User:Moulton

From Wikiversity

Biographical Information[edit]

I am also known as Moulton on the English Wikipedia and on Wikipedia Review.

I am currently a Visiting Scientist at the MIT Media Lab in the Affective Computing Research Group. My long-term field of research is the Role of Emotions in Learning. I am currently working on the role of the Bardic Arts as a traditional method of learning.

I am also a volunteer science educator in the Discovery Spaces at the Boston Museum of Science.

My other affiliations include the Institute for Intelligent Systems at the University of Memphis and the School of Communication and Journalism at Utah State University where I assist in the curriculum in Online Journalism.

I was formerly a Visiting Scientist in the Educational Technology Research Group at BBN Systems and Technologies. Additional professional background information can be found here.

My interest in writing encyclopedia articles in my areas of expertise dates back to 2004 when I co-authored an 8-page article entitled "Electronic (Virtual) Communities" in the Encyclopedia of International Media and Communications. My newest effort at crafting encyclopedia articles is on Google Knol, where I have prepared a number of articles based on 25 years worth of original research.

Some of my other research interests include puzzlecraft, building online communities, and the functional characteristics of rule-driven systems.

I have a Home Page at MIT, a Personal Home Page and an educational blog called Moulton Lava. There is also a collection of essays and lighter pieces on Moulton's Utnebury Pages.

Objectives[edit]

My primary objective here is to achieve a respectable level of accuracy, excellence, and ethics in online media, especially when the subject at hand is an identifiable living person.

My secondary objective is to examine the efficacy of the process and the quality of the product achieved by any given policy, culture, or organizational architecture.

My tertiary objective is to identify and propose functional improvements to systems that are demonstrably falling short of best practices.