Analyzing and modeling complex and big data

Mind the Gap
Mind the Gap

The following video raised some interesting points that I will investigate further when considering learning analytics  and big data this week:

  • How big is big data?
  • What to be careful not to miss when using big data?
  • Why do patterns emerge from big data and do we address them and learn from them?

Professor Maria Fasli reminds us that that we should ‘mind the gap’ between big data and hypotheses to avoid missing the opportunity to discover new knowledge.

 

 

Liked on YouTube: Red Dwarf Redux – S10E04 – Fathers and Suns

 

Viewing points – between 8 mins 40 seconds and 10 minutes

This video popped into my head when I was reading the contents of my previous blog post ‘How algorithms rule the world’.

I drew comparison between the idea of allocating police resources based on the output of algorithms and the actions of the on-board computer in the above video.

Both sources suggest that algorithms can be used to predict future behaviors based on past behaviors, probability and recent trends.

The video also links the cyyberpunk themes (covered in Block 1) to algorithmic theme that we are currently studying.

By: cpsaros

Hi Stuart,

Thanks for such positive feedback!

I think it’s an interesting point you make about the LMS. Something I noticed very early on in my second MOOC was how differently the tool was used and I certainly think that it played a big role in inhibiting interaction.

I was really good to be able to discuss this MOOC with you behind the scenes. The interaction we had , help me identify the key differences in regards to community development and community participation.

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By: Renee Furner

Hi Stuart – and thanks for your comment and generous review!

I’ve added a more wordy, written format of my micro-ethnography, because, as you allude to, there are quite possibly more factors that were influential in the low uptake of communication. The short version:
-it’s a new ‘group’ – it’s probably unrealistic to expect norm formation and relational exchanges, as per Kozinets’ community progression model (2010);
-the MOOC only lasts for 6 weeks at any rate, so it is unlikely that participants anticipate future interaction, and therefore they may remain task oriented (Walther, 1997, cited in Kozinets, 2010, p. 24)
-the course is, like many MOOCs, information oriented, and prescriptive about what needs to be learned. Perhaps real participation needs to be student driven: students deciding what and how they learn (and with whom).

Heading to your blog just now.. Thanks again!
Renée

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By: smilligan

Hi Chenée,

I LOVE how you have presented this in Sway. It looks great!

I’m jealous that you got to experience two MOOCs. I’ll be honest and say I was tempted to change too.

It is refreshing to read that you had a more positive experience in your other MOOC – particularly because it too was provided via FurureLearn.

I’ve read other ethnographies that suggest the community experience was influenced by the functionality of the LMS – your findings certainly suggest that this may not be the only reason – which has restored my faith in the MOOC somewhat.

I mentioned to James that our interactions behind the scenes helped to make sense of the course. Perhaps if others were afforded the same communications then their experience would be very different.

It is an very interesting point you make about the pace in which people progressed through the two courses. I fully agree that the main reason for this was peoples unwillingness to become involved in the course community.

Great work as always.

(and bonus points for using Sway)

Stuart

WHat

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