The #PLENK2010 subject of the week is the eXtended web. In my blog post used in the resources of this week, I highlight a number of issues related to this: The use of Intelligent data for PLE development and networked learning, the challenges of an open online networked environment for learning, and access to technology.
In this post I would like to delve a little deeper in the first aspect on how data can be used to 1. enhance people's searches, and 2. on how data can be used by educators, or 'knowledgeable others' to enhance people's learning.
I will start with the second point first. Ken Clark for instance carried out a study to find out if 'individual teaching staff [can by], reflecting on their courses, learn anything important from examining their courses through analytics? How can this be done effectively? What do they find?'
Of course academics and researchers have researched people's learn and teaching for quite some time, but it is only recently with the introduction of LMSs and their back-office data (eg on how often people access the course, for what activity etc.) that this type of data has been analysed. It has been analysed by administrators, to find out' learning outcomes', but to me this information is fairly limited. It becomes much more rich when you also analyse the 'discourse', the text people have written for instance in discussion boards, or on Massive Open Online courses such as PLENK, other written material and artifacts produced by students and facilitators, such as blog posts, to reach a better understanding of the ways in which people teach and learn.This analysis might help to enhance future courses and future teaching, facilitation and learning. Educators can be involved in this research themselves and directly use their findings to take action to make changes to course/curriculum/interactions etc.
The second new possibility that data collection offers, is that collected data might inform people's searches: By collecting data on people's earlier learning projects, or from people's personal profile, ranking systems and recommendations could be produced that might provide people with 'smart' information, more relevant to their needs than without.
Of course there are problems with this type of data collection as the use of 'intelligent marketing' is currently showing us. Especially privacy issues have been highlighted as being problematic as before you know it people you don't want to know particular things about you, will know them and use them in ways that are not necessarily what you intended the data to be used for in the first place. So there are also ethical issues for research.
On the other hand, the information abundance about which I wrote in an earlier post, make it near impossible for a single human being to find and analyse all information by him/herself without some type of aggregation and filtering, or communication with others.