Online Academic Performance Research Articles

The impact of online learning engagement on college students’ academic performance: The serial mediating effect of inquiry learning and reflective learning

ABSTRACT Although online learning has been widely used in higher education, its impact on students’ academic performance has not been well described. This study surveyed 2963 college students, and used structural equation modelling to determine the impact of college students’ online learning engagement on academic performance. The serial mediating effect of inquiry learning and reflective learning on the relation between online learning engagement and academic performance was also investigated. Results indicated that online learning engagement could positively predict college students’ academic performance. Inquiry learning and reflective learning had a serial mediating effect on the relationship between online learning engagement and academic performance. Reflective learning played a complete mediating role in the relation between inquiry learning and academic performance. This suggests that college students’ online academic performance can be improved by promoting their online learning engagement and encouraging them to use inquiry learning and reflective learning strategies.

A Positive View of Excessive Smartphone Utilization and Its Relationship With Other Academic-Related Variables Within the Online Course Setting.

The continual use of smartphones is a global problem that requires scholars' attention. This study investigates the impact of excessive smartphone use, self-regulation, and procrastination on students' online academic performance. A total of n = 238 university students participated in the study. Mean comparisons unveiled clear discrepancy scores on procrastination, self-regulation, and daily hours spent on smartphones between the smartphone-addicted and non-addicted students. Structural Equation Modeling helps us to answer our hypothesis. Unusually, smartphone use had a significant and positive effect on online students' academic performance. Also, the study provides a better understanding of the procrastination factor that significantly impacts students' smartphone use and online academic performance. Results are discussed considering possible interventions at the academic level.

Academic performance under COVID-19: The role of online learning readiness and emotional competence.

The COVID-19 pandemic caused school closures and social isolation, which created both learning and emotional challenges for adolescents. Schools worked hard to move classes online, but less attention was paid to whether students were cognitively and emotionally ready to learn effectively in a virtual environment. This study focused on online learning readiness and emotional competence as key constructs to investigate their implications for students’ academic performance during the COVID-19 period. Two groups of students participated in this study, with 1,316 high school students (Mean age = 16.32, SD = 0.63) representing adolescents and 668 college students (Mean age = 20.20, SD = 1.43) representing young adults. Structural equation modeling was conducted to explore the associations among online learning readiness, emotional competence, and online academic performance during COVID-19 after controlling for pre–COVID-19 academic performance. The results showed that, for high school students, both online learning readiness and emotional competence were positively associated with online academic performance during COVID-19. However, for college students, only online learning readiness showed a significant positive relationship with online academic performance during COVID-19. These results demonstrated that being ready to study online and having high emotional competence could make adolescents more resilient toward COVID-19–related challenges and help them learn more effectively online. This study also highlighted different patterns of associations among cognitive factors, emotional factors, and online academic performance during COVID-19 in adolescence and young adulthood. Developmental implications were also discussed.

Academic Performance Prediction Method of Online Education using Random Forest Algorithm and Artificial Intelligence Methods

In order to improve the teaching quality of online education, the prediction method of students' online academic performance has been studied. First, the learning analysis, artificial intelligence (AI) and other related theoretical concepts are analyzed and introduced. Then, the decision tree of single classification algorithm and the random forest (RF) of ensemble learning algorithm are analyzed, and the academic performance prediction model of online education is constructed by RF algorithm. Finally, the data of education platform is used for empirical analysis to verify the reliability and practicability of the academic performance prediction algorithm of online education. The connotation of learning analysis, the role and elements of learning analysis in the learning process are introduced. The algorithm principle of RF and decision tree is analyzed. By using the idea of information entropy and discretization, the continuous variables are processed to improve the fitting degree of the algorithm. The model is evaluated by empirical analysis, and the test accuracy of several different algorithms is compared. It is found that the prediction accuracy of the RF algorithm is more than 90%, which shows that the prediction method can help teachers and students to carry out better teaching and learning activities, so as to better improve students' ability to master knowledge. It is hoped that the result can provide some reference for the management of students' learning behavior and the optimization of teachers' teaching strategies in online learning activities

Academic Self-Efficacy and Academic Performance in Online Learning: A Mini Review.

Recently, online learning has been broadened to support learners’ learning processes. Attention has been given to academic self-efficacy (ASE) in educational psychology as an influential factor to enhance academic performance. Consequently, previous studies that have examined the relationship between ASE and online academic performance are reviewed and future directions discussed. Because there are limited findings to conduct a systematic meta-analysis on the relationship between ASE and academic performance in an online learning environment, a focused narrative review based on the previous findings are outlined. Finally, future directions, particularly in relation to similar and different aspects between a general learning and online learning environment are discussed.

Improving Online Course Performance Through Customization

The number of educational courses offered online is growing, with students often having no choice for alternative formats. However, personal characteristics may affect online academic performance. In this study, the authors apply two business analytics methods - multiple linear/polynomial regression and generalized additive modeling (GAM) - to predict online student performance based on six personal characteristics. These characteristics are: communication aptitude, desire to learn, escapism, hours studied, gender, and English as a Second Language. Survey data from 168 students were partitioned into training/validation sets and the best fit models from the training data were tested on the validation data. While the regression method outdid the GAM at predicting student performance overall, the GAM explained the performance behavior better over various predictor intervals using natural splines. The study confirms the usefulness of business analytics methods and presents implications for college administrators and faculty to optimize individual student online learning.

Evaluating the Effectiveness of Lecture Capture: Lessons Learned from an Undergraduate Political Research Class

This article presents the results of a 4-year quasi-experimental study of the effectiveness of lecture capture in an undergraduate political research class. Students self-enrolled in either a traditional in-class lecture-discussion section or a fully online section of a required political research course. The class sessions from the in-class section were recorded and provided asynchronously to the online students. The instructor, course assignments, exams, and supplemental materials were the same for the in-class and online sections. The two types of sections were compared on course-embedded academic performance measures and on the completion rates of the course and of the course assignments, controlling for prior cumulative grade point average (GPA), prior credits completed, and gender. The mean overall course score for the online students was slightly, but significantly, lower than scores for the in-class students. The students’ GPAs interacted with the type of section such that the difference between in-class and online academic performance diminished or disappeared among students with higher GPAs. Completion rates for the course and for assignments were significantly lower for online students. Strategies for addressing these problems through greater student engagement are widely cited, but recent empirical tests of these strategies have not provided satisfactory results.

Effect of Self-efficacy, Motivation and Anxiety on Online Academic Performance

As the number of online-learning users continues to increase, there is a need to understand the Effect of Self-efficacy, Motivation and Anxiety on Online Academic Performance. Through the online learning process, students can access the given information of teachers at any time on the respective online platform. The study aims to find out the Effect of Self-efficacy, Motivation and Anxiety on Online Academic Performance. It is comparatively more convenient in the current situation, because of the pandemic; every educational institute follows this learning method for properly giving education or lessons to their students. The study included 150 participants studying various courses at an Indian University. The mean age of participants was 19 years. The study consisted of 3 scales, the academic self-efficacy scale, the academic motivation scale and the state trait anxiety inventory. The researcher carried out a descriptive study to identify the aim of the study. Independent sample t-test and Mann-Whitney U test were carried out and the results of the study were analysed for the mentioned hypothesis. The study found that the mean scores of academic performances dropped in the individuals with low self-efficacy. Also, the mean scores of academic performances dropped in the individuals with low motivation. These differences were not significant. However, it was found out that the individuals with high anxiety saw a significant drop in their academic performances. Hence the study shows that anxiety had a great effect on the levels of academic performance of students.