ISSS608 2016-17 T1 Assign2 WEI Jingxian

Motivation and Problems

Wikipedia is commonly used in our study and research, but there must be something disappointed users. From Wikipedia aspect, it is essential to know about the attitudes of users, especially university faculty. Because most of university faculty use Wikipedia as an academic reference or a teaching resource and they have more knowledge on their domain, they would be a valuable source of feedback about how well Wikipedia done and what need to be improved in academic area.Based on the survey, Wikipedia can know the attitudes of faculty and improve its performance.
Basically, we would like to know what are the best items and what are the worst items. In addition, we want to explore different preference of faculty with different profile. In more detail, the questions we want to know are listed:

• What is the demographic for these university faculty?
• At category level

1. Which category has the best / worst response?

2. What is the response of faculty in different domain or position?

• At question level

1. The overall result and what are the questions have the best / worst response?

2. What is the response of faculty in different domain or position?

3. Is there any interesting difference among different group of faculty?

Data Information

There is a survey on university faculty perceptions and practices of using Wikipedia as a teaching resource. There are totally 43 questions from 13 categories covered in the survey, and the response ranges from 1, which means strongly disagree to 5, which means strongly agree. The dataset can be download from following link. Wiki4HE Data Set
There are 10 variables recorded participants' profile, including age, gender, domain, PhD or not, years of university teaching experience, university, UOC position, other and other position, and Wikipedia registered user or not. All the information is presented by numbers. For survey questions, the following table shows the 13 categories and corresponding questions.

No.	Survey Categories	Corresponding Survey Questions
1	Perceived usefulness	PU1: The use of Wikipedia makes it easier for students to develop new skills  PU2: The use of Wikipedia improves students' learning  PU3: Wikipedia is useful for teaching
2	Perceived ease of use	PEU1: Wikipedia is user-friendly PEU2: It is easy to find in Wikipedia the information you seek  PEU3: It is easy to add or edit information in Wikipedia
3	Perceived enjoyment	ENJ1: The use of Wikipedia stimulates curiosity  ENJ2: The use of Wikipedia is entertaining
4	Quality	QU1: Articles in Wikipedia are reliable  QU2: Articles in Wikipedia are updated  QU3: Articles in Wikipedia are comprehensive  QU4: In my area of expertise, Wikipedia has a lower quality than other educational resources  QU5: I trust in the editing system of Wikipedia
5	Visibility	VIS1: Wikipedia improves visibility of students' work  VIS2: It is easy to have a record of the contributions made in Wikipedia
6	Social image	IM1: The use of Wikipedia is well considered among colleagues  IM2: In academia, sharing open educational resources is appreciated  IM3: My colleagues use Wikipedia
7	Sharing attitude	SA1: It is important to share academic content in open platforms  SA2: It is important to publish research results in other media than academic journals or books  SA3: It is important that students become familiar with online collaborative environments
8	Use behavior	USE1: I use Wikipedia to develop my teaching materials  USE2: I use Wikipedia as a platform to develop educational activities with students  USE3: I recommend my students to use Wikipedia  USE4: I recommend my colleagues to use Wikipedia  USE5: I agree my students use Wikipedia in my courses
9	Profile 2.0	PF1: I contribute to blogs  PF2: I actively participate in social networks  PF3: I publish academic content in open platforms
10	Job relevance	JR1: My university promotes the use of open collaborative environments in the Internet  JR2: My university considers the use of open collaborative environments in the Internet as a teaching merit
11	Behavioral intention	BI1: In the future I will recommend the use of Wikipedia to my colleagues and students  BI2: In the future I will use Wikipedia in my teaching activity
12	Incentives	INC1: To design educational activities using Wikipedia, it would be helpful: a best practices guide  INC2: To design educational activities using Wikipedia, it would be helpful: getting instruction from a colleague  INC3: To design educational activities using Wikipedia, it would be helpful: getting specific training  INC4: To design educational activities using Wikipedia, it would be helpful: greater institutional recognition
13	Experience	EXP1: I consult Wikipedia for issues related to my field of expertise  EXP2: I consult Wikipedia for other academic related issues EXP3: I consult Wikipedia for personal issues  EXP4: I contribute to Wikipedia (editions, revisions, articles improvement...)  EXP5: I use wikis to work with my students

Data Preparation

Replacement

First of all, it is necessary to check the missing data before we start analysing. There are many '?' excited in several columns, and JMP would treat these columns as character, which is inappropriate data type for answers. Thus, we need to replace all the '?' with null value and change the type of answers into numeric.

Calculation in JMP

In order to check the overall performance of each category, we calculate the averages of questions under each category, and use them to represent the responses of categories. The sample formula is shown in the screenshot below.

Calculation in Tableau

In the dataset, variables related to faculty profile are measured by number, so they cannot be checked directly. Therefore, for these variables, including domain, Uoc position and gender, we create new variables with actual meanings to replace the original variables.

Also, we create groups for age and years of university teaching experience (yearexp), so that we can present them clearer and more easily.

Groups	Criterion	Groups	Criterion
Age Groups	20+  30+ 40+ 50+ 60+	Yearexp Groups	<10  <20   <30 >=30

Format Conversion

Since we would like to discover both category level and question level, and it is inefficient if we mass up two level, we imported the modified dataset twice and used them for different conversion. There is a Pivot function in Tableau can help us to convert the dataset. In the he original dataset, one column represent one catogory or one question. After conversion, there would be only two columns showing the survey result. One is category / question and another is response.

Exploration

Demographic Information

Before we go into the survey result, we need to know about the participants in this survey.

In the original dataset, there are 6 unique values in domain variable. But the data dictionary only provide information for 1-5 exclude 6, also there are some missing value in this variables. 'Other' in domain includes 6 and null value, but only very little part of it is null value. It is obvious that many participant is from the other domain. In addition, the position for most of the participants is adjunct. Demographic information

Category Level

Category Level Dashboard

Question Level

Question Level Dashboard