**2. Literature review**

### **2.1 Research on students' technology acceptance of LMS**

The use of LMS in online learning become famous during COVID-19. Many researchers are trying to investigate features that influence learners' and educators' degree of acceptance among those technologies [7]. The literature has shown that the Technology Acceptance Model (TAM) is the most adopted theory to investigate the students' technology acceptance. Davis [8] suggested the TAM examine the determinants of users' acceptance for using the technology. Originally, TAM postulated that *perceived usefulness* and *perceived ease of use* are two main factors associated with user acceptance. Perceived usefulness is the degree to which the user believes that it would enhance their performance by using a specific system. Perceived ease of use refers to the degree to which the user believes that it would cost less effort by using a specific system. TAM also posits that the actual use of a specific system is determined by *behavioral intention to use*, determined by both perceived usefulness and *attitude toward using technology*.

After the publication of Davis [8], in several studies, it is argued that the attitude toward the use of technology would be removed to simplify the model without losing the explaining power [9, 10]. Therefore, the extended model, TAM2 [11], and another subsequent model, UTUAT [12], removed the attitude toward using technology.

Šumak, Heričko [13] conducted a meta-analysis to summarize the TAM-related studies. They found that perceived usefulness and ease of use are two significant factors affecting users' intention to use e-learning systems. For instance, Brunel University offered a series of online courses in LMS and examined the factors of increasing the use of the platform [14]. They found that both the perceived ease of use and the perceived usefulness have been significantly and positively associated with using the platform. During COVID-19, Siron, Wibowo [15] also found similar results. They used TAM to evaluate the use of e-learning platforms during COVID-19. They found that both the perceived usefulness and the perceived ease of use are the major factors affecting students' intention to use e-learning at several state universities in Indonesia during the pandemic.

However, using the actual usage of technology is not enough to capture the whole picture of their acceptance behavior. Some of the acceptance is not behavioral but mental and abstract, which is considered engagement. Also, academic disciplines would have different emphases in integrating technology into their teaching and learning; thus, it would influence students' acceptance. In the following sections, therefore, we will discuss the relationship between academic discipline, student engagement, and the actual use of LMS.

### **2.2 Engagement and LMS**

Students' academic performance is primarily influenced by student engagement [16]. More recently, researchers have begun to conceptualize student engagement as a multidimensional phenomenon. The review by Fredricks, Blumenfeld [17] identified

### *The Impact of Academic Discipline on Student's Engagement in Online Learning: An Extension… DOI: http://dx.doi.org/10.5772/intechopen.102071*

three dimensions of engagement: behavioral, emotional, and cognitive. Behavioral engagement refers to students' levels of involvement in the learning activity, including attention, participation, interaction with students and teachers, and spent effort and time. Emotional engagement is defined as students' presence of positive emotional reactions to learning in general, such as value, identity, interest, and happiness, and absence of adverse emotional responses such as anxiety. Cognitive engagement centers on students' self-regulation strategies to employ sophisticated rather than superficial learning strategies in their learning processes. Besides, another review by Kahu [18] used the integrative framework to emphasize engagement as a state influenced by a wide array of teacher and student factors. The framework also acknowledges that students learn through being engaged with their study; thus, learning is not only acquiring skills and knowledge. Build on the previous literature, Reeve [19] also proposed agentic engagement, which is defined as students' attempt to contribute to the learning environment to create for themselves a more motivationally supportive learning environment.

Several studies have found student engagement to be an indicator of students' higher academic achievement. Carini, Kuh [20] found that improvements in students' engagement improve their learning outcomes. Kahu and Nelson [21] assessed students' emotional and cognitive engagement and found that emotional and cognitive engagement can predict academic success. However, online/remote learning has been famous recently, and the question may raise whether students engaging in LMS would benefit academic achievement. For example, Wang [22] examined the relationship between behavioral engagement on Moodle and academic performance (defined as course grade) in a university in Taiwan. Wang found that engagement in problem-solving-related learning activities in Moodle has a direct effect on academic performance. In the studies of Hsiao, Huang [23] and Lee, Park [24], they defined academic performance by GPA and self-developed academic capability measurement, respectively. They also found that behavioral engagement positively correlates with academic performance. This study hopes to extend their findings by assessing whether another type of student engagement, cognitive, emotional, and agentic engagement, also predicts academic performance.

### **2.3 Academic discipline and LMS**

Since the technology advanced, students gradually developed "the information-age mindset" over the three decades [25]. In the meantime, the Learning Management System (LMS) was developed in the 2000s to create a virtual learning environment and facilitate the implementation of online learning (Oblinger & Kidwell, 2000). Since the LMS is a teaching and learning tool, the discussion of LMS has to be informed by pedagogical considerations. As early as 2000, researchers were beginning to identify the influence of LMS on teaching and learning to form the theoretical framework. Coates, James [26] identified some practical problems when the teacher used LMS; one of the dominant problems is that LMS is only used to transmit the text. Teachers did not modify their teaching pedagogy; instead, they employed their traditional teaching approaches when using the LMS [27]. Sadaf, Newby [28] also found similar results in the group of pre-service teachers that use of the LMS would predict the intention to use LMS.

On the contrary, the successful use of LMS depends on the integration between LMS and the subject (including teaching material and learning objective). Research also suggests that different courses emphasize different learning outcomes by providing discipline-specific learning environments [29, 30]. For example, teachers from soft fields tend to focus on facilitating and developing students' ability to discuss alternative and critical perspectives [31]. Those in hard fields tend to focus on having students memorize and apply essential concepts [32]. Therefore, courses in the same academic discipline would have similar learning objectives; different academic disciplines would have a different level of integration with LMS. Smith, Torres-Ayala [33] investigate the academic discipline as a factor in the instructional design of e-learning. They found that mathematics and nursing/healthcare emphasize learning outcomes and utilization of e-learning tools differently. For example, mathematics focused on the abstract concept of mathematics, whereas nursing/healthcare focused on authentic assessment, which facilitates students to apply the skill and knowledge in real life. White and Liccardi [34] also used Biglan categorization [35] to categorize academic disciplines based on the degree of consensus about knowledge within them. Their categorization classifies disciplines into soft (a low degree of agreement) and hard fields (a high degree of agreement). They found that soft and hard fields have a difference in using LMS tools, which soft fields utilize discussion and simulated virtual environment (role play). In contrast, hard fields utilize real-time visualization tools and assessment.
