Student Personality and Learning Outcomes in E-Learning

Web-based courses are a popular format in the e-learning environment. Among students enrolled in Web-based courses, some students learn a lot, while others do not. There are many possible reasons for the differences in learning outcomes (e.g., student’s learning style, satisfaction, motivation, etc.). In the last few decades, students’ personality has emerged as an important factor influencing the learning outcomes in a traditional classroom environment. Among different personality models, the Big-Five model of personality has been successfully applied to help understand the relationship between personality and learning outcomes. Because Web-based courses are becoming popular, the Big-Five model is applied to find out if students’ personality traits play an important role in a Web-based course learning outcomes.

Measuring Success in a Synchronous Virtual Classroom

This chapter will benefit those who teach individuals using the synchronous virtual classroom (SVC). The SVC model will help instructors design online courses that incorporate the factors that students need to be successful. This model will also help virtual classroom instructors and managers develop a systematic way of identifying and addressing the external and internal factors that might impact the success of their instruction. The strategies for empirically researching the SVC, which range from qualitative inquiry to experimental design, are discussed along with practical examples. This information will benefit instructors, researchers, non-profit and profit organizations, and academia.

An Introduction to Structural Equation Modeling (SEM) and the Partial Least Squares (PLS) Methodology

Over the past 15 years, the use of Partial Least Squares (PLS) in academic research has enjoyed increasing popularity in many social sciences including Information Systems, marketing, and organizational behavior. PLS can be considered an alternative to covariance-based SEM and has greater flexibility in handling various modeling problems in situations where it is difficult to meet the hard assumptions of more traditional multivariate statistics. This chapter focuses on PLS for beginners. Several topics are covered and include foundational concepts in SEM, the statistical assumptions of PLS, a LISREL-PLS comparison and reflective and formative measurement.

Learning and Satisfaction in Online Communities of Inquiry

The purpose of this chapter is to explain the capability of the Community of Inquiry (CoI) framework as a research model to study student learning and satisfaction. The framework identifies three elements (social, cognitive, and teaching presence) that contribute directly to the success of an e-learning experience through the development of an effective CoI. It is argued that a CoI leads to higher learning and increased satisfaction. The chapter presents findings from two online courses designed using the CoI approach. Overall, the students in these courses had high levels of perceived learning and satisfaction, as well as actual learning outcomes.

Online Project-Based Learning

This study integrated Project-based learning (PBL) in an online environment and aimed to investigate critical issues, dynamics, and challenges related to PBL from 49 student perspectives in an online course. The effect of PBL was examined qualitatively with open-ended questionnaire, observations, and students’ submissions who were taking an online certificate course. According to the findings, students thought that an online PBL course supports their professional development with provision of practical knowledge, enhanced project development skill, self confidence, and research capability. This support is further augmented with the facilities of the online learning environment. Students mainly preferred team-work over individual work. Although students were mostly satisfied with the course, they still had some suggestions for prospective students and instructors. The findings are particularly important for those people who are planning to organize course or activities which involve online PBL and who are about take an online or face-to-face PBL course.

Quality Assurance in E-Learning

Quality is a subjective concept, and as such, there are many criteria for assuring quality, including assessment practices based on industry standards and accreditation requirements. Most assessments, including quality assurance in e-learning, frequently occur at three levels: individual course assessments, department or program assessments, and institutional assessments; frequently these levels cannot be distinctly delineated. While student evaluations are usually included within these frameworks, student views are but one variable in the quality assessment equation. To offer some plausible perspectives of how students view quality, this chapter will provide an overview of quality assurance for online learning from the course, program, and institutional viewpoints as well as review some of the key research related to students’ assessment of what constitutes quality in online courses.

How to Design, Develop, and Deliver Successful E-Learning Initiatives

The purposes of this chapter are three-fold: (1) to present findings in investigating the success factors for designing, developing and delivering e-learning initiatives, (2) to examine the applicability of Information Systems theories to study e-learning success, and (3) to demonstrate the usefulness of action research in furthering understanding of e-learning success. Inspired by issues and challenges experienced in developing an online course, a process approach for measuring and assessing e-learning success is advanced. This approach adopts an Information Systems perspective on e-learning success to address the question of how to guide the design, development, and delivery of successful e-learning initiatives. The validity and applicability of the process approach to measuring and assessing e-learning success is demonstrated in empirical studies involving cycles of action research. Merits of this approach are discussed, and its contributions in paving the way for further research opportunities are presented.

An Introduction to Path Analysis Modeling Using LISREL

Over the past decades, there has been a wide range of empirical research in the e-learning literature. The use of multivariate statistical tools has been a staple of the research stream throughout the decade. Path analysis modeling is part of four related multivariate statistical models, including regression, path analysis, confirmatory factor analysis, and structural equation models. This chapter focuses on path analysis modeling for beginners using LISREL 8.70. Several topics covered in this chapter include foundational concepts, assumptions, and steps of path analysis modeling. The major steps in path analysis modeling explained in this chapter consist of specification, identification, estimation, testing, and modification of models.

A Review of Research Methods in Online and Blended Business Education

This review of the online teaching and learning literature in business education found growing sophistication in analytical approaches over the last 10 years. The authors of this chapter believe researchers are uncovering important findings from the large number of predictors, control variables, and criterion variables examined. Scholars are employing appropriate and increasingly sophisticated techniques such as structural equation models in recent studies (16) within a field setting. To increase methodological rigor, researchers need to consciously incorporate control variables that are known to influence criterion variables of interest so as to clearly partial out the influence of their predictor variables of interest. This will help address shortcomings arising from the inability to convince sample respondents such as instructors, institutional administrators, and graduate business students on the benefits versus the cost of a fully randomized design approach.

Using Experimental Research to Investigate Students’ Satisfaction with Online Learning

The use of experimental research in higher education settings for investigating the effectiveness of technology-supported instructional innovations in K-12 and higher education settings is fairly limited. The implementation of the No Child Left Behind Act (NCLB) of 2001 has renewed the emphasis on the use of experimental research for establishing evidence to support the effectiveness of instructional interventions and other school-based programs in K-12 and higher education contexts. This chapter discusses the most common experimental designs and threats to internal validity of experimental procedures that must be controlled to ensure that the interventions or programs under investigation are responsible for changes in the dependent variables of interest. A study by Bangert (2008) is used to illustrate procedures for conducting experimental research, controlling potential threats to internal validity and reporting results that communicate both practical and statistical significance.