Personalizing Group Instruction Using Knowledge Space Theory and Clustering Techniques
A Master of Science thesis in Engineering Systems Management by Rim S. Zakaria entitled, "Personalizing Group Instruction Using Knowledge Space Theory and Clustering Techniques," submitted in May 2016. Thesis advisor is Dr. Imran A. Zualkernan. Soft and hard copy available.
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| Format: | doctoralThesis |
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2016
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| Online Access: | http://hdl.handle.net/11073/8336 |
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| _version_ | 1864513444623941632 |
|---|---|
| author | Zakaria, Rim S. |
| author_facet | Zakaria, Rim S. |
| author_role | author |
| dc.contributor.none.fl_str_mv | Zualkernan, Imran |
| dc.creator.none.fl_str_mv | Zakaria, Rim S. |
| dc.date.none.fl_str_mv | 2016-06-12T07:39:36Z 2016-06-12T07:39:36Z 2016-05 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2016.26 http://hdl.handle.net/11073/8336 |
| dc.language.none.fl_str_mv | en_US |
| dc.subject.none.fl_str_mv | Skills Management Talent Management Instructional Decision-Making KST Knowledge Space Theory (KST) HR Human Capital Management Optimizing-Decision Making Clustering Labor Training and Development Education Experimental methods Teaching Methodology Employees Training of Engineering firms |
| dc.title.none.fl_str_mv | Personalizing Group Instruction Using Knowledge Space Theory and Clustering Techniques |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science thesis in Engineering Systems Management by Rim S. Zakaria entitled, "Personalizing Group Instruction Using Knowledge Space Theory and Clustering Techniques," submitted in May 2016. Thesis advisor is Dr. Imran A. Zualkernan. Soft and hard copy available. |
| format | doctoralThesis |
| id | aus_71a2faa1b36ba607461500fecc119460 |
| identifier_str_mv | 35.232-2016.26 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/8336 |
| publishDate | 2016 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
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| spelling | Personalizing Group Instruction Using Knowledge Space Theory and Clustering TechniquesZakaria, Rim S.Skills ManagementTalent ManagementInstructional Decision-MakingKSTKnowledge Space Theory (KST)HRHuman Capital ManagementOptimizing-Decision MakingClusteringLabor Training and DevelopmentEducationExperimental methodsTeachingMethodologyEmployeesTraining ofEngineering firmsA Master of Science thesis in Engineering Systems Management by Rim S. Zakaria entitled, "Personalizing Group Instruction Using Knowledge Space Theory and Clustering Techniques," submitted in May 2016. Thesis advisor is Dr. Imran A. Zualkernan. Soft and hard copy available.In the competitive market today, availability of appropriate skills and competencies that are aligned with an organization's objectives and that contribute to the organization's long-term success and survival is not always guaranteed. Lack of such alignment leads to a Skill Gap. A Skill Gap is the gap between an organization's current capabilities and the skills the employees must have to achieve an organization's goals. This is especially true for engineering companies where the half-life of knowledge is relatively short. One cause of Skill Gap is inefficient and standardized training programs. Due to high costs, most training programs are not personalized, and deliver generic training to employees which may not be very effective in addressing individual or group Skill Gaps. This thesis explores personalizing and optimizing the content delivery decisions made by workforce trainers and instructors. The proposed approach is data driven and combines a set-theoretic framework called the Knowledge Space Theory (KST) with analytic techniques like cluster analysis. In specific, K-Means, DBSCAN and EM clustering techniques are used in conjunction with KST to cluster learners based on currently acquired skills, and on skills they are ready to acquire next. These clusters of learners can be used to design personalized training/instructional programs. Various internal measures like Compactness, Separation, Dunn Index, and Davies-Bouldin Index, and external measures like Purity, Entropy, Normalized Mutual Information, and Adjusted Random Index are used to compare alternative clustering techniques. Sensitivity analysis was also carried out. In general, K-Means seems to perform better than DBSCAN and EM for this type of data. However, there is no systematic preference between prior learning as opposed to affordance for future learning to cluster data.College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM)Zualkernan, Imran2016-06-12T07:39:36Z2016-06-12T07:39:36Z2016-05info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2016.26http://hdl.handle.net/11073/8336en_USoai:repository.aus.edu:11073/83362025-06-26T12:36:32Z |
| spellingShingle | Personalizing Group Instruction Using Knowledge Space Theory and Clustering Techniques Zakaria, Rim S. Skills Management Talent Management Instructional Decision-Making KST Knowledge Space Theory (KST) HR Human Capital Management Optimizing-Decision Making Clustering Labor Training and Development Education Experimental methods Teaching Methodology Employees Training of Engineering firms |
| status_str | publishedVersion |
| title | Personalizing Group Instruction Using Knowledge Space Theory and Clustering Techniques |
| title_full | Personalizing Group Instruction Using Knowledge Space Theory and Clustering Techniques |
| title_fullStr | Personalizing Group Instruction Using Knowledge Space Theory and Clustering Techniques |
| title_full_unstemmed | Personalizing Group Instruction Using Knowledge Space Theory and Clustering Techniques |
| title_short | Personalizing Group Instruction Using Knowledge Space Theory and Clustering Techniques |
| title_sort | Personalizing Group Instruction Using Knowledge Space Theory and Clustering Techniques |
| topic | Skills Management Talent Management Instructional Decision-Making KST Knowledge Space Theory (KST) HR Human Capital Management Optimizing-Decision Making Clustering Labor Training and Development Education Experimental methods Teaching Methodology Employees Training of Engineering firms |
| url | http://hdl.handle.net/11073/8336 |