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Factors Influencing Satisfaction and Perceived Learning Performance in Blended Learning Using The Chaoxing Learning Platform
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Background and Aim: Despite the success of blended learning, student satisfaction, and perceived learning performance remain key indicators of its effectiveness. While existing studies have explored these factors, most treat technology platforms generically, with limited focus on specific platforms. Therefore, this study investigates the factors influencing satisfaction (SAT) and perceived learning performance (PLP) in blended learning, specifically using the Chaoxing Learning Platform.
Materials and Methods: A quantitative research design was employed using a structured questionnaire, grounded in blended learning theories and the Technology Acceptance Model. The survey targeted students at the School of Intelligent Manufacturing, Zhanjiang University of Science and Technology. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were used to analyze the data from 493 valid responses.
Results: The analysis revealed significant positive relationships between the factors and satisfaction. Perceived Ease of Use (PEU), Perceived Usefulness (PU), Social Presence (SP), Teaching Presence (TP), and Cognitive Presence (CP) all contributed to higher satisfaction levels. Additionally, satisfaction was found to be a strong predictor of perceived learning performance, indicating that satisfied students believed they had performed better academically.
Conclusion: This study confirmed five key factors influencing student satisfaction in blended learning using the Chaoxing Learning Platform. It demonstrated a strong positive relationship between satisfaction and perceived learning performance, highlighting the central role of satisfaction in determining educational outcomes in blended learning environments. However, the study's focus on a single institution may limit the generalizability of the findings. Future research should explore diverse contexts and measure actual learning outcomes for a more comprehensive understanding.
Dr. Ken Institute of Academic Development and Promotion
Title: Factors Influencing Satisfaction and Perceived Learning Performance in Blended Learning Using The Chaoxing Learning Platform
Description:
Background and Aim: Despite the success of blended learning, student satisfaction, and perceived learning performance remain key indicators of its effectiveness.
While existing studies have explored these factors, most treat technology platforms generically, with limited focus on specific platforms.
Therefore, this study investigates the factors influencing satisfaction (SAT) and perceived learning performance (PLP) in blended learning, specifically using the Chaoxing Learning Platform.
Materials and Methods: A quantitative research design was employed using a structured questionnaire, grounded in blended learning theories and the Technology Acceptance Model.
The survey targeted students at the School of Intelligent Manufacturing, Zhanjiang University of Science and Technology.
Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were used to analyze the data from 493 valid responses.
Results: The analysis revealed significant positive relationships between the factors and satisfaction.
Perceived Ease of Use (PEU), Perceived Usefulness (PU), Social Presence (SP), Teaching Presence (TP), and Cognitive Presence (CP) all contributed to higher satisfaction levels.
Additionally, satisfaction was found to be a strong predictor of perceived learning performance, indicating that satisfied students believed they had performed better academically.
Conclusion: This study confirmed five key factors influencing student satisfaction in blended learning using the Chaoxing Learning Platform.
It demonstrated a strong positive relationship between satisfaction and perceived learning performance, highlighting the central role of satisfaction in determining educational outcomes in blended learning environments.
However, the study's focus on a single institution may limit the generalizability of the findings.
Future research should explore diverse contexts and measure actual learning outcomes for a more comprehensive understanding.
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