the facilitators (Blasco-Arcas et al. 2013). The interaction with fellow students is usually in the
form of group discussion, and class participation; these types of activities can improve active and
high-order learning (Crouch & Mazur 2001). Active learning that involves collaboration with other
students has been noted to improve students’ learning experience (Blasco-Arcas et al. 2013) and
possibly student satisfaction. This is due to the possibilities that interaction may enable the students
to think critically and look for alternative answers, leading to deeper knowledge processing activity
(Blasco-Arcas et al. 2013). Hence, it can be hypothesized that:
H4: The higher the level of interactivity during online learning, the higher the learning
satisfaction.
2.5 Technological factors
Instructional support that students receive can mostly come from the course instructor and the
institutions, but technology can be used to provide support to individual students and instructional
contexts (Chen et al. 2010). The tools that are available online to the facilitators and learners can
influence the online learning process (Deshwal et al. 2017). A previous study on the experience
of online learning through massive open online courses (MOOCs) reported that internet access
quality played an important factor in MOOC users’ decision to continue participating in online
courses. (Nurhudatiana et al. 2019). Therefore, it can be hypothesized that:
H5: Availability of the technological facilities will have a positive influence on learning
satisfaction
2.6 Supporting factors
Online learning enables the learners to process learning materials based on their individual preferences at any time and from any place; they may select and examine material from a large pool
of information (Artino & Stephens 2009; Narciss et al. 2007). Universities generally provide additional services such as a hotline or email address that students can contact should they encounter
any problem during the online learning situation. Pieces of advice, counseling, and other facilities
are also provided to improve the students’ learning experience. A face-to-face consultation that
complements online consultation facilities can also enable students to solve problems that may
arise during online learning. Therefore, it can be hypothesized that:
H6: The availability of supporting factors has a positive and significant influence on learning
satisfaction.
3 METHOD
This research utilizes the quantitative method with survey as the data collection method. The
survey was distributed to undergraduate and Master’s degree students who learn using an online
learning method. There are seven variables investigated in this research: learner characteristics,
instructor characteristics, course suitability, interactivity, technological factors, supporting factors,
and learning satisfaction.
The data analysis was conducted using PLS SEM software. Construct reliability and validity
as well as discriminant validity, were checked based on the Cronbach’s Alpha’s values, Average
Variance Extracted, Composite Reliability, and Fornell-Lackner criterion. All the indicators were
found to be valid and reliable.
4 RESULTS AND DISCUSSIONS
In total, 118 respondents participated in this study, with 100 usable responses which were further
analyzed using PLS SEM software. Out of those 100 respondents, 31 were females (31%), 65
303
form of group discussion, and class participation; these types of activities can improve active and
high-order learning (Crouch & Mazur 2001). Active learning that involves collaboration with other
students has been noted to improve students’ learning experience (Blasco-Arcas et al. 2013) and
possibly student satisfaction. This is due to the possibilities that interaction may enable the students
to think critically and look for alternative answers, leading to deeper knowledge processing activity
(Blasco-Arcas et al. 2013). Hence, it can be hypothesized that:
H4: The higher the level of interactivity during online learning, the higher the learning
satisfaction.
2.5 Technological factors
Instructional support that students receive can mostly come from the course instructor and the
institutions, but technology can be used to provide support to individual students and instructional
contexts (Chen et al. 2010). The tools that are available online to the facilitators and learners can
influence the online learning process (Deshwal et al. 2017). A previous study on the experience
of online learning through massive open online courses (MOOCs) reported that internet access
quality played an important factor in MOOC users’ decision to continue participating in online
courses. (Nurhudatiana et al. 2019). Therefore, it can be hypothesized that:
H5: Availability of the technological facilities will have a positive influence on learning
satisfaction
2.6 Supporting factors
Online learning enables the learners to process learning materials based on their individual preferences at any time and from any place; they may select and examine material from a large pool
of information (Artino & Stephens 2009; Narciss et al. 2007). Universities generally provide additional services such as a hotline or email address that students can contact should they encounter
any problem during the online learning situation. Pieces of advice, counseling, and other facilities
are also provided to improve the students’ learning experience. A face-to-face consultation that
complements online consultation facilities can also enable students to solve problems that may
arise during online learning. Therefore, it can be hypothesized that:
H6: The availability of supporting factors has a positive and significant influence on learning
satisfaction.
3 METHOD
This research utilizes the quantitative method with survey as the data collection method. The
survey was distributed to undergraduate and Master’s degree students who learn using an online
learning method. There are seven variables investigated in this research: learner characteristics,
instructor characteristics, course suitability, interactivity, technological factors, supporting factors,
and learning satisfaction.
The data analysis was conducted using PLS SEM software. Construct reliability and validity
as well as discriminant validity, were checked based on the Cronbach’s Alpha’s values, Average
Variance Extracted, Composite Reliability, and Fornell-Lackner criterion. All the indicators were
found to be valid and reliable.
4 RESULTS AND DISCUSSIONS
In total, 118 respondents participated in this study, with 100 usable responses which were further
analyzed using PLS SEM software. Out of those 100 respondents, 31 were females (31%), 65
303