 |
Asia-Pacific Forum on Science Learning and Teaching, Volume 20, Issue 2, Article 5 (Jun., 2021) |
The validity of e-content and e-assessment modules were analyzed using Item Content Validity Index (I-CVI) (Polit & Beck, 2006), while the reliability of both modules was analyzed using the internal consistency method. Students' perceptions of the Chemistry MOOC were analyzed using Statistical Packages for Social Sciences (SPSS) version 20 to get the mean scores and standard deviations. The data will be presented according to the constructs and sub-constructs in the perception questionnaire.
Four chemistry content experts checked the content validity of both e-content and e-assessment modules. Comments and suggestions from the experts were followed, and corrections were done accordingly. Hence, all the experts agreed on the relevance of all content in both modules giving the results of 1.00 for all I-CVIs in the content validity evaluation form. Lynn (1986) suggested that I-CVI should be 1.00 when less than six experts are evaluating the content validity. On the other hand, the reliability index of the e-content and e-assessment module is 0.94 (Hamid et al., 2021) and 0.97 (Kamarudin et al., 2020), respectively. These indexes indicate that both modules show high reliability (Hair, Black, Babin, Anderson & Tatham, 2006) because students can follow the content in the e-content module and answer the questions in the e-assessment module.
The perception of students on the Chemistry MOOC was analyzed based on instructional design elements, acceptance and usage barrier. Table IV shows the mean scores and standard deviations for the students’ perceptions of Chemistry MOOC according to the constructs and sub-constructs.
Table IV. Students’ Perception on Chemistry MOOC by Constructs and Sub-Constructs
No.
Construct
Sub-Construct
Mean
Standard Deviation
1.
Instructional design elements
Course Information (CI)
4.05
0.63
Course Resources (CR)
3.82
0.74
Active Learning (AL)
3.98
0.68
Monitoring Learning (ML)
4.01
0.70
Interaction (IR)
3.68
0.89
Total
3.91
0.62
2.
Acceptance
Performance Expectancy (PE)
4.05
0.65
Effort Expectancy (EE)
3.97
0.66
Social Influence (SI)
4.00
0.71
Facilitating Conditions (FC)
3.30
0.92
Total
3.83
0.52
3.
Usage barrier
2.80
0.92
4.
Overall perception
3.87
0.53
There are 20 items in the instructional design elements with four items in each sub-construct: course information (CI), course resources (CR), active learning (AL), monitoring learning (ML) and interaction (IR). The CI sub-construct obtained the highest mean score (M = 4.05, SD = 0.63) while IR recorded the lowest mean score (M = 3.68, SD = 0.89).
The motivation issue was reported as a challenge in enrollment or retention of MOOC learning (Henderikx et al., 2018; Siemens, 2013; Wang & Baker, 2015). The respondents in this study were briefed about the Chemistry MOOC, and their participation in this study was based on a voluntary basis. Hence, they have no intrinsic motivation (Ryan & Deci, 2000) to use the MOOC, and their registration to the Chemistry MOOC might be due to curiosity (Davis, Dickens, Leon Urrutia, Sánchez-Vera, & White, 2014; MOOC @ Edinburgh, 2013; Sukhbaatar et al., 2018; Wang & Baker, 2015) or due to the recommendation by the researchers (Sukhbaatar et al., 2018). This also explains the identification type of social influence (Kelman, 1958) because of the identity of the researchers as their lecturers.
In order to extend learners’ engagement to the MOOC and ensure retention, MOOC designers need to make sure that they have quality instructional design elements (Atiaja & Proenza, 2016) in the MOOCs. Previous studies showed that course content (Atiaja & Proenza, 2016; Henderikx et al., 2018; Hone & El Said, 2016; Kop, 2011; Wang & Baker, 2015) and interaction (Atiaja & Proenza, 2016; Henderikx et al., 2018; Hervatta, 2016; Hone & El Said, 2016; Murray, 2014; Kop et al., 2011) are important features in designing MOOCs to ensure retention and decrease the dropout rate.
Course content in MOOCs enables the learners to read, watch and play, so they normally comprised supporting notes, informative videos, discussion forums, interactive quizzes and interesting games (Murray, 2014; Wang & Baker, 2015). Content presented in MOOC must be relevant, reliable, innovative and interactive. Videos presented in the MOOC must be segmented into a short chunk (Guo, Kim & Rubin, 2014; Hone & El Said, 2016) to avoid boredom and ensure engagement. Hence, the duration of the video must not be more than six minutes (Guo et al., 2014; Schmoller.net, 2015) as it is the maximum duration for learner engagement. Besides that, MOOC designers must be creative in designing the content by selecting techno-learning tools, such as augmented reality, virtual reality, gamification, and social media use (Atiaja & Proenza, 2016).
Social interaction in MOOCs involves three main components: content, learner and instructor. The three components can interact in multi ways such as content-learner, content-instructor, content-content, learner-instructor, learner-learner and instructor-instructor to promote deep and meaningful learning (Anderson & Garrison, 1998). According to the equivalence theorem (Anderson, 2003), deep and meaningful formal learning is supported as one of the three forms of interaction (student-teacher; student-student; student-content) is at a high level (p.4). The teacher’s presence supports cognitive presence. Facilitator-learner and learner-learner interaction are important to enhance learning (Kop et al., 2011). The virtual community of practice (Hervatta, 2016) in MOOC is learning together by collaborative learning. They share thoughts and knowledge, evaluating other’s ideas and monitoring their peers’ progress. However, a large number of forum discussion participants make the instructors or facilitators unable to respond immediately (Hervatta, 2016), causing poor retention or dropout among the learners. Hence, MOOC designers must improve the quality of interaction through high levels of automation, allowing the optimization of instructors’ time with tools that promote scalability of a need of a huge number of learners (Atiaja & Proenza, 2016).
The acceptance construct includes four sub-constructs: performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC). The overall mean score for acceptance construct is 3.83 (SD = 0.52). The sub-construct with the highest mean score in the acceptance construct is PE (M = 4.05, SD = 0.65) while the sub-construct with the lowest mean score is FC (M = 3.30, SD = 0.92).
The acceptance constructs aimed to examine undergraduate students’ acceptance of the new technology, specifically Chemistry MOOC, in this study. Acceptance constructs in the perception questionnaire are based on The Unified Theory of Use and Acceptance of Technology (UTAUT) (Venkatesh, Morris, Davis & Davis, 2003). PE is defined as the degree to which an individual believes that using the new technology will help them attain gains in job performance (Venkatesh et al., 2003). The high mean score in this study is in line with Daud and colleagues’ study (2017), where each of their items in the PE constructs gained a high mean score value. The highest mean score in PE sub-construct supports Venkatesh et al. (2003) output where PE is the strongest predictor to the behaviour intention of technology acceptance. Students believed that they could study Chemistry course more effectively by using this MOOC. By reading the notes in the PowerPoint slides and watching the informative videos, students can enhance their learning about the related chemistry concepts. Hence, it can help them achieve better understanding and gain better result in the test.
EE is the degree of ease associated with the use of new technology (Venkatesh et al., 2003). This sub-construct is similar to the Perceived Ease of Use (Davis, 1989) and convenient to use (Sukhbaatar, Choimaa & Usagawa, 2018). Items in this sub construct asked about whether it is easy or difficult to use Chemistry MOOC. Students’ responses showed that the MOOC is quite easy to use, as reflected by the moderately high mean score. All the content in the MOOC were arranged systematically in the e-content module and e-assessment module. Learners were shown the advanced organizer (Ausubel, Novak & Hanesian, 1978), which acts as the cognitive instructional tool to help learners organize the information presented to them. Mental scaffolding provided by the advance organizer eases the process of learning (Lee, Rohadi, Alfana, 2016; Mohammadi, Moenikia & Zahed-Babelan, 2010).
SI is related to the degree to which an individual perceives that it is important for others to believe they should use the new technology (Venkatesh et al., 2003). Important others in this study refer to teachers, lecturers, parents and friends. According to Social Influence Theory, important others influence an individual’s beliefs, attitudes, and behaviours via three processes: compliance, identification, and internalization (Kelman, 1958). In this study, undergraduate students’ behaviour on using Chemistry MOOC is through the identification process. They adopt the behaviour because it is associated with the desired relationship, not because of avoiding punishment (compliance) or intrinsically rewarding (internalization) (Kelman, 1958). The mean score for SI in this study is the second-highest in the acceptance construct compared to other studies in Malaysia (Azmi & Rasalli, 2018; Daud et al., 2017).
FC refers to the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the new technology (Venkatesh et al., 2003). This sub-construct gained the lowest mean score, in line with Daud and colleagues’ study (2017). However, Azmi and Razalli (2018) reported the second-highest mean score for this sub-construct. FC in this study refers to the equipment needed, knowledge of students and assistance from others when students were using Chemistry MOOC. Learning via MOOC in the OpenLearning platform is considered a new experience among undergraduate students in the related university. Students were only briefed once before they started to explore the Chemistry MOOC. Hence, they may face difficulty asking for help if they need an explanation to clear their doubt in using MOOC. This supports the finding for the usage barrier about knowledge to use MOOC in the next construct.
The overall mean score for the usage barrier is quite low, with a mean score of 2.80 (SD = 0.92). The item related to the barrier factor on Internet coverage (item A13) gained the highest mean score (M = 3.98, SD = 1.13). The usage barrier factor on the content of Chemistry MOOC (item A18) recorded the lowest mean score (M = 2.52, SD = 1.15). The detailed information on the usage barrier items with their related mean scores and standard deviations are displayed in Table V.
Table V. Mean Score and Standard Deviation for Usage Barrier Construct
No.
Item
Mean
Standard Deviation
I am less involved in MOOC learning because
A13.
poor Internet / Wifi coverage.
3.98
1.13
A14.
lack of knowledge in the use of MOOC.
3.20
1.16
A15.
lack of skill to use MOOC.
3.04
1.14
A16.
equipment to use MOOC is incomplete.
2.99
1.18
A17.
no self motivation to learn to use MOOC.
3.04
1.07
A18.
material in MOOC is not attractive.
2.52
1.15
A19.
no standard allocation marks in MOOC.
2.82
1.09
A20.
courses to be followed are not offered by MOOC.
2.81
1.12
Self-paced learning, self-directed learning, independent learning, life-long learning and collaborative learning are inter-correlated when a student learns a certain course in the MOOC platform. MOOC designers and instructors should provide sufficient content to ensure that learners can have a quality learning experience in the MOOC. However, statistics showed that only a small portion of learners completed the MOOC due to some retention barriers (Atiaja & Proenza, 2016; Hone & El Said, 2016; Siemens, 2013; Wang & Baker, 2015).
Major problems faced by the respondents in this study when using Chemistry MOOC are poor Internet/wifi coverage problem, lack of knowledge or skill in using MOOC (Henderikx, Kreijns & Kalz, 2018; Kop et al., 2011; Sukhbaatar et al., 2018) and no self-motivation to learn to use MOOC. These results support previous research in Malaysia where Internet problems and self-motivation are the main barriers in using MOOC (Azmi & Rasalli, 2018). The location of the study causes Internet problem because Internet quality is not so good in the town area compared to the city. Students need to connect to the university’s wifi to access the MOOC. A huge number of wifi users reduces the quality and speed of the connection. This problem does not apply to Daud and colleagues’ study (2017) because their respondents were from a city with good quality Internet and wifi coverage. Besides, the respondents in Hone and El Said’s study (2016) stated that they had difficulty loading the long video due to connectivity issues, especially from mobile devices. Hence, learning with MOOC will be more convenient when computers or laptops with strong Internet/wifi connections are used instead of mobile devices.
