Reading Time Prediction Model on Chinese Technical Documentation

Zhijun Gao, Fan Li, Jingsong Yu

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

This paper was presented at the Invited Panel session “Technical Communication in China”. There has been various research on the reading time and legibility of online texts with people's tendency to online materials. Text-related attributes like font size or letterspacing are commonly used variables in this field. The objective of this study is to investigate the influential factors on the reading time of Chinese technical documentation, and to build a Decision Tree model to predict its reading time. In the experiment, log data including information of over a million user visits from a cloud service provider's website are collected. User's visit time, stay time, visit step, visit device and many other data fields are recorded in a user session. In addition to user behavioral data from log files, data metrics concerning technical documentation itself are also collected. For all documents used in the experiment, their word counts, image counts, link counts and section counts are scraped using web crawlers. The linear correlation analysis is applied in order to explore the correlations between variables for predictions. The results show that a 75 percent accuracy is achieved using the Decision Tree model.
Original languageEnglish
Title of host publication2020 IEEE International Professional Communication Conference (ProComm)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages161-167
Number of pages7
ISBN (Electronic)978-1-7281-5563-0
ISBN (Print)978-1-7281-5564-7
DOIs
Publication statusPublished - Jul 2020
EventIEEE International Professional Communication Conference, ProComm 2020 - Online via Engagez.net conference platform, Kennesaw, United States
Duration: 20 Jul 202021 Jul 2020

Publication series

NameIEEE International Professional Communication Conference (ProComm)
PublisherIEEE
Volume2020
ISSN (Print)2158-091X
ISSN (Electronic)2158-1002

Conference

ConferenceIEEE International Professional Communication Conference, ProComm 2020
Abbreviated titleProComm 2020
CountryUnited States
CityKennesaw
Period20/07/2021/07/20

Keywords

  • Decision tree
  • Online documentation
  • Readability
  • Technical communication

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