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Related Experiment Video

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Interactive and Visualized Online Experimentation System for Engineering Education and Research

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Scholar Plot: Design and Evaluation of an Information Interface for Faculty Research Performance.

Dinesh Majeti¹, Ergun Akleman², Mohammed Emtiaz Ahmed¹

¹Computational Physiology Laboratory, University of Houston, Houston, TX, United States.

Frontiers in Research Metrics and Analytics

|April 19, 2021

Summary

Scholar Plot (SP) is a new interface for collecting and analyzing academic performance data. It addresses biases and presents data in scalable formats for consistent university-wide rankings.

Keywords:

information visualization research career evaluation science of science scientometrics university evaluation

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Area of Science:

Bibliometrics
Scientometrics
Academic Analytics

Background:

Objective assessment of academic performance is crucial for merit recognition, policy development, and scientific advancement.
Current systems for collecting and analyzing academic data are limited, often lacking in bias removal and insightful presentation.
Automated online interfaces are increasingly important for gathering valid and current academic performance data.

Purpose of the Study:

To introduce Scholar Plot (SP), an interface designed to harvest and analyze academic data from online sources.
To address systematic biases in academic data through nominal and normalized metrics.
To present data in scalable plot and iconic forms for both expert appraisal and broader consumption.

Main Methods:

Harvesting bibliographic and research funding data from online sources.
Implementing nominal and normalized metrics to mitigate data biases.
Developing scalable plot and iconic visualizations for data representation.
Conducting an evaluation survey with senior faculty experienced in promotion and tenure assessment.

Main Results:

Scholar Plot (SP) effectively harvests bibliographic and research funding data.
Nominal and normalized metrics are shown to address systematic biases in the data.
The plot and iconic forms of SP are scalable for analyzing individual scholars and academic units.
An evaluation survey indicated the utility of SP's design principles.

Conclusions:

Scholar Plot (SP) offers a novel approach to collecting and analyzing academic performance data.
SP's bias mitigation techniques and scalable visualizations support consistent and insightful academic assessment.
The interface has the potential to improve academic policy, merit recognition, and the promotion of science.