ISSS608 2016-17 T1 Assign2 XU Qiuhui

Data Sources

Dataset from UCI, Survey of faculty members from two Spanish universities on teaching uses of Wikipedia

Source: E. Aibar, J. Lladós, A. Meseguer, J. Minguillón (jminguillona[at]uoc[dot]edu), M. Lerga. Universitat Oberta de Catalunya, Barcelona, Spain.

Theme of Interest and Motivation

This Analysis aims to find out overall impressions of different user segments on Wikipedia and their use behavior according to high dimensional survey question answers. Then propose recommendations for Wikipedia's future development. In this analysis, we'll mainly answer the following questions：

1. Relationships between user impressions and user behaviors.
2. Relationships user behaviors and external environments.

Data Preparation

Transfer Data Type

Variables	Original Data Type	Transferred Data Type	Reason
Gender	Numeric	Categorical	According to dataset dictionary, gender is meaningless while using numeric value to do analysis.
PhD	Numeric	Categorical	According to dataset dictionary, PhD is meaningless while using numeric value to do analysis.
University	Numeric	Categorical	According to dataset dictionary, University is meaningless while using numeric value to do analysis.
YearsExp	Categorical	Numeric	Years of experience should be continuous data, so that we can firstly bin them into several groups, then use groups to classify them.

Bin Numeric Data

Variables	Original	Transferred Variables	Formula
Age		Age(bin)	If(:AGE <= 30,"20~30",If(:AGE <= 40,"30~40",If(:AGE <= 50,"40~50",If(:AGE <= 60,"50~60","60~70"))))
YearsExp		YearsExp(bin)	If( :YEARSEXP <= 10,"0~10",If( :YEARSEXP <= 20,"10~20",If( :YEARSEXP <= 30,"20~30","more than 30")))

Group Categorical Data

Transform all survey question answers with 1-5 scores to “High, Mid, Low” degree.

Scores	Degree
1	Low
2	Low
3	Mid
4	High
5	High

Inset New Column

Insert a new column, User ID to uniquely represent one user in the dataset.

Variable	Data Type	Example	Description
UserID	Categorical	“U1”, “U2” …” U913”	Each User ID uniquely identifies a user in the dataset.

Visualization

Parallel Set

Analysis

Users Overview

1. Among people who respond to the survey, number of people with and without PhD degree are comparable, while those who don't hold a PhD degree are relatively higher.
2. As years of experience increase, number of respondents decrease.
3. Almost half of respondents come from unknown domain, others mainly come from arts & humanities, engineering and law.
4. Among all respondents, number of Adjunct are dominant.

User impressions and behaviors

Overall Impressions

We selected 4 dimensions, usefulness, ease of use, enjoyment, and quality, to see score distribution and average score to get to know people's overall impression on Wikipedia. As we can see, generally, average scores are higher than 3, which is median value. So we consider, overall, people has a positive impression on Wiki. Then we Drill down to each detailed dimension. For quality, it's the lowest on average, and also has fewer responds as score increases. So among usefulness, quality, ease of use, and enjoyment, we consider quality is Wikipedia's weakest feature.

Relationships

There’s a large proportion user who don’t use to teach have very good impression on wiki, they’re potential users.

Information on wiki are considered updated and relatively reliable, but still considered with lower quality than other educational resources.

Even though wiki is considered with lower quality, users still trust in it's editing system.

User behaviors and external environments

External environments tend to have huge influences on behavioral intention. Form the parallel set we can clearly get that almost all people whose colleagues don’t use wiki and are not consider well on wiki are not intended to use wiki in teaching in the future.

Tools Utilized

1. High-D - For initial data exploration and analysis
2. JMP 12, MS Excel – For data preparation
3. d3.js, Tableau, Treemap - For data visualization