Identifying Students' Learning Patterns in Online Learning Environments: A Literature Review

Abstract

Digital learning environments have offered new opportunities to stream educational materials such as courses, educational videos, forums and provide outcomes of the students, grades, engagement details, and learning patterns. These valuable educational data has emerged with the latest technologies and software tools to provide researchers and decision-makers with a better understanding of students' behaviours. These virtual learning environments can professionally aid struggling students by observing, learning, and identifying different learning patterns. Many researchers have discussed that even if there are instructions and helping tools within these environments, some students remain at risk of negative learning behaviours such as boredom, disengagement, and failure. Particularly when approaching complex or new educational content. Previous researchers have observed that the students exhibit high persistence levels when spending too much time on a particular task when they are learning remotely or too many tries to solve a specific task without reaching the success level. Students' persistence is identified as a prominent learning skill contributing to confirmed success while learning new education materials. Many works of literature recognised the value of persistence. They reached a fundamental fact that not all persistence is considered productive, especially when spending more time and effort without moving toward a state of mastery in learning new skills and topics. This scenario may eventually lead to frustration and disengagement; in the worst-case scenarios, the students will finally drop the course or just quit learning. By examining the most relevant literature, this paper discusses the main factors affecting students' digital learning persistence. Different models performed at each learning opportunity are observed to categorise when involvements may be arranged to best aid the learners facing learning struggles.

A Conjoint Study and Segmentation on the Preferred Online Learning Attributes of Senior High School Learners amidst COVID-19 Pandemic

“…Ong et al (2021) concluded that learners who interact during class discussions with their teacher and peers complete and pass the subject successfully. Therefore, it is imperative to consider the logistical requirements of particular online instructional designs, such as the compatibility of the device, bandwidth for accessing the learning management system, participating in synchronous sessions, accessing learning materials, and using specific content formats like videos (Ali et al, 2022).…”

The Effect of Virtual Reality Technology in Teaching Mathematics on Students’ Ability to Process Data and Graphic Representation

“…VR technology immerses the user in an experience rather than merely observing a screen in front of them. Users can interact with a 3-dimensional world using multiple senses, including sight and even touch, facilitated by wearing VR glasses [2]. By utilizing VR technology in science education, students can explore various scientific topics in a fun and interactive manner, including space, the solar system, human anatomy, and other scientific fields [3].…”

Design of a Machine Learning Model to Predict Student Attrition

“…Student dropouts can occur due to various factors, including family background, inadequate social connections, employment, poor relationships, lack of motivation, insufficient persistence or skills, previous academic struggles, stress, disengagement, self-evaluation issues, poor academic performance, financial hardships, unexpected external problems, classroom environment, teacher engagement, and course material, among other factors [1] [6] [7] [11] [18]. While these factors may indicate a student's risk of dropping out, gathering data, particularly sensitive data such as parental educational background and financial status, can be problematic.…”