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?

  1. 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.)
  2. Activity: Scholarship data
    • Import sources
      • Generic import (RIS/Bibtex) from institutional sources
      • In product import sources
  3. Activity: Service
    • University Service
    • Professional Service
    • Community Service
  4. Profile: Degrees/Education
  5. Profile: Work Experience
  6. Other priorities as needed

3. Determine User Access:

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

  • Overview
    • Goals
    • Basic product functionality
    • How to do data entry 

4.3. Tracking

  • 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. Support:

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.

5.2. Library

  • 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)

5.3. Questions

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

  • Mandatory
  • Service offered - voluntary

6.4. Expectation Setting

  • Adhere to standards
    • Format of CV
    • Delivery location
    • Deadline
  • Participation
    • Available as a resource when/if needed
    • Validate data entry
    • Manage/modify/correct data entry
    • Keep up-to-date moving forward

7. Faculty Verification:

7.1. When?

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

7.2. Who?

Options:

  • 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