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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.

 

Configure the Manual Data Entry Process

1. Determine the Scope of the 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

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.

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. Determine Account Details

  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 for accessing scholarly sources and extracting data (best practice for searching)
  3. Questions
    • Who should data entry users go to 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

When

Dependent on the priorities set, the options are:

  • After all, parsing is completed for the targeted group
  • After an individual faculty member’s CV is completed
  • At the completion of the parsing project
Who

Options:

  • Faculty member
  • College/school/department-level support resources
Set clear expectations Pre-population of data is intended as a starting point from which the faculty member will review, modify, and 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 the 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 the main POC on campus.
  • IT subcontractor of Interfolio: 15 minutes/page to parse data
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