Students' engagement characteristics predict success and completion of online courses (original) (raw)

Measuring Student Engagement in the Online Course: The Online Student Engagement Scale (OSE)

Online Learning, 2015

Student engagement is critical to student learning, especially in the online environment, where students can often feel isolated and disconnected. Therefore, teachers and researchers need to be able to measure student engagement. This study provides validation of the Online Student Engagement scale (OSE) by correlating student self-reports of engagement (via the OSE) with tracking data of student behaviors from an online course management system. It hypothesized that reported student engagement on the OSE would be significantly correlated with two types of student behaviors: observational learning behaviors (i.e., reading e-mails, reading discussion posts, viewing content lectures and documents) and application learning behaviors (posting to forums, writing e-mails, taking quizzes). The OSE was significantly and positively correlated with application learning behaviors. Results are discussed along with potential uses of the OSE by researchers and online instructors.

The Relationship Between Student Engagement and Academic Performance in Online Education

2021

In recent years, online education has become a mature, recognised, and heavily used alternative for delivering higher education programmes. Beyond its benefits, online education faces a number of challenges, some of which relate to its engagement and impact on student performance. To support the ongoing research into the complex relationships developed, this research investigated the relationship between engagement and academic performance for students that undertake standalone online programmes. The study uses as input the module content engagement data, as collected from an e-learning platform, including the number of content views, forum posts, completed assignments, and watching of videos. The study used Pearson correlation to evaluate the relationship between learner engagement and academic performance. The analysis revealed that the student engagement was positively correlated to the student performance both for individual modules as well as across the cohort. In addition, correlation between initial engagement with individual subjects and the overall engagement was also strong, indicating both variables lead to improved academic results.

Factors that Predict Student Engagement in Online Learning: HarvardX Case Study

With the increased availability of learning analytics data in the online learning space, there is room for research that examines and develops quantifiable measures of online teaching and learning practices. This paper examines the 2012-2013 HarvardX learning analytics data to look at factors predicting student engagement. The effects of student demographic and online learning behavior on student engagement levels has been examined through logistic regression analysis. According to the results, there are three significant predictors for student engagement: (1) student age; (2) the number of unique days students interacted within the course; and (3) the number of chapters with which the student interacted. Implications for future studies using learning analytics data for measuring online teaching and learning practices are discussed.

Student Perceptions of Engagement in Online Courses: An Exploratory Study

Online Journal of Distance Learning Administration, 2019

Given the increasing numbers of students who choose to learn online, educators should understand the conditions necessary for student success in this environment. Previous studies have documented that student engagement is essential to student learning, retention, persistence, and satisfaction. In this descriptive qualitative study, we sought to understand how students conceptualize engagement in online courses as well as to understand what elements students perceive to be engaging. For this work, we interviewed or surveyed 40 students who shared their perceptions of engagement in online courses. We uncovered several key themes related to types of engagement including behavioral engagement, cognitive engagement, social engagement, emotional engagement, and agentic engagement. Additionally, the students described specific course elements they find engaging. We offer suggestions for distance learning administrators and instructional designers who wish to work with instructors on engag...

Matters of Frequency, Immediacy and Regularity: Engagement in an Online Asynchronous Course

Innovative Higher Education

Many models of online student engagement posit a "more is better" relationship between students' course-related actions and their engagement. However, recent research indicates that the timing of engagement is also an important consideration. In addition to the frequency (how often) of engagement, two other constructs of timing were explored in this study: immediacy (how early) and regularity (in what ordered pattern). These indicators of engagement were applied to three learning assessment types used in an online, undergraduate, competency-based, technology skills course. The study employed advanced data collection and learning analytics techniques to collect continuous behavioral data over seven semesters (n = 438). Results revealed that several indicators of engagement predicted academic success, but significance differed by assessment type. "More" is not always better, as some highly engaged students earn lower grades. Successful students tended to engage earlier with lessons regardless of assessment type.

A Predictive Model to Evaluate Students’ Cognitive Engagement in Online Learning

Procedia - Social and Behavioral Sciences, 2014

The expanding usage of online learning at all levels of education has drawn attention to the quality of online learning. In this study, online learning quality is evaluated through students' cognitive engagement which is reflected in their online written messages in discussions and their online participation. This study proposes the use of two types of data: students' participation, and written messages. Both types of data was collected and analyzed using the data mining technique to produce a predictive model that illustrates students' pathways while engaging in online learning cognitively. The findings of this study indicate that from 22 variables, only two were significant for students' online cognitive engagement; sharing information and posting highlevel messages. The two variables led to the formation of three different pathways in the students' predictive model.

Can Student Engagement in Online Courses Predict Performance on Online Knowledge Surveys

International Journal of Learning, Teaching and Educational Research, 2017

The link between student engagement and academic performance has been widely examined. However, most of these studies have focused on ascertaining the existence of such a relationship on the summative assessment level. By comparing students’ experience points in an online course and students’ scores on online knowledge surveys (KS), this study examined the relationship between student engagement and performance on online KS on the formative assessment level. Knowledge surveys were developed and formatively administered in four sections of an online Integration of ICT in Education course. Using Moodle Feedback Module, knowledge surveys were designed based on three key elements: learning objectives, the course content, and the revised Bloom’s Taxonomy of learning objectives. Using rated multiple choice KS questions, the correlation between students’ scores on KSs and students’ experience points was calculated using SPSS. The results show that students’ confidence levels in ability to ...

Impact of Student Engagement in Online Learning Environments

Optimizing Student Engagement in Online Learning Environments

Online learning is a fast-growing technology in an educational field which uses internet as a media to deliver the educational contents to the students. The main research area in online learning is to identify the disengaged learners and motivate them. The success of online learning systems depends on how quickly it identifies the disengaged learners and techniques used to reengage them. Through this chapter, we are going to discuss briefly about the online learning, advantages and disadvantages of online learning, importance of motivation in online learning, types of motivation, the motivational theories related to student engagement and finally discuss about various disengagement detection techniques in online learning.

Engaging online learners: The impact of Web-based learning technology on college student engagement

Computers & Education, 2010

Widespread use of the Web and other Internet technologies in postsecondary education has exploded in the last 15 years. Using a set of items developed by the National Survey of Student Engagement (NSSE), the researchers utilized the hierarchical linear model (HLM) and multiple regressions to investigate the impact of Web-based learning technology on student engagement and self-reported learning outcomes in face-to-face and online learning environments. The results show a general positive relationship between the use the learning technology and student engagement and learning outcomes. We also discuss the possible impact on minority and part-time students as they are more likely to enroll in online courses.