Core Research Topics of Studies on Personalized Feedback in the Past Four Decades

Xieling CHEN, Di ZOU*, Gary CHENG, Haoran XIE, Fu Lee WANG, Leung Pun WONG

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review


Assessment feedback is an essential part of learners’ learning experiences. Personalized feedback in learning is a useful and common strategy for assisting learners to optimize their learning. With the increasing need to provide learners with high quality, immediate, and personalized feedback, a large number of studies had been conducted to investigate how to provide students with personalized feedback effectively. In this study, bibliometric analysis and word cloud techniques were applied to identify research trends and status related to personalized feedback in teaching and learning, based on 276 publications retrieved from the Web of Science database. To be specific, the data were analyzed in terms of annual numbers of publications and citations, important publication sources, countries/regions, and institutions, as well as important research issues and concerns. The findings of this study provided scholars as well as instructors with a general picture of the personalized feedback research.
Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications. DASFAA 2020 International Workshops
EditorsYunmook NAH, Chulyun KIM, Seon-Young KIM, Yang-Sae MOON, Steven Euijong WHANG
PublisherSpringer, Cham
Number of pages10
ISBN (Electronic)9783030594138
ISBN (Print)9783030594121
Publication statusPublished - 22 Sept 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12115 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

This research received grants from the Standing Committee on Language Education and Research (EDB(LE)/P&R/EL/175/2), the Education Bureau of the Hong Kong Special Administrative Region, the Internal Research Grant (RG93/2018–2019R), and the Internal Research Fund (RG 1/2019–2020R), the Interdisciplinary Research Scheme of the Dean’s Research Fund 2018–19 (FLASS/DRF/IDS-3) and the Small Research Grant for Academic Staff 2019–20 (MIT/SGA04/19–20) of The Education University of Hong Kong.


  • Bibliometric analysis
  • Personalized feedback
  • Word cloud


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