Digitizing Peer Assessments in the U.S. Army Officer Candidate School

Abstract

The purpose of this research was to develop a sustainable, versatile, and useful digital peer assessment for the U.S. Army Officer Candidate School that is compatible with the Army network and meets identified instructional and programmatic needs. The digital assessment is comprised of two dynamic Microsoft Excel based tools a data collection tool and a compiler tool that expedite the data collection process and automate the synthesis of peer data. The tools were developed through an iterative process of testing, gathering end user input and feedback, and making necessary modifications. Qualitative and quantitative utility and usability feedback data were collected from end users to evaluate the tools and processes for conducting the digital peer assessment. This report describes the development, testing, and evaluation of the digital peer assessment tools. Although developed for OCS, the tools and processes can be adapted for other instructional contexts and can be used to enable formative and summative assessments.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2021
Accession Number
AD1125667

Entities

People

  • Ashley H. Wittig
  • Celeste N. Sanders
  • Elizabeth R. Uhl
  • Frederick J. Diedrich
  • Joshua Shireman
  • Kristy Reynolds
  • Ronelle L. Koschny
  • Scott M. Flanagan
  • Tatiana H. Toumbeva

Organizations

  • Aptima (United States)
  • U.S. Army Research Institute for the Behavioral and Social Sciences

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Accountability
  • Army Training
  • Compilers
  • Contracts
  • Data Management
  • Digital Data
  • Feedback
  • Information Systems
  • Instructions
  • Instructors
  • Leadership
  • Learning
  • Military Research
  • Networks
  • Ratings
  • Schools
  • Social Sciences
  • Spreadsheet Software
  • Students
  • Surveys
  • Test And Evaluation
  • Training
  • User Friendly

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Instructional Design and Training Evaluation.
  • Software Engineering.