How Highly Achieved Students Differ From the Others? A Text-Mining Approach to Personal Learning Goals

Hilary K. Y. Ng, C. H. Chan

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

Abstract

People often set goals at the start of a new event in their life. Goals are related to performance across different domains, including sports, psychotherapy, leadership, health care, as well as education. Those students who set learning goals are found to have higher learning motivation, more persistence in learning, better course attendance, and better academic performance than their counterparts. Previous studies showed students benefited most from setting specific, challenging, measurable, and achievable learning goals than their counterparts did. While goal-setting activity appears to be an effective and inexpensive way to enhance learning performance, how learning goals vary as a function of students’ course grades remains under-explored. Rather than classifying students’ learning goals into pre-established categories for summative investigation, the present study adopts a text-mining approach to examine whether learning goals associate with course grades. There were 192 university students who set three different learning goals at the beginning of a semester. Results from 552 valid responses indicated that highly achieved students differ from their counterparts in expressing their personal goals. The present finding provides an opportunity for us to learn from the highly achieved students. Other theoretical advances and practical advances in education, teaching and learning will also be discussed.
Original languageEnglish
Title of host publicationThe IAFOR Conference on Educational Research & Innovation
Subtitle of host publication2023 Official Conference Proceedings
PublisherThe International Academic Forum (IAFOR)
Pages239-247
Number of pages9
Volume2023
DOIs
Publication statusPublished - 19 Jun 2023

Publication series

NameERI Official Conference Proceedings
ISSN (Print)2435-1202

Keywords

  • Learning goals
  • Text-mining
  • Learning performance

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