2018 NetSciEd Symposium Data Literacy Discussion

NetSciEd 2018 featured a panel discussion revolving around the role of complexity science, complex networks and education in building up the concept of data literacy. This page includes the transcription of brainstorming session held during the panel discussion.

Figure 4.1: Snapshot of the notes taken during the brainstorming session.

Notes from Discussion

    • What is Data Science about? Networks? Statistical Samples?

    • Different Publication Cycles

    • What are the main data driven approaches?

    • Statistics! = Data Science !

    • Interpretation of Data

  • Lack of Insight

    • It should include theory

    • Various Programmes: How was it implemented?

    • Limits of validity of statistics

    • Avoid sectorialism!

    • Difficult to find a common language

    • Exploration vs. Exploitation

    • Math of network science can be data science

    • All NTW science has to have data

    • Network data science? Outputs?

    • Experiments vs. Observations

    • Replication Crisis Model vs. Norms

    • Challenge of correct interpretation vs. practicality

    • Different backgrounds and conference venues

    • Responsible Decision Making: ETHICS - PRIVACY

    • Awareness What to do? What can be done?

    • Deliverable Data Literacy - Summary vs. Discussion

    • Code of data ethics, open source, checked by the community.

    • Algorithms stated explicitly.

    • Need to provide theoretical defense against biases due to profit. Investigate free of ulterior motives.

    • Make people aware of the limits and built-in assumptions (biases) of a given prediction.

    • Translate ideas and knowledge across fields. “Google translate” across fields. Promote respect across fields. Try to learn from each other.

    • How can data literacy develop?

    • Importance of BOTTOM-UP approaches.

    • Crowdsourcing ! Get in touch ! Make it easy to join! Make it visible! Highlight connections already present!

    • Proof of concept.

    • How to construct an activity? ! Good practices, Make it simple! Different activities activate different skills! Research! conceptual knowledge.

    • Cross the boundaries. Underline uncertainty / Pedagogy ! Personalised learning, Digital tools / Cross-sectional data! Possible variants of paths !

    • Measure learning performance, teacher experts.

    • Designing conceptual paths. De facto prerequisites ! Context dependent ! What are core competencies?.

  • Consequential approach and synchronized structure

    • Use “Complex” Networks. Not made trivial, “Seductive details” danger. Best Practice !

    • Tools to rely on, Learn by doing, Danger in oversimplification, Importance of relational learning.

    • Simple vs. Complex - we have to be both.