Monitoring online learners' performance based on learning progress prediction

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

Abstract

The teacher needs to know the learner's progress so that he/she can improve her/his teaching strategy. The initial challenges faced by the teachers are including the need to monitor each learner's progress so that their needs and difficulties can be recognized as a guide for enhancing learning strategies. One way to solve these problems is to predict student's progress. Mathematics in elementary school is one of the subjects with a high failure rate of learners. In this study, the progress of learners was predicted using Random Forest based on classification using similarity characteristics of learners obtained from the results of previous formative assessment. The random forest algorithm was used because it can classify data that has incomplete attributes, which are usually contained in learner characteristics. Prediction models are built based on data of assessment results from 2 math classes with 46 students in elementary school. The resulting model performance will be measured using accuracy and recall because False Positive is better than False Negative so learning progress will always be improved. The results show that the Random Forest algorithm can create a learning progress prediction model with an accuracy of 90% in training data and 93% in testing data.

Original languageEnglish
Title of host publication2nd Science and Mathematics International Conference, SMIC 2020
Subtitle of host publicationTransforming Research and Education of Science and Mathematics in the Digital Age
EditorsMeiliasari Meiliasari, Yuli Rahmawati, Mutia Delina, Ella Fitriani
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735440753
DOIs
Publication statusPublished - 2 Apr 2021
Event2nd Science and Mathematics International Conference: Transforming Research and Education of Science and Mathematics in the Digital Age, SMIC 2020 - Jakarta, Indonesia
Duration: 8 Aug 20209 Aug 2020

Publication series

NameAIP Conference Proceedings
Volume2331
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2nd Science and Mathematics International Conference: Transforming Research and Education of Science and Mathematics in the Digital Age, SMIC 2020
Country/TerritoryIndonesia
CityJakarta
Period8/08/209/08/20

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