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Project Motivation

The SMU Libraries Analytics and Research Department strives to develop a data-informed approach for achieving strategic objectives related to library operations and user needs. For this purpose, they have conducted an initial survey for the freshman batch of 2017 to evaluate the difference in their confidence level in various research skills before and after their first semester at SMU. The library currently aims to develop it’s trainings (content, methodology & availability) catering to solving specific student problems associated with those skills based on findings from the survey. Considering the importance of using library resources efficiently, it is central to understand the different trends and patterns students demonstrate in their usage of library resources and how it relates to attributes like modules undertaken, trainings attended and so forth. This will help us to provide the library with specific problems and targeted solutions based on schools and modules to eventually make the research process of an SMU student more effective and efficient.

Project Objectives

The objectives of the project are the following:

1. Business objective: To discover the current confidence level of freshmen across different faculties and identify trends. Moreover, to explain, with clear visuals, how students have responded to different trainings for each skill at the end of the semester
2. Technical objective: To use data analytics tools and statistical methods to study the data and obtain insights to facilitate the business objective

To achieve our two primary objectives, we will need:

• To understand the data domains
• To understand the library training process
• To identify if there exist any students who experience high or low confidence and its contributing factors
• To create a visual representation of the effectiveness of the trainings conducted during the semester, and provide recommendations.

Data

Our sponsor conducted two surveys with the freshman batch of 2017. Pre-survey was conducted before the start of the semester (Aug 2017) and post-survey at the end of the semester (Nov 2017). The pre and post survey datasets contain responses of students before and after the first semester on their confidence level in research skills.

Project Methodology

Our methodology can be summarised as:

1. We began our analysis by understanding the data domains that were provided along with secondary research
2. We continued to clean to data and transform it according to research requirements
3. We carried out exploratory data analysis using Tableau 10.0. This is where we did the visualisation analysis using the divergent stacked bar graphs
4. From the initial insights, we sought to statistically prove the relationships that were observed. For this, we used JMP Pro 13 to carry out the chi-squared tests
5. We conducted the text analysis to find out if students had any major issues with the trainings conducted
6. Lastly, we used all the analysis done to give recommendations to the library

Project Scope

Phase 1: Learning about the Case Context

We gathered information about the trainings conducted by the library to learn about the case context. This includes:

• Mapping out the workshops and trainings conducted by the library across the semester targeted for freshmen
• Reviewing the content of the trainings conducted and how they relate to the courses taken by freshmen from the different schools

Phase 2: Data Cleaning

As we were given several datasets by our sponsor, in the first phase, we studied the datasets to understand each of their variables and values to discern which ones would be useful given our project scope. Following that, we furthur studied the variables and values of the datasets that we chose to use. The steps include:

1. Recording the description and range for each variable and its values
2. Identifying irrelevant or duplicate fields
3. Resolving missing and invalid values
4. Cross-check related variables to verify accuracy
5. Transform variables for ease of analysis
6. Record assumptions made
7. Convert data values appropriately by removing null values, filling appropriate values
8. Combining related datasets on key variables
9. Documenting all of the above

Phase 3: Data Exploration

In the second phase, we conducted exploratory data analysis. The steps include:

• Studying the distributions of variables
• Identifying and treating outliers/anomalies
• Checking of assumptions about the relationships between the variables
• Develop hypotheses based on literature

This analysis was iterated a number of times, and we continually compared our findings to existing literature as well as what we knew of student behaviour.

Phase 4: Statistical Analysis

With a good understanding of the data and case, we performed statistical analysis. The steps include:

• Conduct statistical analysis to test the relationship between training and confidence
• Interpret the analysis to develop strategies that SMU Library can adopt

Phase 5: Text Analysis

The post survey contains a column with the comments of the respondents. We conducted word frequency analysis on the comments to derive insights about how the students feel about the library trainings. The steps include:

• Identify relevant words for analysis (adjectives, nouns and verbs)
• Determine minimum frequency for a word to be considered commonly used in the comments