ADAPTIVE ASSESSMENT AND KNOWLEDGE LEVEL ESTIMATION BY MEANS OF KNOWLEDGE MODEL AND BAYESIAN NETWORKS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

KARDAN AHMAD; KARDAN SAMAD

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Technology of Education Journal Year:2009 | Volume:3 | Issue:4 Page(s): 267-280

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

717

Download:

Cites:

Information Journal Paper

Title

ADAPTIVE ASSESSMENT AND KNOWLEDGE LEVEL ESTIMATION BY MEANS OF KNOWLEDGE MODEL AND BAYESIAN NETWORKS

Author(s)

KARDAN AHMAD | KARDAN SAMAD | Issue Writer Certificate

Keywords

LEARNINGQ3

E-LEARNINGQ3

BAYESIAN NETWORKQ3

Abstract

One of the important issues in a LEARNING management system is the assessment of the learner’s knowledge level. In basic methods of assessment, questions are simply ticked by the learners, and then automatically scored by machine. In this sort of examinations, two problems may be raised. Firstly, the learner may answer and mark the questions accidentally by guessing the answer without having enough knowledge about the related subject.Secondly, he/she may slip in answering the questions due to his/her inattention, although he/she has enough knowledge about the related subject. In this work, an improved method for knowledge level assessment is presented. In the proposed method LEARNING concepts are modeled based on the hierarchical construct of LEARNING objectives, and the learner’s knowledge model is used to estimate his/her knowledge level. This model is established on the basis of BAYESIAN NETWORKs, and considering the hierarchical construct of LEARNING objectives. To evaluate the proposed method, a set of questions by considering the LEARNING objectives was designed and ordered into different levels of complications. Then these questions were used for assessments. In this work also a new algorithm for updating the nodes in a BAYESIAN NETWORK is introduced. By means of this algorithm the effect of guessing and slipping answers in an assessment is undoubtedly reduced. Finally, by presenting a model for knowledge and use of the hierarchical construct of LEARNING objectives, an effective solution for three above mentioned problems of guessing answers, slipping answers, and the origin of wrong answers have been established.

Cites

No record.

References

No record.

Cite

APA: Copy

KARDAN, AHMAD, & KARDAN, SAMAD. (2009). ADAPTIVE ASSESSMENT AND KNOWLEDGE LEVEL ESTIMATION BY MEANS OF KNOWLEDGE MODEL AND BAYESIAN NETWORKS. JOURNAL OF TECHNOLOGY OF EDUCATION (JOURNAL OF TECHNOLOGY AND EDUCATION), 3(4), 267-280. SID. https://sid.ir/paper/155468/en

Vancouver: Copy

KARDAN AHMAD, KARDAN SAMAD. ADAPTIVE ASSESSMENT AND KNOWLEDGE LEVEL ESTIMATION BY MEANS OF KNOWLEDGE MODEL AND BAYESIAN NETWORKS. JOURNAL OF TECHNOLOGY OF EDUCATION (JOURNAL OF TECHNOLOGY AND EDUCATION)[Internet]. 2009;3(4):267-280. Available from: https://sid.ir/paper/155468/en

IEEE: Copy

AHMAD KARDAN, and SAMAD KARDAN, “ADAPTIVE ASSESSMENT AND KNOWLEDGE LEVEL ESTIMATION BY MEANS OF KNOWLEDGE MODEL AND BAYESIAN NETWORKS,” JOURNAL OF TECHNOLOGY OF EDUCATION (JOURNAL OF TECHNOLOGY AND EDUCATION), vol. 3, no. 4, pp. 267–280, 2009, [Online]. Available: https://sid.ir/paper/155468/en

Related Journal Papers