rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Educational Data Analytics for Teachers and School Leaders
  name:
    name part: Mougiakou, Sofia.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Vinatsella, Dimitra.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Sampson, Demetrios.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Papamitsiou, Zacharoula.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Giannakos, Michail.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Ifenthaler, Dirk.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2023
    edition: 1st ed. 2023.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XII, 238 pages 94 illustrations, 82 illustrations in color)
  abstract: 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 t…
  table of contents: Chapter 1: Online and Blended Teaching and Learning supported by Educational Data -- Chapter 2: Adding Value and Ethical Principles to Educational Data Chapter -- 3: Learning Analytics Chapter -- 4: Teaching Analytics -- Appendix: Learn2Analyse Educational Data Literacy competence framework.
  note: by Sofia Mougiakou, Dimitra Vinatsella, Demetrios Sampson, Zacharoula Papamitsiou, Michail Giannakos, Dirk Ifenthaler.
  note: Open Access
  subject:
    topic: Educational technology
  subject:
    topic: Quantitative research
  subject:
    topic: Digital Education and Educational Technology
  subject:
    topic: Data Analysis and Big Data
  classification: LB1028.3
  classification: 371.33
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Advances in Analytics for Learning and Teaching
  identifier: 9978-3-031-15266-5
  identifier: 9783031152665
  identifier: https://doi.org/10.1007/978-3-031-15266-5
  access condition: Open Access
  record info:
    record creation date: 221028
    record change date: 20230829014738.5
    record identifier: in00004654311