The semantic web has received far more hype over the last five years than practical results on our daily lives would suggest. However, there are some fascinating developments underway with linked data that may have a dramatic impact in how we encounter information. The task is enormous - remove the ambiguity of terms/concepts/words. But, as Peter Norvig's video last week suggested - a large enough data set, combined with probabilistic computing, can provide a huge leap forward. What happens when we tag big data and combine it with linked data? That's the topic this week - and after you've had time to review a few readings/videos, please share your thoughts about how this might impact education, learning, and training and development...
George Siemens
Discussions started by George Siemens
Before our elluminate session with Ryan Baker yesterday, I polled attendees about whether or not their organizational had a clearly defined strategy with regards to data use for learning and knowledge. Out of ~50 people, only one person stated their organization had such a strategy.
This is surprising. We keep hearing about how we are in the "knowledge age"...and that knowledge is our most important advantage as institutions, companies, and countries. Governments provide enormous investments in knowledge-related work: research, universities, etc. Essentially, knowledge is to the future of work as oil was (is) to the industrial world - i.e. the basis on which the economy is starting to run. And yet, organizations aren't effectively considering how data can be analyzed to provide new insights, learning, and knowledge growth. Or, if they are considering it, they don't appear to be doing so from a strategic, systems-wide level.
Two questions:
1. What should an organizational data strategy look like?
2. What are the skills/people that should comprise and analytics team?
I'm not too clear yet on question 1, but for the analytics team, I'd suggest:
1. Leadership should be involved - not to confine the work of the team, but to communicate its value to others in the organization. Insights generated by an analytics team will need to be championed within the university/corporation. Important developments will have greatest impact if they are scaled up systemically.
2. Data scientists. This is a vague term, but I use it here to mean those who define the structural/organizational data needs and to ensure that the right types of data are being collected to help analyze and improve learning
3. Data miners - these are the folks who run statistical analysis of data, seek patterns, etc. Obviously, they work v. closely with the data scientists
4. Sociologist/psychologist: people who understand the social (softer) aspect of interaction and knowledge growth/exchange. An algorithm is one way of analyzing a domain. Other approaches exist and need to also be explored.
5. Faculty/trainers: individuals who are able to augment discoveries or innovations by the data scientists/miners by offering a perspective based in the reality of teaching/learning or workplace settings.
Who else should be involved?
This is surprising. We keep hearing about how we are in the "knowledge age"...and that knowledge is our most important advantage as institutions, companies, and countries. Governments provide enormous investments in knowledge-related work: research, universities, etc. Essentially, knowledge is to the future of work as oil was (is) to the industrial world - i.e. the basis on which the economy is starting to run. And yet, organizations aren't effectively considering how data can be analyzed to provide new insights, learning, and knowledge growth. Or, if they are considering it, they don't appear to be doing so from a strategic, systems-wide level.
Two questions:
1. What should an organizational data strategy look like?
2. What are the skills/people that should comprise and analytics team?
I'm not too clear yet on question 1, but for the analytics team, I'd suggest:
1. Leadership should be involved - not to confine the work of the team, but to communicate its value to others in the organization. Insights generated by an analytics team will need to be championed within the university/corporation. Important developments will have greatest impact if they are scaled up systemically.
2. Data scientists. This is a vague term, but I use it here to mean those who define the structural/organizational data needs and to ensure that the right types of data are being collected to help analyze and improve learning
3. Data miners - these are the folks who run statistical analysis of data, seek patterns, etc. Obviously, they work v. closely with the data scientists
4. Sociologist/psychologist: people who understand the social (softer) aspect of interaction and knowledge growth/exchange. An algorithm is one way of analyzing a domain. Other approaches exist and need to also be explored.
5. Faculty/trainers: individuals who are able to augment discoveries or innovations by the data scientists/miners by offering a perspective based in the reality of teaching/learning or workplace settings.
Who else should be involved?
What are your concerns with analytics when applied to learning and knowledge? What types of critiques and concepts should we explore/consider?
I've started with a few quick thoughts on the topic here: http://www.learninganalytics.net/?p=101
I've started with a few quick thoughts on the topic here: http://www.learninganalytics.net/?p=101
We've been focusing on learning analytics in higher education. Corporations are making extensive use of analytics and business intelligence. IBM in particular has been a leader on this front (see the book Numerati for an overview of their extensive and at times disturbing use of analytics in improving individual/team effectiveness).
So - for those of you that represent or have interest in the corporate space, what are your interests/concerns/thoughts around the use of analytics to improve learning and/or collaboration?
So - for those of you that represent or have interest in the corporate space, what are your interests/concerns/thoughts around the use of analytics to improve learning and/or collaboration?
As the first week in this course, we'll spend some time trying to develop some familiarity with language and concepts. Use this forum to explore the readings and resources from week 1.