ISSS608 2016-17 T1 Assign2 Meenakshi
Contents
Abstract
The new open collaborative environment and open education content has had a great impact on student learning and teaching practices
in the universities across globe. Wikipedia stands out to be the primary technology platform that is enabling learning and knowledge sharing. In this study we are trying to understand the teaching use of wikipedia from a dataSurvey of faculty members from two Spanish universities.
Problem and motivation
The objective is to analyse the multivariate survey data using appropriate visualisation tools and techniques.-
The results would enable us understand the main attributes that influence the teaching uses of wikipedia.
This will give us insights on how the open learning platform is being received and how are the practices going to be in the academic
world in the future.
Tools Used
Tableau version 10.0
JMP
Approaches
Following the Visual Analytics Application Design Process, we approach the problem is 3 steps
- Identify theme of interest:
The focus is on Visualizing factors that influence the use of Wikipedia as a platform for teaching and learning in the academic world.
What do faculty members consider important in order to use and promote wikipedia.
- Define questions for investigation:
The wiki4HE survey data, contains information about the faculty members basic profile. Survey questions are defined considering these factors:
Perceived Usefulness and Ease of Use
Perceived Enjoyment
Quality
Visibility
SocialImage
Sharing Attitude
Use Behavior
Profile 2.0
Job relevance
Use Behavior
Behavioral Intention
Incentives
Experience
The associations to be studied are
- Who are the poeple taking the survey? Some demogrpahics on the survey participants
- Do faculty members from various departments differ in their opinion on the perceived usefulness and Quality.
- Is Quality the key factor considered by most faculty members. Does it influence the perceived usefulness and ease of use
- Is the Use behavior and Behavioral intention defined by quality
- Does the faculty Social profile influence the sharing attitude
- How important is the Job relevance for the use of wikipedia. Does it also affect the sharing attitude
- Does the Social Image- colleague's perception, influence the perceived usefulness and behavior intention
- How does visibility affect the use behavior and behavioral intention
As we progress with data explorations, some questions may have to be re defined.
- Analysis:
Data Preparation and cleaning
- The JMP tool is used for this task. The wiki4HE csv file is loaded in JMP to check the data types and get a preview/summary of the data.
- The data types for some of the variables needs to be redefined. Such as - Changing Gender and Phd to categorical data.
- The raw data has some missing values and '?' mark fields in the table. Referring to the data dictionary, appropriate values are recoded using JMP. Through this process we can make sense of some of the '?' fields. Recoding also aids interpreting data during analysis.The steps are summarized in table below-
- Add a ID column to represent individual survey respondenst with a unique ID wiki4HE_Clean xls file.
- Add a category column to the individual questions in a new sheet in the the wiki4HE_Clean xls file.
- The data table in JMP is now ready to check for missing values. Check the distribution of all parameters to understand more.
It is seen that the response is missing for some of the questions, however we consider the %Total of responses for each scale during our analysis. Hence these missing values can be ignored in the table.
- The cleaned data from JMP is saved as csv/xls file types to be ready to use for the visual analysis.
- Visual analysis - Import the cleaned data to tableau to analyse survey data.
The Qns category sheet and the wiki4HE_Clean xls are joined by QuestionID and imported to Tableau. We need the Question Ids to be rows instead of columns to visualise the Likert scale data. The Pivot function in tableau achieves this. </gallery>
Results
The results of the study are shown in the infographics below. The observations will enable stake holders to see the past and current trends for housing supply and prices. Business decisions can be influenced positively by studying the demand and supply.