ITS, The End of the World as We Know It: Transitioning AIED into a Service-Oriented Ecosystem (bibtex)
by Benjamin D. Nye
Abstract:
Advanced learning technologies are reaching a new phase of their evolution where they are finally entering mainstream educational contexts, with persistent user bases. However, as AIED scales, it will need to follow recent trends in service-oriented and ubiquitous computing: breaking AIED platforms into distinct services that can be composed for different platforms (web, mobile, etc.) and distributed across multiple systems. This will represent a move from learning platforms to an ecosystem of interacting learning tools. Such tools will enable new opportunities for both user-adaptation and experimentation. Traditional macro-adaptation (problem selection) and step-based adaptation (hints and feedback) will be extended by meta-adaptation (adaptive system selection) and micro-adaptation (event-level optimization). The existence of persistent and widely-used systems will also support new paradigms for experimentation in education, allowing researchers to understand interactions and boundary conditions for learning principles. New central research questions for the field will also need to be answered due to these changes in the AIED landscape.
Reference:
ITS, The End of the World as We Know It: Transitioning AIED into a Service-Oriented Ecosystem (Benjamin D. Nye), In International Journal of Artificial Intelligence in Education, volume 26, 2016.
Bibtex Entry:
@article{nye_its_2016,
	title = {{ITS}, {The} {End} of the {World} as {We} {Know} {It}: {Transitioning} {AIED} into a {Service}-{Oriented} {Ecosystem}},
	volume = {26},
	issn = {1560-4292, 1560-4306},
	url = {http://link.springer.com/10.1007/s40593-016-0098-8},
	doi = {10.1007/s40593-016-0098-8},
	abstract = {Advanced learning technologies are reaching a new phase of their evolution where they are finally entering mainstream educational contexts, with persistent user bases. However, as AIED scales, it will need to follow recent trends in service-oriented and ubiquitous computing: breaking AIED platforms into distinct services that can be composed for different platforms (web, mobile, etc.) and distributed across multiple systems. This will represent a move from learning platforms to an ecosystem of interacting learning tools. Such tools will enable new opportunities for both user-adaptation and experimentation. Traditional macro-adaptation (problem selection) and step-based adaptation (hints and feedback) will be extended by meta-adaptation (adaptive system selection) and micro-adaptation (event-level optimization). The existence of persistent and widely-used systems will also support new paradigms for experimentation in education, allowing researchers to understand interactions and boundary conditions for learning principles. New central research questions for the field will also need to be answered due to these changes in the AIED landscape.},
	number = {2},
	journal = {International Journal of Artificial Intelligence in Education},
	author = {Nye, Benjamin D.},
	month = jun,
	year = {2016},
	keywords = {Learning Sciences, UARC},
	pages = {756--770}
}
Powered by bibtexbrowser