Evaluation of classification algorithms for predicting students’ learning performance based on bookroll reading logs

Anna Yu Qing Huang, Owen Hsin Tes Lu, Stephen J.H. Yang

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

Abstract

To help students’ learning, finding at-risk students is an important issue in education. This study has collected students’ learning logs in ebook reading environment for 17 blended learning courses in Taiwan. Then, we have construct students’ learning performance prediction model based on learning logs by using eight classification methods. To improve prediction performance, we have also propose grading policy rules which include of grading on stringency rule, grading on moderate rule, and grading on leniency rule to define the labels of students’ learning performance. According to the prediction performance results, the LR is the suitable classification method for predicting students’ learning performance. Besides, the grading on leniency rule has obtained the higher prediction performance. For exploring the influence factors on classification performance, we investigate the relationship between students’ online learning actions and students’ academic performance by Spearman coefficient analysis the open file (f1), the number of delete marker (f6), the number of next pages (f12), and the number of previous pages (f13) features are the critical factors for affecting the prediction performance of learning logs.

Original languageEnglish
Title of host publicationCognitive Cities - 2nd International Conference, IC3 2019, Revised Selected Papers
EditorsJian Shen, Yao-Chung Chang, Yu-Sheng Su, Hiroaki Ogata
PublisherSpringer
Pages262-272
Number of pages11
ISBN (Print)9789811561122
DOIs
StatePublished - 2020
Event2nd International Cognitive Cities Conference, IC3 2019 - Kyoto, Japan
Duration: 3 Sep 20196 Sep 2019

Publication series

NameCommunications in Computer and Information Science
Volume1227 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference2nd International Cognitive Cities Conference, IC3 2019
Country/TerritoryJapan
CityKyoto
Period3/09/196/09/19

Keywords

  • AUC
  • Classification
  • Spearman correlation

Fingerprint

Dive into the research topics of 'Evaluation of classification algorithms for predicting students’ learning performance based on bookroll reading logs'. Together they form a unique fingerprint.

Cite this