Intelligent Market Based Learner Modeling

Ashoori, Maryam; Miao, Chunyan; Goh, Angela; Wang, Qiong

doi:10.1007/11801603_13

Cited by 2 publications

(5 citation statements)

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“…Among all the IMS LIP items, the related items to preferences are "Activity", "Identification" and "Interests" while the system is basically built to serve as a platform for students as supplement learning environment rather than organization selection of new employees [4]. Students are modeled based on: where different variations of agent's scenarios are mapped directly based on the values of "Interests".…”

Section: Learner Modelingmentioning

confidence: 99%

“…Thereafter, next time when the students ask mentor where to go to investigate, the mentor will suggest the student those areas and those effective agents with a higher probability. The credit assignment algorithm is the one used by [4,7] where student is considered as a task through the system and a particular stock amount of the task would be assigned to the Guides. Whenever a Guide faced with the student and talk to him, the amounts of the stock value of that particular task would be updated by Mentor for the agent.…”

Section: Evaluating the Efficiency Of Guidesmentioning

confidence: 99%

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Socializing Pedagogical Agents for Personalization in Virtual Learning Environments

Ashoori¹,

Miao²,

Cai³

2007

2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops

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Personalization in virtual learning environments is the system ability to provide individualization and a set of personalized services such as personalized content management, learner model, or adaptive instant interaction. The intelligent agent technology has potential regarding the creation of such personalized, adaptive and interactive elearning applications. However, most of the available solutions have so far focused on porting existing courses with traditional teaching methods onto the virtual environments, making them available in an attractive animated interface without any fine-tuning and adaptation to the learner needs. This paper proposes a novel market-inspired collaboration model where the agents are self-interested autonomic elements collaborate to achieve a comprehensive learner model. Mentor agent makes decisions on top of a Dempster-Shafer belief accumulation to help student whenever she believes student has lost the clues and needs help. Proposed architecture is validated by applying on a sample agent augmented virtual environment designed to engage and motivate students at the lower secondary level in Singapore. Extensive experiments illustrate the effectiveness of the proposed interaction model where students have found the mentor agent as believable as a virtual teacher.

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Section: Learner Modelingmentioning

confidence: 99%

Section: Evaluating the Efficiency Of Guidesmentioning

confidence: 99%

Socializing Pedagogical Agents for Personalization in Virtual Learning Environments

Ashoori¹,

Miao²,

Cai³

2007

2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops

Self Cite

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“…Agent runs a credit assignment algorithm over the system. The credit assignment algorithm is the one discussed by [4,5,8]. It is like considering some kind of rewards for those agents who communicate effectively with students.…”

Section: Outline Of the Envisaged Approachmentioning

confidence: 99%

“…One of the most popular standards for modeling learner profile is IMS Learner Modified IMS LIP [4] has been used as a standard for defining the required parameters for user modeling in this research.…”

Section: Learner Modelmentioning

confidence: 99%

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Intelligent pedagogical agents for personalizing learning objects in educational environments

Ashoori¹

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India. Or, the funny moments I had with Nannan especially when she couldn't control herself laughing at my terrible Chinese accent. I'd like to remember Shabnam, Pari, Narges, Shayan, and all my precious Iranian friends with the brightest moments they gifted to me over last two years. Thanks to my great friends Ying, Kia Yan, Long Dong, Jang Tao, Ghim Eng, and Angela, who made our ERLab a lovely environment plenty of friendship and happiness. Last but not least, I give my special thanks to my lovely parents for their kind supports. I don't have words to acknowledge Elham and Alireza for their endless support and boundless wisdom. They have been my strength, my criticism, my motivator and also my best friends.

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Intelligent Market Based Learner Modeling

Cited by 2 publications

References 0 publications

Socializing Pedagogical Agents for Personalization in Virtual Learning Environments

Socializing Pedagogical Agents for Personalization in Virtual Learning Environments

Intelligent pedagogical agents for personalizing learning objects in educational environments

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