Pedagogy of the Data Oppressed – developing a pedagogy for working with Students to Solve Problems w Members : OER19

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Public Group Active 4 years, 8 months ago

Wed, Apr 10 2019, 5:20pm – 6:20pm

Authors: Mary Loftus

Room: G006

Theme: Critical literacies

Type: Workshops and panels (60mins)

Description

Session Description

How do we teach and learn with our students about data literacy in our learning environments and in our lives? How do we teach and learn with each other about the ethics and implications of a data-informed teaching practice? We now live in systems where our data is being used in ways over which we have seemingly little control and often little knowledge or insight. The proliferation of data and information makes analysis and interpretation ever more complex. (Ridsdale et al., 2015)

In this workshop, we will explore some hands-on approaches to building an open pedagogy of data literacy; we will share and develop ways in which teachers and students can work together, and develop a praxis approach to use data to ‘read the world’ around them (Freire, 1970).

Session content

Workshop Running Order:

Introduction & Workshop Setup – 30 mins

Data & Learning Scenarios – 10 mins

Scene Setting and Small Group formation – 5 mins

Parallel Small Group discussions – 15 mins
Critical Thinking with Data (Allison and Pan, 2011)
Data Culture & Data Ethics (Prinsloo and Slade, 2016)
Issues with Helping Students to Gather & Interpret Data
Inventory of Skills & Experiences in the Room

Experiential Student Problem Solving with Learning Data Walk-through – 30 mins

Problem Solving with Data – a Design Thinking approach – 5 mins
Empathise – understanding the problem from a human centred starting point
Define
Ideate
Prototype
Test & Validate
Implement
and iterate…

Data Preparation & Validation – 10 mins
Ethics & Privacy Checklist (Drachsler and Greller, 2016)
Data Discovery and Collection
Data Cleanup & Conversion
Evaluating and Ensuring Quality of Data and Sources

Data Analysis & Visualisation – 15 mins
Data Analysis
Regression
Bayesian Network
Data Analysis & Model Validation
Data Visualization

Reviewing & Improving our Draft Pedagogy – 30 mins
From this experiential walk-through of our proposed pedagogy for working with students to solve problems with data, we hope participants will be well positioned to critique and improve the approach. The final phase of the workshop will give participants an opportunity to share experiences, question underlying principles and values, and add their own experiences to the mix.

Parallel Small Group discussions – 15 mins
Reflection from a Student’s point-of-view
Reflection from a Teacher’s point-of-view

Plenary Discussion of Proposed Changes to this Data Literacy Pedagogy – 15 mins

References

Allison, J., Pan, W., 2011. Implementing and Evaluating the Integration of Critical Thinking into Problem Based Learning in Environmental Building. J. Educ. Built Environ. 6, 93–115. https://doi.org/10.11120/jebe.2011.06020093
Drachsler, H., Greller, W., 2016. Privacy and analytics: it’s a DELICATE issue a checklist for trusted learning analytics. ACM Press, pp. 89–98. https://doi.org/10.1145/2883851.2883893
Pearl, J., 1985. Bayesian networks: A model of self-activated memory for evidential reasoning. University of California (Los Angeles). Computer Science Department.
Prinsloo, P., Slade, S., 2016. Student vulnerability, agency, and learning analytics: An exploration. J. Learn. Anal. 159–182. https://doi.org/10.18608/jla.2016.31.10
Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., Kelley, D., Matwin, S., Wuetherick, B., 2015. Strategies and best practices for data literacy education: Knowledge synthesis report.

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