Propose Data Mining System to Advance E-Learning Over Online Social Network (Facebook)
This research presents a proposal to advance e-learning over online social network, facebook, through analyzing the structure of this network and the behavior of their users. This proposalwill construct facebook group for Iraqi postgraduate higher education computer sciences students (IPHECSS), this...
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| Format: | Article |
| Language: | English |
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Unviversity of Technology- Iraq
2015-03-01
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| Series: | Engineering and Technology Journal |
| Subjects: | |
| Online Access: | https://etj.uotechnology.edu.iq/article_105298_ebeee7bbcbc8b6c238ab7e1a244dd60f.pdf |
| _version_ | 1797325778940067840 |
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| author | Soukaena Hassan Hashem Sarraa Mowaffaq Abood |
| author_facet | Soukaena Hassan Hashem Sarraa Mowaffaq Abood |
| author_sort | Soukaena Hassan Hashem |
| collection | DOAJ |
| description | This research presents a proposal to advance e-learning over online social network, facebook, through analyzing the structure of this network and the behavior of their users. This proposalwill construct facebook group for Iraqi postgraduate higher education computer sciences students (IPHECSS), this group consist of 300 users.The Proposal has four consequence steps to advance the e-learning over facebook, these steps are:1. Constructing a proposed student’s facebooks dataset for Iraq students' society called Iraqi postgraduate higher education students (IPHES), which contains self-defined characteristics of a student’s facebooks. 2. Applying customized Frequent Pattern (FP-growth) Association Rule (AR) technique to IPHES dataset as a ranker (since it calculates the frequency of attributes) and mining technique (since it extracts knowledge to predict decision making to support e-learning over facebook through analyzing student’s behavior). 3. Applying Traditional k-mean and proposed Modified k-mean techniques to IPHES dataset to advance the traditional KM in clustering the students to introduce the structure of network’s users; this helps in supporting e-learning over facebok through analyzing students broadcasting and activities. Modification on k-mean is done by injecting a preprocessing substep in traditional KM called attributes weighting depending on ranking results obtained by applying AR as a ranker and modifying Euclidian distance similarity measure to result vectors instead of single value. 4. Analyzing the results of both association rules and clustering using excel2007 and UCINET software. |
| first_indexed | 2024-03-08T06:14:23Z |
| format | Article |
| id | doaj.art-4cbe767a5f4345e396e91293e7d49e30 |
| institution | Directory Open Access Journal |
| issn | 1681-6900 2412-0758 |
| language | English |
| last_indexed | 2024-03-08T06:14:23Z |
| publishDate | 2015-03-01 |
| publisher | Unviversity of Technology- Iraq |
| record_format | Article |
| series | Engineering and Technology Journal |
| spelling | doaj.art-4cbe767a5f4345e396e91293e7d49e302024-02-04T17:28:12ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582015-03-01333B51252710.30684/etj.33.3B.11105298Propose Data Mining System to Advance E-Learning Over Online Social Network (Facebook)Soukaena Hassan HashemSarraa Mowaffaq AboodThis research presents a proposal to advance e-learning over online social network, facebook, through analyzing the structure of this network and the behavior of their users. This proposalwill construct facebook group for Iraqi postgraduate higher education computer sciences students (IPHECSS), this group consist of 300 users.The Proposal has four consequence steps to advance the e-learning over facebook, these steps are:1. Constructing a proposed student’s facebooks dataset for Iraq students' society called Iraqi postgraduate higher education students (IPHES), which contains self-defined characteristics of a student’s facebooks. 2. Applying customized Frequent Pattern (FP-growth) Association Rule (AR) technique to IPHES dataset as a ranker (since it calculates the frequency of attributes) and mining technique (since it extracts knowledge to predict decision making to support e-learning over facebook through analyzing student’s behavior). 3. Applying Traditional k-mean and proposed Modified k-mean techniques to IPHES dataset to advance the traditional KM in clustering the students to introduce the structure of network’s users; this helps in supporting e-learning over facebok through analyzing students broadcasting and activities. Modification on k-mean is done by injecting a preprocessing substep in traditional KM called attributes weighting depending on ranking results obtained by applying AR as a ranker and modifying Euclidian distance similarity measure to result vectors instead of single value. 4. Analyzing the results of both association rules and clustering using excel2007 and UCINET software.https://etj.uotechnology.edu.iq/article_105298_ebeee7bbcbc8b6c238ab7e1a244dd60f.pdffacebookassociation rulesmeanattributes ranking |
| spellingShingle | Soukaena Hassan Hashem Sarraa Mowaffaq Abood Propose Data Mining System to Advance E-Learning Over Online Social Network (Facebook) Engineering and Technology Journal association rules mean attributes ranking |
| title | Propose Data Mining System to Advance E-Learning Over Online Social Network (Facebook) |
| title_full | Propose Data Mining System to Advance E-Learning Over Online Social Network (Facebook) |
| title_fullStr | Propose Data Mining System to Advance E-Learning Over Online Social Network (Facebook) |
| title_full_unstemmed | Propose Data Mining System to Advance E-Learning Over Online Social Network (Facebook) |
| title_short | Propose Data Mining System to Advance E-Learning Over Online Social Network (Facebook) |
| title_sort | propose data mining system to advance e learning over online social network facebook |
| topic | facebook association rules mean attributes ranking |
| url | https://etj.uotechnology.edu.iq/article_105298_ebeee7bbcbc8b6c238ab7e1a244dd60f.pdf |
| work_keys_str_mv | AT soukaenahassanhashem proposedataminingsystemtoadvanceelearningoveronlinesocialnetworkfacebook AT sarraamowaffaqabood proposedataminingsystemtoadvanceelearningoveronlinesocialnetworkfacebook |