Participation in Social Media Discussion

This module is delivered in a ‘blended learning’ mode. The basically boils down to my belief that you will learn as much from personal discovery and from your peers based on your own shared experience as you will from listening to my lectures on set topics. As such I expect that at least 50% of your time will be dedicated to participating and interacting with one another (and me) on a variety of social media vehicles (especially appropriate given the topics in our study). Continue reading Participation in Social Media Discussion

Critical Reflection

The final assignment for this module is to evaluate the effectiveness of the module itself. Please critically reflect on the past twelve weeks and consider the following in completing your evaluation:

  1. What did you find to be the most valuable aspect of the module for you personally;
  2. What were the most effective aspects of the module?
  3. What did you find challenging?
  4. What didn’t work and how would you improve it?
  5. What topic would you add to future delivery?

This submission should be of no more than  1,000 words. It will account for 10% of your term mark.

Please submit via email ( before 8 January.

Week 2 – Collaboration

Action Items this Week

  • Visit the forum and respond to the question for week 2 – discuss amongst yourselves;
  • If you haven’t done so: Choose a journal article to review.
  • Form your own groups and choose a topic that you will research, develop and present to the class later on in the term. Choose a topic and lock in your choice via the Group/Topic signup form.
  • For our next class (22 October – No Lecture Next Week) please browse to and explore the Free Software Foundation. What do they espouse? What are the principles behind ‘Free Software’? How is it different from Open Source Software?

Continue reading Week 2 – Collaboration

Algorithmic Governance

Pitched as being one of the potentially most pervasive applications of big data, algorithmic governance posits that in a evidence-driven policy world and perfectly transparent process, machines can both construct and enforce the law. One of the bigger criticisms of this is the potential biases initially engineered into the system.

Case as example, Google image recognises blacks as gorillas.