ANLY482 AY2016-17 T2 Group11: Project Overview

From Analytics Practicum
Revision as of 23:51, 21 February 2017 by Gareth.ng.2013 (talk | contribs)
Jump to navigation Jump to search

Return to ANLY482 AY2016-17 Home Page

T11 logo.png

T11 home.png T11 about us.png T11 overview 2.png T11 mgmt.png T11 findings.png T11 documentation.png


T11 background.jpg

The Ministry of Education (MOE) collects and analyses data from schools through Singapore to continually improve on their policies and practices in Education which they set for schools in Singapore. However, most of the data from MOE are not publicly available for research and analysis for people who are not working inside MOE. With that limitation, it is hard to gain insights about education in Singapore to make improvements or suggestions to the education system. An alternative for this is through the publicly available data collected by the Organisation for Economic Co-operation and Development (OECD) through the Programme for International Student Assessment (PISA) global education survey.

The OECD PISA global education survey is a triennial international survey which aims to evaluate education systems worldwide by testing the skills and knowledge of 15 year old students in math, reading, and science. The survey has become increasingly influential on politicians who see their countries and their policies being measured against these global school league tables.

Asian countries continue to dominate, with Singapore rated as best, replacing Shanghai, which is now part of a combined entry for China.

The 2015 PISA data was released last December 6 2016 and this will be used for the team’s analysis.


T11 motivation.jpg


T11 problem.jpg



T11 data source.jpg

OECD

PISA Global Education Survey Data

  • Student Questionnaire
  • Teacher Questionnaire
  • School Questionnaire
  • Cognitive data
  • Timing data
  • Codebook


T11 deliverables.jpg

Insights from Data Analysis

  • As mentioned above, the time-series analysis will be conducted and these insights will be compiled. The insights include a forecasting model of the number of anticipated volume of patients in the Emergency Department as the air quality factors rise to certain levels.