Manual Data Entry Process
One of the key ways to improve faculty buy-in as part of the implementation of Faculty180 is to have data already available in the system when a faculty member logs in. It takes the onus of re-entering a lifetime’s worth of academic work into Faculty180 off the faculty member.
1. Determine Scope of Project:
1.1. Determine the population of faculty
- Determine by employment status (full time, part-time/adjunct, etc.)
- Determine by rank/tenure status (Assistant Professors, Associate Professors, Full Professors, etc.)
1.2. Determine order
- By employment status
- By rank
- By college/school/department (and then by employment status/rank)
1.3. Data Scope
- Full parse
- Partial (targeted time frame) parse
- Activity-based parse
2. Determine Plan:
2.1. What is the Source of data?
- Faculty member’s CV (should be in either word or pdf format)
- Formats needed
- Electronic - to cut and paste from an electronic document into the appropriate section/field.
- Paper - to cross out as entries are completed.
2.2. What is the order of operations?
- Identify data imported from other sources and exclude those sections for input.
- Profile (Personal Information, Contact Information, Current Positions, etc.)
- Activity (Teaching, Grants, Advising, etc.)
- Activity: Scholarship data
- Import sources
- Generic import (RIS/Bibtex) from institutional sources
- In product import sources
- Import sources
- Activity: Service
- University Service
- Professional Service
- Community Service
- Profile: Degrees/Education
- Profile: Work Experience
- Other priorities as needed
3. Determine User Access:
3.1. Recommended Type of Account
Support Account - set up with user credentials to go through authentication.
3.2. Which environment should data entry occur in - Dev or PRD?
This is dependent on the implementation status:
- In progress / not deployed - Development
- Deployed - Production
4. Training Recommendations:
4.1. Account Access
- How to log in
- User experience (what they should expect to see as a support account user and navigation to emulate a faculty account)
4.2. Faculty Input
- Basic product functionality
- How to do data entry
- As items are validated and entered into the faculty account in the FAR module, they should be crossed out on the paper copy of the faculty member’s vita.
- Peer review - resources assigned to parse should also be assigned as peer reviewers.
- Last check by POC or his/her designee.
5.1. POC on Campus
- Serves as the primary manager of the parsing effort (training/supervising resources)
- Coordinating across college/school/department - providing training and high-level oversight.
- Identify the best source of scholarly data based on discipline.
- Serve as a resource on accessing scholarly sources and extracting data (best practice for searching)
Who should data entry users go with questions about data on the CV?
6. Develop a Communication Plan:
6.1. Who is Responsible/ What Resources?
- Executive-level area (Academic Affairs, Provost, Institutional Research, etc.)
- The resources assigned to develop/implement the plan
6.2. Channels identified
- Provost -> Dean -> Chairs -> Faculty
- Faculty Assembly
6.3. Overarching Message
- Service offered - voluntary
6.4. Expectation Setting
- Adhere to standards
- Format of CV
- Delivery location
- Available as a resource when/if needed
- Validate data entry
- Manage/modify/correct data entry
- Keep up-to-date moving forward
7. Faculty Verification:
Dependent on priorities set, the options are:
- After all parsing is completed for targeted group
- After individual faculty member’s CV is completed
- At the completion of the parsing project
- Faculty member
- College/school/department level support resources
7.3. Set clear expectations:
Pre-population of data is intended as a starting point from which the faculty member will review, modified, add new entries in an effort to jump-start the maintenance of data input that will occur over time.
Customer Use Case Details
- In this example, 8 grad students entered CVs for 300+ faculty over the course of 1.5 months. Managed by main POC on campus. Faculty Input training provided by Interfolio
- 3 users (2 students, 1 full-time employee) took 841 hours over the course of two terms to enter 155 CVs. The average processing time per CV was 5.5 hours. Managed by main POC on campus.
- IT subcontractor of Interfolio: 15 minutes / page to parse data