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首页> 外文期刊>Interactive Learning Environments >A multi-analytical approach to modeling of customer satisfaction and intention to use in Massive Open Online Courses (MOOC)
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A multi-analytical approach to modeling of customer satisfaction and intention to use in Massive Open Online Courses (MOOC)

机译：一种多分析方法来建模客户满意度和意图在大规模开放的在线课程中使用（MOOC）

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摘要

The purpose of this study is to classify the predictors of satisfaction and intention to use in Massive Open Online Courses (MOOC). Informed by a scientific literature review, this work poses a behavioral model to explain intention to use via various constructs. To this end, the authors have carried out a study through an online survey of Spanish Internet users. Two techniques were used: first, structural equation modeling (SEM) was approached to determine which variables had significant influence on MOOC adoption; in a second phase, the neural network model was used to rank the relative influence of significant predictors obtained by SEM. The analysis also shows that perceived satisfaction is affected by the quality of the course, its entertainment value and its usefulness. The latter variable also plays a major role when addressing user emotions. On the other hand, results from the neural network analysis confirmed many SEM findings and also provided a slightly different order of influence of significant predictors. The study provides an original focus in the study of perceived satisfaction and intention to use for MOOCs by extending the models proposed in previous research with regard to this emerging field.
机译：本研究的目的是将满足的预测因子分类为在大规模开放的在线课程（MOOC）中使用的满意度和意图。通过科学文献综述通知，这项工作构成了行为模型，以解释通过各种构造使用的意图。为此，作者通过对西班牙互联网用户的在线调查进行了研究。使用了两种技术：首先，接近结构方程建模（SEM）以确定哪些变量对MOOC采用产生了重大影响;在第二阶段，神经网络模型用于对SEM获得的显着预测器的相对影响进行排列。分析还表明，感知的满意度受到课程质量，娱乐价值及其有用性的影响。后一变量在解决用户情绪时也发挥着重要作用。另一方面，神经网络分析的结果证实了许多SEM发现，并且还提供了显着预测因子的影响略微不同。该研究提供了一种原始焦点，在研究中，通过在此新兴领域延长先前研究中提出的模型来研究MoOCS的感知的满意度和意图。

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