllabus per Course Mapped to the Identified Dimensions
References
Index