Maximum Project Overview
The library currently aims to optimise its resource availability and distributions channels to maximise the learning effectiveness of its students. This could be in terms of increasing resources available for certain highly searched topics, altering current trainings and workshops to focus on any common mistakes committed by students while using the assets or finding any unexpected trends in user journey through digital and physical touch points. They further want to know if usage patterns vary between students based on certain attributes like Programme, Year of Graduation and Education Level. 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 joining SMU, factoring in several considerations like modules taken, library workshops attended and so on and so forth. They wish for us to understand if this survey contains any actionable insights.
We had an initial discussion with our project sponsor and they would like us to create a visual dashboard to ascertain cause and effect relationship between the initiatives and resources of the library, and student performance (in terms of confidence and optimal usage of resources).
The objectives of the project would be of the following:
- Analysis of the Library’s proxy server logs (usage of online resources) and turnstile logs (usage of physical resources)
- Initial exploratory analysis to identify clusters in usage patterns
- To understand if these patterns affect confidence level of students (limited to freshmen)
- Alternatively, determine if confidence level spurs certain search behaviour.
- Recommendations based on findings
- To help stakeholders understand the analysis of the findings
- To validate or suggest changes in current workshops and trainings
- To validate or suggest changes in the current availability of resources
The current objectives may be subjected to further changes after we have obtained and look at the actual data.
The sponsor has provided us with five datasets - student data, request log data, turnstile data, and pre and post survey data.
The student dataset contains information about the current students of SMU across all batches. The record attributes are the following:
- email (hashed to a 64-digit- long hexadecimal number for non-disclosure reasons)
- education level
- faculty
- admission year
- graduation year
- degree program
The request log dataset contains records captured by the library’s URL rewriting proxy server throughout the year of 2017. This dataset captures all user requests to external databases. The record attributes are the following:
- user ID
- session ID
- search database
- timestamp
- search query
The turnstile dataset contains records captured by the library’s gantries throughout the year of 2017. This dataset captures physical taps on the gantries of the library. The record attributes are the following:
- date
- time
- device name
- email (hashed to a 64-digit- long hexadecimal number for non-disclosure reasons)
The pre and post survey dataset contains responses of students before and after the first semester of freshman year on their confidence level in various research skills. Some of the record attributes are as follows:
- email (hashed to a 64-digit- long hexadecimal number for non-disclosure reasons)
- school
- modules taken
- library workshops attended
