Assessing students’ continuance intention in using multimedia online learning

Abstract

The study aims to assess the students' continuance intention (SCI) in using multimedia online learning including students' perceived usefulness (SPU), students' ease of use (SPE), and students' flow experience (SFE). There is no, so far, assessment of students' activity in using multimedia online learning within the extended Technology Acceptance Model (TAM).There were 523 students as the samples. They learned and examined the content of online English learning resources as well as multimedia aspects.TAM was used to predict the SCI. The results indicate that the students' intentions are influenced positively by perceived usefulness, ease of use, and the flow experience. The SPU influenced more students' motivation than SPE. It is recommended that designers of online learning should be more specific in determining target users. The online learners' skill on ICT, pedagogical principles, and technological barrier in different region are among the determinant aspects for receiving positive enduring intention.

The Impact of e-NPQEL on the Continuance Intention of Using e-Training among Aspired School Leaders in Malaysia

“…In other words, the influence of technical support and course design on promoting continuance intention of using e-training can be strengthen if the e-training program perceived as useful by the participants [26][28].This result showed that perceived usefulness has important influence in both models i.e. direct or indirect effect model and supported by many previous studies on online learning [19][20][21][22][23][24][25] that perceived usefulness of e-learning product has significant role in influencing the willingness of users to continue using the product. Besides having a good technical support and well designed, the online platform for learning as well as training must perceived as useful by the participants in motivating them to continue using it or other platforms like it.…”

“…Many of the results of previous e-learning studies such as on cloud e-learning application [19], Massive Open Online Course (MOOC) [20], Mobile web 2.0 [21], multimedia online learning [22], etc. suggested that learners' willingness to continue using the online learning platform depends on how they feel and enjoy the program [19][20].…”

Application of Feed Forward Backpropagation Neural Network in Monthly Rainfall Prediction

“…The result of RAPS analysis are presented in Table 1 respectively. [30]. The Goodness-of-fit statistic of selected ANN models during calibration and validation is presented in Table 2.…”

Stock Price Forecasting of E-Commerce Company Using Feedforward Backpropagation Neural Network

“…FFBNN is comprised of various computational components portrayed as neurons. These are sorted out in three layers: the input layer which contains the information unit that gets data from external and will be identified with the system, at least hidden layer of calculation neurons where the information is prepared and an output layer of calculation neurons where the outcomes for given sources of info are created [22]. The back-propagation consists of two passes: a forward propagation and backward propagation.…”