The Impacts of System and Human Factors on Online Learning Systems Use and Learner Satisfaction

Alshare, Khaled A.; Freeze, Ronald D.; Lane, Peggy L.; Wen, H. Joseph

doi:10.1111/j.1540-4609.2011.00321.x

Cited by 46 publications

(43 citation statements)

References 42 publications

(61 reference statements)

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“…The concerns include a lack of discipline on the part of e-learners, low retention rates (Allen & Seaman, 2013), and poor educational experiences for learners (Eom & Arbaugh, 2011). A stream of research identifying predictors of e-learning success has emerged over the past decade (Alshare, Freeze, Lane, & Wen, 2011;Arbaugh et al, 2009;Eom & Arbaugh, 2011;Eom, Ashill, & Wen, 2006;Martín-Rodríguez, Fernández-Molina, Montero-Alonso, & González-Gómez, 2015;Mashaw, 2012;Sun, Tsai, Finger, Chen, & Yeh, 2008;Xu, Huang, Wang, & Heales, 2014), and this stream suggests that there are several Critical Success Factors (CSFs) that must be managed effectively to fully realize the promise of e-learning.…”

Section: Introductionmentioning

confidence: 99%

The Determinants of Students’ Perceived Learning Outcomes and Satisfaction in University Online Education: An Update*

Eom

Ashill

2016

Decision Sci J Innov Edu

268

234

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A stream of research over the past decade that identifies predictors of e‐learning success suggests that there are several critical success factors (CSFs) that must be managed effectively to fully realize promise for e‐learning. Grounded in constructivist learning theories, this study advances previous work on CSFs in university online education. Structural equation modeling is applied to examine the determinants of students’ satisfaction and their perceived learning outcomes in the context of university online courses. The independent variables of motivation (intrinsic and extrinsic), student self‐regulation, dialogue (instructor‐student, and student‐student), instructor, and course design are examined as potential determinants of online learning outcomes. A total of 372 responses from students who have completed at least one online course at a university in the Midwestern United States were used to examine the structural model. Findings indicate that instructor‐student dialogue, student‐student dialogue, instructor, and course design significantly affect students’ satisfaction and learning outcomes. However, both extrinsic student motivation and student self‐regulation have no significant relationship with user satisfaction and learning outcomes. Finally, intrinsic student motivation affects learning outcomes but not user satisfaction. The findings suggest that course design, instructor, and dialogue are the strongest predictors of user satisfaction and learning outcomes.

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Section: Introductionmentioning

confidence: 99%

The Determinants of Students’ Perceived Learning Outcomes and Satisfaction in University Online Education: An Update*

Eom

Ashill

2016

Decision Sci J Innov Edu

268

234

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“…Studies that focus on instructors are less common, but those that have been done suggest that instructors are important contributors for positive online environments (Arbaugh, ; Ke, ; Kirschner & van Merrienboer, ). Even those studies that examine more than one of these groups either tend to consider only two of the three and find support for both (Alshare, Freeze, Lane, & Wen, ; Arbaugh, ; Eom, Wen, & Ashill, ; Rienties et al ., ) or are reflective of early experiences with online teaching and learning in more resource‐munificent environments (Martins & Kellermanns, ). Therefore, prior literature gives us an incomplete picture for prioritizing resource allocation.…”

Section: Introductionmentioning

confidence: 99%

System, scholar or students? Which most influences online MBA course effectiveness?

Arbaugh

2014

Computer Assisted Learning

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Considering the increasingly challenging resource environments in many business schools, this study examined whether course technologies, learner behaviors or instructor behaviors best predict online course outcomes so that administrators and support personnel can prioritize their efforts and investments. Based on reviewing prior online and blended management education literature, we hypothesized that instructor behaviors would be most predictive of online course outcomes. However, our study of 48 online Master of Business Administration courses found that although instructor behaviors (operationalized as teaching presence) was the strongest predictor of any of our three outcome variables (perceived learning), only student behaviors (operationalized as social presence) significantly predicted all three (course grades, perceived learning and delivery medium satisfaction). Technological characteristics, operationalized using variables from the technology acceptance model and media variety, predicted perceived learning (perceived ease of use) and delivery medium satisfaction (perceived usefulness). The paper provides recommendations for instructors and administrators based on the findings, specifically advocating for a balance between instructor and administrator involvement in course design, presentation and conduct. The paper concludes by describing research opportunities regarding the roles of social presence in and collaborative approaches to online learning in business schools.

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“…His aim was to identify and neutralize reasons of rejection for these technical items. There are several studies in the literature that have successfully extended TAM applications for e-learning technology (Alshare et al, 2011;Hu & Hui, 2012;Sharma & Chandel, 2013;Sharma et al, 2014). TAM has also been used to measure e-learning acceptance in developing countries, such as Jordan (Abbad et al, 2009;Al-Adwan & Smedley, 2013).…”

Section: Contribution Of This Paper To the Literaturementioning

confidence: 99%

Analysis of the Factors for the Successful E-Learning Services Adoption from Education Providers’ and Students’ Perspectives: A case study of Private Universities in Northern Iraq

Abdullah

Toycan

2017

EURASIA J MATH SCI T

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Electronic learning (e-learning) adoption has always been a challenge for developing countries which is often stunted by the facilitating conditions as well as the resistance of both the professionals and students. Two-step research methodology is applied in private Universities of Northern Iraq by utilizing a hypothesized model of technology acceptance model (TAM). First, the readiness factors were investigated through University staff by analysing 516 participants. As the second research objective, the intention of students is explored with 256 valid respondents in these Universities. Data were obtained from seven private Universities' staff and students via a paper based quantitative survey. Respondents were selected based on the convenience sampling method, where researchers visited Universities during the semester with permission of their administration bodies. The findings reveal that the lowest value was for human resource readiness factor. Cultural acceptance, both from education providers' and students' perspective, is quite crucial in order to have a sustainable e-learning applications. From a technical point of view, our findings also confirm the importance of the technological readiness and the main TAM constructs of perceived ease of use (PEOU) and perceived usefulness (PU). Therefore, management of the Universities need to ensure that selected systems adequately address these issues.

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The Impacts of System and Human Factors on Online Learning Systems Use and Learner Satisfaction

Cited by 46 publications

References 42 publications

The Determinants of Students’ Perceived Learning Outcomes and Satisfaction in University Online Education: An Update*

The Determinants of Students’ Perceived Learning Outcomes and Satisfaction in University Online Education: An Update*

System, scholar or students? Which most influences online MBA course effectiveness?

Analysis of the Factors for the Successful E-Learning Services Adoption from Education Providers’ and Students’ Perspectives: A case study of Private Universities in Northern Iraq

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