Friday, July 29, 2016

You are who you learn: authenic assessment and team analytics?

Forget facts - that's the learning of the past. The learning of the future is soft skills. But how do you assess it? Peter Williams argues that we can approach the problem though learning analytics. Along the way he also provides the best theoretical introduction to authentic assessment that I've read yet. He also flags Lombardi's definition of authentic learning:

  1. Real-world relevance: the need for authentic activities within a realistic context.
  2. Ill-defined problem: confronting challenges that may be open to multiple interpretations.
  3. Sustained investigation: undertaking complex tasks over a realistic period of time.
  4. Multiple sources and perspectives: employing a variety of perspectives to locate relevant and useful resources.
  5. Collaboration: achieving success through division of labour and teamworking.
  6. Reflection (metacognition): reflection upon individual and team decisions.
  7. Interdisciplinary perspective: encouraging the adoption of diverse roles and thinking.
  8. Integrated assessment: coinciding the learning process with feedback that reflects real-world evaluation.
  9. Polished products: achieving real and complete outcomes rather than completing partial exercises.
  10. Multiple interpretations and outcomes: appreciating diverse interpretations and competing solutions.

So this paper is worth reading for the above reasons. But am I convinced about his pitch for learning analytics as the way forward? No - it's completely fanciful and unsupported by any evidence. Which makes me feel better - we agree on the problem and it's not just me being thick because I can't quite figure the solution.

Assessing collaborative learning: big data, analytics and university futures. Assessment & Evaluation in Higher Education 28 Jul 2016 doi: 10.1080/02602938.2016.1216084
Assessment in higher education has focused on the performance of individual students. This focus has been a practical as well as an epistemic one: methods of assessment are constrained by the technology of the day, and in the past they required the completion by individuals under controlled conditions of set-piece academic exercises. Recent advances in learning analytics, drawing upon vast sets of digitally stored student activity data, open new practical and epistemic possibilities for assessment, and carry the potential to transform higher education. It is becoming practicable to assess the individual and collective performance of team members working on complex projects that closely simulate the professional contexts that graduates will encounter. In addition to academic knowledge, this authentic assessment can include a diverse range of personal qualities and dispositions that are key to the computer-supported cooperative working of professionals in the knowledge economy. This paper explores the implications of such opportunities for the purpose and practices of assessment in higher education, as universities adapt their institutional missions to address twenty-first century needs. The paper concludes with a strong recommendation for university leaders to deploy analytics to support and evaluate the collaborative learning of students working in realistic contexts.

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