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Configure Manual Data Entry Process

One of the key ways to improve faculty buy-in as part of the implementation of Faculty Activity Reporting (FAR, Faculty180) is to have data already available in the system when a faculty member logs in. It takes the responsibility of re-entering a lifetime’s worth of academic work into FAR off the faculty member.


1. Determine Scope of Project

  1. Determine the population of faculty
    • Determine by employment status (full time, part-time/adjunct, and more)
    • Determine by rank/tenure status (Assistant Professors, Associate Professors, Full Professors, and more)
  2. Determine order
    • By employment status
    • By rank
    • By college/school/department (and then by employment status/rank)
  3. Data Scope
    • Full parse
    • Partial (targeted time frame) parse
    • Activity-based parse

2. Determine Plan

  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. 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, and more)
      • Activity (Teaching, Grants, Advising, and more)
    • Activity: Scholarship data
      • Import sources
        • Generic import (RIS/Bibtex) from institutional sources
        • In product import sources
    • Activity: Service
      • University Service
      • Professional Service
      • Community Service
    • Profile: Degrees/Education
    • Profile: Work Experience
    • Other priorities as needed

3. Determine User Access

  1. Recommended Type of Account
    • Support Account - set up with user credentials to go through authentication.
  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

  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)
  2. Faculty Input
    • Overview
      • Goals
      • Basic product functionality
      • How to do data entry 
  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

  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.
  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)
  3. Questions
    • Who should data entry users go with questions about data on the CV?

6. Develop a Communication Plan

  1. Who is Responsible/ What Resources?
    • Executive-level area (Academic Affairs, Provost, Institutional Research, and more)
    • The resources assigned to develop/implement the plan
  2. Channels identified
    • Provost -> Dean -> Chairs -> Faculty
    • Faculty Assembly
  3. Overarching Message
    • Mandatory
    • Service offered - voluntary
  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

  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
  2. Who?
    • Options:
      • Faculty member
      • College/school/department level support resources
  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