Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAE
Educational Data Mining (EDM) is the process of discovering information and relationships from educational data for better understanding of students’ performance, and characteristics of their education providers. Classification is a Data Mining (DM) technique used for prediction. On the other hand,...
محفوظ في:
| المؤلف الرئيسي: | |
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| منشور في: |
2018
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://bspace.buid.ac.ae/handle/1234/2006 |
| الوسوم: |
إضافة وسم
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| _version_ | 1862980610203582464 |
|---|---|
| author | SHWEDEH, FATEN |
| author_facet | SHWEDEH, FATEN |
| author_role | author |
| dc.creator.none.fl_str_mv | SHWEDEH, FATEN |
| dc.date.none.fl_str_mv | 2018-04 2022-05-19T07:10:13Z 2022-05-19T07:10:13Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 2015128 https://bspace.buid.ac.ae/handle/1234/2006 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | The British University in Dubai (BUiD) |
| dc.subject.none.fl_str_mv | prediction model Dubai private schools data Educational Data Mining (EDM) classification data mining international assessments educational data United Arab Emirates (UAE) Trends in Mathematics and Science Study (TIMSS) |
| dc.title.none.fl_str_mv | Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAE |
| dc.type.none.fl_str_mv | Dissertation |
| description | Educational Data Mining (EDM) is the process of discovering information and relationships from educational data for better understanding of students’ performance, and characteristics of their education providers. Classification is a Data Mining (DM) technique used for prediction. On the other hand, feature selection is the process of finding the best set of features that has the most impact on a specific target. This dissertation provides an extensive descriptive and predictive analysis on Grade-4 student performance in the Trends in Mathematics and Science Study (TIMSS) in the United Arab Emirates (UAE). The main purpose is to bridge the gap between EDM and International Assessments in the Arab world by applying EDM to predict Grade-4 student levels in TIMSS assessments in the UAE. We examined different feature selection methods and classification algorithms to find the best prediction model with the highest accuracy. The study in this dissertation was expanded to delve deeper into Dubai’s private schools data and discover the important features leading to improvements. In addition to building a prediction model to examine if a school will improve in the future TIMSS assessment cycles. As a result, it was found that the Tree-based feature selection method associated with Decision Tree (DT) classifier built the most accurate prediction models on most TIMSS datasets. The main key factors influencing students’ performance in science is discovered and presented. To the best of our knowledge, this study is the first scientific analysis implementing EDM in the field of international assessments in the UAE. In addition to being the first scientific study that considers all TIMSS questionnaires database in EDM task. |
| id | budr_d52277a63f8f41152fe0cd4954fd88ba |
| identifier_str_mv | 2015128 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/2006 |
| publishDate | 2018 |
| publisher.none.fl_str_mv | The British University in Dubai (BUiD) |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAESHWEDEH, FATENprediction modelDubai private schools dataEducational Data Mining (EDM)classificationdata mininginternational assessmentseducational dataUnited Arab Emirates (UAE)Trends in Mathematics and Science Study (TIMSS)Educational Data Mining (EDM) is the process of discovering information and relationships from educational data for better understanding of students’ performance, and characteristics of their education providers. Classification is a Data Mining (DM) technique used for prediction. On the other hand, feature selection is the process of finding the best set of features that has the most impact on a specific target. This dissertation provides an extensive descriptive and predictive analysis on Grade-4 student performance in the Trends in Mathematics and Science Study (TIMSS) in the United Arab Emirates (UAE). The main purpose is to bridge the gap between EDM and International Assessments in the Arab world by applying EDM to predict Grade-4 student levels in TIMSS assessments in the UAE. We examined different feature selection methods and classification algorithms to find the best prediction model with the highest accuracy. The study in this dissertation was expanded to delve deeper into Dubai’s private schools data and discover the important features leading to improvements. In addition to building a prediction model to examine if a school will improve in the future TIMSS assessment cycles. As a result, it was found that the Tree-based feature selection method associated with Decision Tree (DT) classifier built the most accurate prediction models on most TIMSS datasets. The main key factors influencing students’ performance in science is discovered and presented. To the best of our knowledge, this study is the first scientific analysis implementing EDM in the field of international assessments in the UAE. In addition to being the first scientific study that considers all TIMSS questionnaires database in EDM task.The British University in Dubai (BUiD)2022-05-19T07:10:13Z2022-05-19T07:10:13Z2018-04Dissertationapplication/pdf2015128https://bspace.buid.ac.ae/handle/1234/2006enoai:bspace.buid.ac.ae:1234/20062022-06-09T12:56:54Z |
| spellingShingle | Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAE SHWEDEH, FATEN prediction model Dubai private schools data Educational Data Mining (EDM) classification data mining international assessments educational data United Arab Emirates (UAE) Trends in Mathematics and Science Study (TIMSS) |
| title | Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAE |
| title_full | Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAE |
| title_fullStr | Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAE |
| title_full_unstemmed | Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAE |
| title_short | Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAE |
| title_sort | Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAE |
| topic | prediction model Dubai private schools data Educational Data Mining (EDM) classification data mining international assessments educational data United Arab Emirates (UAE) Trends in Mathematics and Science Study (TIMSS) |
| url | https://bspace.buid.ac.ae/handle/1234/2006 |