Predictors of web-student performance: the role of self-efficacy and reasons for taking an on-line class

Wang, Alvin Y.; Newlin, Michael H.

doi:10.1016/s0747-5632(01)00042-5

Cited by 182 publications

(127 citation statements)

References 27 publications

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“…Learners with high self-efficacy are more confident in accomplishing e-learning activities and improving their satisfaction (19,35) . Wang and Newlin (2002) (36) , from research on 122 students, conclude that students with higher self-efficacy are more inclined to adopt network-based learning and earn significantly better final grades. Liaw (2008) (35) stated that the most critical factor that positively affected e-learners' satisfaction toward e-learning and e-learning usage was perceived self-efficacy of using e-learning.…”

Section: Discussionmentioning

confidence: 99%

Factors Affecting Nursing Student’s Satisfaction with E- Learning Experience in King Khalid University, Saudi Arabia

Ali

2012

IJLD

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Background; the use of information technology and the internet as teaching and learning tool is rapidly expanding into today's learning environments. Education institutions in the Kingdom of Saudi Arabia (KSA) are preparing students for a rapidly changing information and technology driven world. The KSA needs graduates who are ready for the workplace and who have a high level of knowledge and confidence in the use of technology to help them in their lifelong learning. Since e-learning is conducted using the Internet and World Wide Web, the learning environment becomes more complicated. Students' initial perceived satisfaction with technology-based e-learning will determine whether they will use the system continually. So this study aimed to assess perceived e-learner satisfaction and investigate the preceding factors influence on nursing students' satisfaction with e-learning experience in King Khalid University. Subjects: A convenience sample of 135 female nursing students affiliated to University Center for Female Studies, King Khalid University was enrolled in this study. Tool: data were collected by using three tools. The first was concerned with collecting data related to sample characteristics, the second concerned with identifying the factors that may affect the e-learner satisfaction with e-learning. The third concerned with measuring the learner satisfaction with e-learning. Results; revealed that 61.5% of participant students were unsatisfied with their e-learning experience and learner attitude towards computer, learners' computer anxiety, e-learning course flexibility, e-learning course quality, technology quality, perceived usefulness, perceived ease of use, diversity in assessment, and learner perceived interaction with others were the critical factors affecting learners' perceived satisfaction. Recommendation &implications: Helping students build their confidence in using computers

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Section: Discussionmentioning

confidence: 99%

Factors Affecting Nursing Student’s Satisfaction with E- Learning Experience in King Khalid University, Saudi Arabia

Ali

2012

IJLD

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“…Thus, in their study Joo, Bang and Choi (2000) found that self-efficacy was not significant for performance prediction in a web-based test, but it was significant in the context of a written test. Lee and Witta (2001) used a small sample -16 students and thus, the generalisability of results is questionable; also their result about self-efficacy for course content and performance is contradicted by results obtained by Wang and Newlin (2002): self-efficacy for course content correlated with the final exam performance. In his research DeTure (2004) had a sample of self-selected students; also, the statistical mode for online technologies self-efficacy for his sample was the highest score on the scale he used for measuring it, which leads to a shadow of doubt about the variety of data and thus, to the results of the regression prediction.…”

Section: Social Cognitive Learning Theory In E-learning Researchmentioning

confidence: 97%

Assessment of Motivation in Online Learning Environments

Cocea

2006

Lecture Notes in Computer Science

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“…Correlation analyses have been applied together to predict web-student performance in on-line classes [44], to predict a student"s final exam score in online tutoring [45] and for predicting high school students" probabilities of success in universities [46]. …”

Section: Overview Of Literaturementioning

confidence: 99%

Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression

Oyerinde¹,

Chia²

2017

IJCA

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Learning Analytics is an area of Information Systems research that integrates data analytics and data mining techniques with the aim of enhancing knowledge management and learning delivery in education management..The current research proposes a framework to administer prediction of Students Academic Performance using Learning Analytics techniques. The research illustrates how this model is used effectively on secondary data collected from the Department of Computer Science, University of Jos, Nigeria.Multiple Linear Regression was used with the aid of the Statistical Package for Social Sciences (SPSS) analysis tool. Statistical Hypothesis testing was then used to validate the model with a 5% level of significance.

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Predictors of web-student performance: the role of self-efficacy and reasons for taking an on-line class

Cited by 182 publications

References 27 publications

Factors Affecting Nursing Student’s Satisfaction with E- Learning Experience in King Khalid University, Saudi Arabia

Factors Affecting Nursing Student’s Satisfaction with E- Learning Experience in King Khalid University, Saudi Arabia

Assessment of Motivation in Online Learning Environments

Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression

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