AI

Watson ties top players in Jeopardy — “Disrupting Class” phenomenon continues

On 02/15/2011, in Artificial Intelligence/Agents/LLMs/and Related, assistive technologies, disruption, education technology, emerging technologies, game-changing environment, human-computer interaction (HCI), innovation, learner profiles, learning agents, learning ecosystem, personalized/customized learning, the downsides of how people use tech, by Daniel Christian

Computer ties human as they square off on ‘Jeopardy!’ — from CNN.com

.From DSC:
To be clear, I celebrate what the LORD has created and given to us in our amazingly-complex minds! I do not subscribe to the idea that robots are better than humans or that technologies are to be glorified and that technologies will save the world — not at all. (In fact, I have some concerns about the havoc that could easily occur if certain technologies wound up in the wrong hands — with those who have no fear of the LORD and who have massive amounts of pride…with hearts of stone.)

Getting back to my point…
The phenomenon that Christensen, Horn, and Johnson describe in Disrupting Class continues to play out in higher education/K-12. The innovations are mainly happening outside the face-to-face T&L environments.

Also see:

IBM’s Watson is very 20th century — from Judy Breck
How IBM created Watson to beat jeopardy — from digitizd
This Computer Could Defeat You at ‘Jeopardy!’ Q: What is Watson? — from PBS
IBM Watson Videos –nextbigfuture.com

Tagged with: AI • assistive technologies • customized learning • disruption • educational technology • emerging technologies • game-changing environment • human-computer interaction (HCI) • information overload • innovation • learner profiles • learning ecosystem • personalized learning • the downsides of technology

Interesting post from Jon Bischke: Learning Graph + Reputation Graph = Massive Disruption in Higher Ed?

On 02/12/2011, in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, assessment, career development, cloud-based computing / apps / other cloud-related, competency-based learning/education, disruption, higher education, K-12 related, learner profiles, learning agents, learning ecosystem, workplace, by Daniel Christian

Learning Graph + Reputation Graph = Massive Disruption in Higher Ed? — from Jon Bischke

From DSC:
Jon gets at something similar to my thoughts on cloud-based learner profiles. Jon links to — and also see — Kirsten Winkler’s posting from August 2010:

Bill Gates and Marco Masoni on Online Education – Why We Need a Learning Graph

Also see:

Reputation Graph: One of the Web’s largest opportunities — from Jon Bischke

Tagged with: 1:1 • 21st century • 24x7x365 • AI • assessment • career development • cloud-based computing / apps / other cloud-related • competency-based learning/education • disruption • K-12 • learner profiles • Learning Agents • workplace

Elementary, my dear Watson: Jeopardy computer offers insight into human cognition

On 02/09/2011, in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, Canada, future, future of higher education, game-changing environment, learning agents, learning ecosystem, personalized/customized learning, vendors, by Daniel Christian

Elementary, my dear Watson: Jeopardy computer offers insight into human cognition — from Sentient Developments by George Dvorsky

Also see:

The DeepQA Project — from IBM; the overarching principles in DeepQA are:

Massive parallelism:
Exploit massive parallelism in the consideration of multiple interpretations and hypotheses.

Many experts:
Facilitate the integration, application and con-textual evaluation of a wide range of loosely coupled probabilistic question and content analytics.

Pervasive confidence estimation:
No single component commits to an answer; all components produce features and associated confidences, scoring different question and contentinterpretations. An underlying confidence processing substratelearns how to stack and combine the scores.

Integrate shallow and deep knowledge:
Balance the use of strict semantics and shallow semantics, leveraging many loosely formed ontologies.

Tagged with: 1:1 • AI • Canada • cognition • cognitive psychology / computing • customized learning • future • future of higher education • game-changing environment • Learning Agents • learning ecosystem • personalized learning • vendors

This is the type of learning engine that could be applied in education for personalized, customized learning (on demand)

On 02/04/2011, in 21st century, analytics, Artificial Intelligence/Agents/LLMs/and Related, emerging technologies, IT in HE, learner profiles, learning agents, personalized/customized learning, trends, by Daniel Christian

Along these lines, check out:
‘Jeopardy’ champs take on IBM’s Watson

“This is huge; this isn’t just about answering questions,” says computational linguistics expert George Luger of the University of New Mexico in Albuquerque, who was not part of IBM’s team. “Everything before this was just a run-up to computers as true personal assistants.”

Tagged with: 1:1 • 21st century • AI • analytics • customized learning • emerging technologies • IT • learner profiles • Learning Agents • personalized learning • trends

Video/discussion about “The Singularity”

On 12/27/2010, in Artificial Intelligence/Agents/LLMs/and Related, ethics, future, human-computer interaction (HCI), science, by Daniel Christian

From DSC:
Again, some of this is fascinating to me, while other portions of this is very unnerving and controversial to me.

Tagged with: AI • ethics • future • human-computer interaction (HCI) • humanities • technologies

So this is school? …and another item from the 2011 Futurist Conference

On 12/17/2010, in Artificial Intelligence/Agents/LLMs/and Related, blended learning, career development, change, educational games / serious games, future, hybrid learning, innovation, mobile learning, mobile technologies, online learning, virtual K-12 schools, workplace, by Daniel Christian

Futurist Conference 2011 > Learning and Education
So This is School?
Brian Collins, Florida Virtual School, Orlando, Florida

As educational opportunities move from the traditional classroom to cyberspace and beyond, the very paradigm of how students are engaged is being redefined. Mobile devices? Location based technologies? Gaming? Holograms? Artificial intelligence? All of these things, and more, are converging to provide unparalled experiences for today’s learners. The most innovative schools are exploring bold steps to redefine where and how educational content is being delivered. This, combined with an understanding of where technology and society is heading, with a little imagination thrown in, will provide profound changes in the educational landscape and surely captivate students as we move into the future!

Also see:

Future SCANN: A Network to Help Students Envision and Co-Design Careers of the Future

Tagged with: AI • blended learning • career development • change • emerging technologies • future • games • hybrid learning • innovation • K-12 • Mobile learning • mobile technology • online learning • virtual schools • workplace

Tagged with: AI

Tagged with: AI • biology

Agents and multi-agents — most likely a part of our future learning ecosystems

On 10/30/2010, in Artificial Intelligence/Agents/LLMs/and Related, computer science, Daniel S. Christian, learning agents, learning ecosystem, programming, by Daniel Christian

Below graphics from:
Introduction to MultiAgent Systems

From DSC:

Tagged with: AI • computer science • Daniel S. Christian • Learning Agents • learning ecosystem • programming

Check out the topics in this symposium — talk about a game-changing environment!

On 10/19/2010, in 21st century, Artificial Intelligence/Agents/LLMs/and Related, future, game-changing environment, human-computer interaction (HCI), innovation, intelligent tutoring, internet, learning agents, learning ecosystem, multimedia, pace of change, programming, robotics, simulations, telecommunications, trends, web-based collaboration, by Daniel Christian

Symposium on Progress in Information and Communication Technology (SPICT’10)

Conference date: 12-13 Dec,2010
Conference venue: The Royale Bintang, Kuala Lumpur
Conference country: Malaysia

SPICT’10 aims to bring together scientists, industry practitioners and students to exchange the latest fundamental advances and trends, and identify emerging research topics in the field of information and communication technology.

Activities:

* Agent & Multi-agent Systems
* Antennas & Propagation
* Artificial Intelligence
* Bioinformatics & Scientific Computing
* Business Intelligence
* Communication Systems and Networks
* Complex Systems: Modeling and Simulation
* Computer Vision
* Database and Application
* Geographical Information Systems
* Grid and Utility Computing
* Image Processing
* Information indexing & retrieval
* Information Systems
* Intelligent Systems
* Internet Technology
* Knowledge Management
* Mobile Communication Services
* Multimedia Technology and Systems
* Natural Language Processing
* Network Management and services
* Ontology and Web Semantic
* Optical Communications and Networks
* Parallel and Distributed Computing
* Pattern Recognition
* Pervasive Computing
* Real-Time and Embedded Systems
* Remote Sensing
* Robotic Technologies
* Security and Cryptography
* Sensor Networks
* Service Computing
* Signal Processing
* Software Engineering
* Strategic Information Systems

Tagged with: 1:1 • 21st century • AI • future • game-changing environment • human-computer interaction (HCI) • innovation • internet • Learning Agents • learning ecosystem • multimedia • pace of change • robotics • semantic web • telecommunications • trends • web-based collaboration

Some reflections re: web/cloud-based learner profiles

On 10/16/2010, in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, assessment, cloud-based computing / apps / other cloud-related, CMS/LMS, colleges, community colleges, competency-based learning/education, Daniel S. Christian, data related items, digital learning, education technology, elearning, emerging technologies, human-computer interaction (HCI), innovation, instructional design, intelligent tutoring, internet, K-12 related, learning agents, learning ecosystem, learning preferences, lifelong learning, mobile learning, multimedia, online learning, online media, online tutoring, personalized/customized learning, training / L&D, ubiquitous learning, universities, workplace, by Daniel Christian

Reflections on web-based learner profiles -- by Daniel S. Christian

Tagged with: 1:1 • 21st century • 24x7x365 • AI • assessment • Blackboard • cloud-based computing / apps / other cloud-related • CMS/LMS • colleges • community college • competency-based learning/education • corporate training / universities • customized learning • Daniel S. Christian • data mining • digital learning • e-learning • educational technology • emerging technologies • evaluation • HCI • innovation • instructional design • intelligent tutoring • internet • K-12 • Learning Agents • learning ecosystem • learning preferences • Lifelong learning • Mobile learning • Moodle • personalized learning • scaffolding • training • ubiquitous learning • universities

Traditional instructional methods versus intelligent tutoring systems

On 10/16/2010, in intelligent tutoring, personalized/customized learning, traditional / tradition, by Daniel Christian

Traditional instructional methods versus intelligent tutoring systems — from4u-all.com

From DSC:
I’m not crazy about the VS. part here…this post isn’t mean to stir competitive juices or put some folks out there on the defensive. Rather, I thought it had some interesting, understandable things to say about intelligent tutoring systems and what benefits they might provide.

A potential solution to this problem is the use of novel software known as “Intelligent Tutoring Systems” (ITS), with built-in artificial intelligence. These systems, which adapt themselves to the current knowledge stage of the learner and support different learning strategies on an individual basis, could be integrated with the Web for effective training and tutoring.

Intelligent tutoring systems (ITSs) are software programs that give support to the learning activity. These systems can be used in the conventional educational process, distant learning courses as well corporate training, either under the form of CDROMs or as applications that deliver knowledge over the Internet. They present new ways for education, which can change the role of the human tutor or teacher, and enhance it.

They present educational materials in a flexible and personalized way that is similar to one-to-one tutoring. In particular, ITSs have the ability to provide learners with tailored instructions and feedback. The basic underlying idea of ITSs is to realise that each student is unique.

They use simulations and other highly interactive learning environments that require people to apply their knowledge and skills. These active, situated learning environments help them retain and apply knowledge and skills more effectively in operational settings.

An intelligent tutoring system personalizes the instruction based on the background and the progress of each individual student. In this way, the learner is able to receive immediate feedback on his performance. Today, prototype and operational ITS systems provide practice-based instruction to support corporate training, high school and college education, military training etc.

The goal of intelligent tutoring systems is to provide the benefits of one-on-one instruction automatically and cost effectively. Intelligent tutoring systems enable participants to practice their skills by carrying out tasks within highly interactive learning environments…

Tagged with: 1:1 • AI • customized learning • intelligent tutoring • personalized learning • traditional

Thoughts on lecture capture, personalization, web-based learner profiles

On 10/11/2010, in 21st century, Artificial Intelligence/Agents/LLMs/and Related, competency-based learning/education, education technology, emerging technologies, game-changing environment, innovation, instructional design, intelligent tutoring, learner profiles, learning agents, online learning, online tutoring, by Daniel Christian

From DSC:
I was reading a white paper from Tegrity today (see below graphics). It mentioned that the next frontier for lecture capture technologies is focused on developing more personalized learning experiences.

— A brief aside from DSC:
Reminds me of some of the functionality found in Livescribe’s echo smartpen.

The ability to integrate lecture capture platforms with Learning Management Systems (LMS’s) can help to automate the authentication and authorization needed to ensure learners get to review what they are allowed to review. Integration hooks provided by lecture capture and LMS vendors are viable as methods of ensuring a baseline approach to secure access. Yet most lecture capture systems do not know who the viewer is (as the LMS does the authentication and authorization); they only know that the stream is permitted to play and that students of the course are watching.

This sets the stage for the next transformation of lecture capture solutions – into platforms that can understand not just who their users are, but also what those users need to do and how their experience can be personalized and enhanced.

The coming shift will bring creation of custom learning environments that cater to the individual student by offering personal context-sensitivity, the ability to draw on the knowledge of peers and instructors, and the ability to better manage and monitor each individual learner’s behaviors and customize their experience to their individual needs. Among the major effects of this shift:

Democratization of the content creation process as learners themselves contribute to or otherwise use lecture capture tools to learn from or teach others

Faster learning by enabling learners to access information more quickly through bookmarks – and placing efficiencies within the platform to streamline teaching and learning

Changing impact on educators, who can rely on lecture capture feedback loops based on features like bookmarking to enable them to adjust content and teaching styles to suit learner needs

Use of presence and the fact that a system can know a learner to automate and make more efficient the act of finding peers or instructors for further learning interactions

Greater ability to deliver content and offer customized features via mobile devices

This white paper focuses on the evolution of lecture capture as a tool for creating a coherent environment for learner-centered instruction, showing the possibilities for improved efficiencies and better learning outcomes.

From DSC:
The integration of a lecture capture system w/ an LMS got me to thinking…what if each person in the world had a constantly-updated, adaptive, web-based learner profile that detailed their current age, current and past places of residence, language(s), hobbies, interests, courses taken, major(s), minor(s), last grade completed, which RSS feeds they subscribe to, which sources of educational content they prefer, etc. Given permission by the student, a vendor’s tool could then query the database and look for particular fields…plugging that content into their own application for greater context and engagement.

So if a 3rd grader in India loved horses, the math problems could utilize that information to make the problems more engaging to that person.

Hmmm…along these lines, I think I’ll set up some Google alerts to include:

Multi-agent systems
Adaptive learning systems
Artificial intelligence education
Distributed e-learning systems
Semantic web education
Learning agents
Intelligent tutoring
Online tutoring

The next few years should be veeeerrrryyy interesting. Fasten your seatbelts!

Tagged with: 1:1 • AI • competency-based learning/education • customized learning • educational technology • game-changing environment • innovation • instructional design • learner profiles • Learning Agents • lecture recording • online learning • personalized learning

References from “Developing Adaptive and Personalized Distributed Learning Systems…”

On 09/14/2010, in Artificial Intelligence/Agents/LLMs/and Related, learning agents, personalized/customized learning, by Daniel Christian

From DSC:
Though I can’t re-publish this article (it costs $19), I do hope it’s ok that I share the abstract and the references of the article (if not Birol or Mustafa, please advise). They are definitely onto something here, and we all need to continue to keep our eyes on such keywords as:

learning agents
multi agent systems
1:1
personalized/customized learning
artificial intelligence (AI)
the semantic web
…as these items represent where technology can be powerfully leveraged in the future.

Developing Adaptive and Personalized Distributed Learning Systems with Semantic Web Supported Multi Agent Technology

Birol Ciloglugil
Dept. of Comp. Engineering
Ege UniversityIzmir, Turkey

Mustafa Murat Inceoglu
Dept. of Comp. Education and Instructional Technology
Ege University
Izmir, Turkey

Abstract—The early e-learning systems were developed with the one-size-fits-all approach where the differences among the learners were disregarded and the same learning materials were supplied to each user. Nowadays, with the technological advances and the new trends in system design, the newly-developed systems take into consideration the needs, the preferences and the learning styles of the learners. As a result of this, more personalized e-learning systems have been developed. This thesis will investigate how possible technologies such as multi-agent systems and semantic web can be used to achieve more adaptive and more personalized distributed e-learning environments.

Keywords-adaptive systems; e-learning, multi agent systems; personalized e-learning systems; semantic web

REFERENCES

[1] H. Wang, P. Holt, “The design of an integrated course delivery system for Web-based distance education”, Proceedings of the IASTED International Conference on Computers and Advanced Technology in Education (CATE 2002), 2002, pp. 122-126.

[2] F. O. Lin, Designing Distributed Learning Environments with Intelligent Software Agents, Information Science Publishing, 2004.

[3] B. Ciloglugil, M. M. Inceoglu, “Exploring the state of the art in adaptive distributed learning environments”, LNCS, vol. 6017, 2010, pp. 556-569.

[4] I. S. B. Gago, V. M. B. Werneck, R. M. Costa, “Modeling an Educational Multi-Agent System in MaSE”, LNCS, vol. 5820, 2009, pp. 335-346.

[5] S. Garruzzo, D. Rosaci, G. M. L. Sarne, “ISABEL: A multi agent elearning system that supports multiple devices”, IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2007, pp.85-88.

[6] T. Berners-Lee, J. Hendler, O. Lassila, The semantic web, Scientific American, 2001, pp. 34-43.

[7] A. Gladun, J. Rogushina, F. Garc?a-Sanchez, R. Martínez-Béjar, J. T. Fernández-Breis, “An application of intelligent techniques and semantic web technologies in e-learning environments”, Expert Syst. Appl., vol. 36, 2, 2009, pp. 1922-1931.

[8] M. Gaeta, F. Orciuoli, P. Ritrovato, “Advanced ontology management system for personalised e-learning”, Know.-Based Syst., vol. 22, 4, 2009, pp. 292-301.

[9] B. G. Aslan, M. M. Inceoglu, “Machine learning based learner modeling for adaptive Web-based learning”, LNCS, vol. 4705, 2007, pp. 1133-1145.

[10] W. S. Lo, I. C. Chung, H. J. Hsu, “Using ontological engineering for computer education on online e-Learning community system”, International Conference on Education Technology and Computer, Singapore, 2009, pp. 167-170.

[11] L. Romero, H. P. Leone, “An ontology on learning assessment domain”, New Perspectives on Systems and Information Technology, vol. 2, 2007, pp. 137-148.

[12] A. Canales-Cruz, V. G. Sanchez-Arias, F. Cervantes-Perez, R. Peredo-Valderrama, “Multi-agent system for the making of intelligence and interactive decisions within the learner’s learning process in a web-based education environment”, Journal of Applied Research and Technology, vol. 7, 3, 2009, pp. 310-322.

Tagged with: 1:1 • AI • customized learning • Learning Agents • personalized learning • semantic web

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