A Brief Study of The Use of Pattern Recognition in Online Learning: Recommendation for Assessing Teaching Skills Automatically Online Based

Abstract

Online learning has become a trend for the current generation of students who have been exposed to advanced information and communication technology. Smart education can use pattern recognition. Manual assessments are subjective and inconsistent. To overcome these problems, pattern recognition can be used in the non-verbal aspect assessment system. This study describes pattern recognition in online learning about the functions, modalities, and algorithms and specifically related to the recognition system of non-verbal aspects of teaching skills. The literature study was carried out through the stages of planning, selection, extraction, and selection. There are 86 articles reviewed. The first result is the functions of implementing pattern recognition in online learning are engagement recognition, attention detection, emotion recognition, learning behavior, learning activity recognition, authentication, teaching training, etc. using four classifications of modality: visual, audio, biosignal, behavioral, and CNN as the most widely used learning algorithm. Secondly, all modalities (except behavioral) and CNN algorithm can be used for assessing teaching skills. Early development of the non-verbal aspect assessment system can use Facial Expression Recognition (FER) and Hand Gesture Recognition (HGR). The future analysis needs to focus on technology characteristics, the meaningfulness of the content, and the proper teaching mode. In the end, hopefully, prospective teachers will acquire technology that can make it easier for them to practice teaching and get objective assessments.

Pattern Recognition of EFL University Students’ Online Behaviors through Data Science: Any Investment on English Language Components or Skills?

“…This is supported by other similar researches in the field. In his study, Utami et al, (2022), probed pattern recognition in online learning and asserted that pattern recognition can be applied in the non-verbal aspect assessment system of teaching skills.…”

“…Armed with such awareness, individuals can transform into independent, self-critical, and self-regulated participants, actively steering and monitoring their own teaching and learning processes. However, some aspects regarding the use of AI and technology within English teaching learning context should consistently be considered, such as the ease, technical aspects, and flexibility of such means (Utami et al, 2022).…”