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Student Assessment eBook

Better Evidence, Better Decisions, Better Learning

de Dylan Wiliam, Nancy Frey e Douglas Fisher
idioma: inglês
Editor: SAGE PUBLICATIONS, maio de 2024 ‧
33,61€
30,25€
10% DESCONTO IMEDIATO
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Fuel student-driven success and enhance learning with better assessment

What if there was a better way to collect and interpret assessment data that could strengthen the link between teaching and learning? Student Assessment: Better Evidence, Better Decisions, Better Learning is the innovative guide to show you how its done and done right. This unique book offers a new assessment model focused on decision-driven data collection and provides an arsenal of tools for collecting better evidence to increase student learning. You will learn:

  • the importance of assessment in the teaching and learning process
  • what a decision-driven data collection assessment model looks like and how it differs from a more traditional data-driven decision-making model
  • how to effectively establish learning intentions and success criteria within assessments to actively engage students in their own learning
  • how to implement an array of assessment tools that yield better evidence and improve decision-making
  • how to address the challenge of ensuring authenticity in students responses when AI-generated content is becoming more common

With this guide in hand, youll be ready for a better approach to assessmentarmed with practical tools and strategies to ensure students can analyze, synthesize, adapt, critique, and most importantly, take charge of their own learning.

Student Assessment

Better Evidence, Better Decisions, Better Learning

de Dylan Wiliam, Nancy Frey e Douglas Fisher

Propriedade Descrição
ISBN: 9781071949542
Editor: SAGE PUBLICATIONS
Data de Lançamento: maio de 2024
Idioma: Inglês
Páginas: 176
Tipo de produto: eBook
Formato e Compatibilidade:
Classificação Temática: eBooks em Inglês > Ensino e Educação > Teorias Educacionais e Currículo
EAN: 9781071949542
Acessibilidade: Ver características de acessibilidade indicadas pelo editor