**4.4 Characteristics of students and the field of study**

10 E-Learning – Organizational Infrastructure and Tools for Specific Areas

through the following obstacles in accepting e-learning technology: integration with other systems in organization, incompatibility in technology use and existing work practice, the problem of integrating technology and existing practice in traditional classrooms. Moscinska and Rutkowski (2011) confirm the attributes: flexibility and "user-friendly" which influence the acceptance and use of e-learning systems, and present technical characteristics of the e-

It is well known that different beliefs about the value of e-learning encourage teachers to apply e-learning technology on different levels (Renzi, 2008). The perception of e-learning usefulness is formed under the influence of intrinsic and extrinsic factors, and numerous authors list: belief in institution's competitiveness, increased number of enrolled students (Osika et al., 2009), facilitated student cooperation in educational context (Lofstrom & Nevgi, 2008), communication and additional support for students, distribution of study material, the ease of administration, the value of collaborative online work (Keller, 2009), belief in information sharing, automated activities of the learning process, value of social learning as an important part of learning in general (Renzi, 2008). Successful pedagogical use of e-learning technology depends on teacher's attitude towards technology Mihhailova, 2006). Research results show that teacher's attitude has been studied more from the technical

Teacher's personality is a powerful intrinsic motivational factor which influences e-learning technology acceptance. It represents a set of characteristics which make every teacher

The most commonly studied teacher's features are: self-efficacy and anxiety, more often approached from the technical aspect. Computer anxiety is closely connected to the teacher's attitude, author suggests the possibility of understanding computer self-efficacy as a construct of perceived ease of use (Timothy, 2009). Malik et al. (2010) mention teacher's organizational commitment as an important factor in quality teaching process and Baia (2009) confirmed the influence of commitment to the pedagogical quality on the e-learning

Teacher's personality is evident through teaching and learning style applied in the education process and which includes certain teaching methods and techniques, and represents a mechanism responsible for quality conveyance of the educational content influencing the student success (Grasha, 1994). Changes in the teacher's belief, attitude and values influence the teaching style. Lucas & Wright (2009) predicts the possibility of connection between teaching style and the attitude towards the use of e-learning technology. Dugas (2006) determined a slight connection of teaching style and the degree of

Apart from the teacher's personality, great importance lies in the demographic and situational variables. The experience with LMS and computer experience are strong motivators in teachers' acceptance of e-learning (Gautreau, 2011). In his research Timothy (2009) did not find significant link between attitude, age and gender, which contradicts the hypothesis by Houtz and Gupta, Cully et al. (Timothy, 2009), he found a significant difference in the attitudes of the female computer users. However, Marwan and Sweeney (2010) point out to a significant connection between gender, department and academic title with the teacher's attitude towards e-learning technology. Academic title and years of work

unique in education process and it is strongly influenced by the surroundings.

and less from the pedagogical aspect (Mahdizadeh et al., 2008).

learning system.

**4.3 Teacher's personality**

technology acceptance.

innovation with accepting e-learning technology.

While creating virtual learning environment the choice of e-learning technology depends on pedagogical model, and its choice is influenced by: field of study characteristics and characteristics of the students, which both represent situational factors.

Kanuka (2006) stresses out the importance of the following factors: value and culture within certain discipline, understanding unique problems within each field of study as crucial elements when designing learning environment. Keller (2009) proved that the culture within the discipline represents the obstacle of e-learning application. Before using the virtual learning environment, reasons for the use of e-learning technology need to be defined, where, according to Rebman Jr et al. (2004), certain physical educational activities require classical approach in a traditional classroom. Knowledge is hierarchically organized and therefore it is essential to define learning outcomes within each course using knowledge taxonomy, and based on the outcomes define educational strategies and student activities (Donnelly, 2005). Numerous models of instructional design can be found in literature, however, Donnelly (2005) emphasizes that teachers mostly use non-systemized personal models because the planning of the educational structure requires: time, commitment and careful systematic approach. One characteristic of the study object (any segment of the digital study material) is: multiple use in different educational contexts; however, Parrish (2007) brings up the problem of intellectual property which limits the distribution of the study objects. Learning happens in predictable patterns that can me modeled using algorithms, which influences the development of the intelligent tutoring systems (Parrish, 2007).

Student characteristics can act as motivators for application and development of e-learning in teaching, and student capabilities (Osika et al., 2009) can be an obstacle in using elearning technology in teaching. Each student has his or her own learning style and there are various instruments that can measure those styles (Grasha, 1994). A very important student characteristic is motivation; a motivated student shows greater interest in information, the quality of information, confidence when accessing information and technology, satisfaction in work (Kumarawadu, 2011). Colorado and Eberle (2009) conclude that the level of student self-regulated learning is related to demographic data: gender, status, certify cates, completed degree of education and characteristics of the self-regulated learning: learning strategy, critical thinking, knowledge sharing, asking for help, where students who have graduated have a higher level of self-regulated learning.

On the other hand, the number of students in virtual classrooms and complexity of education scenario influence the success of virtual learning process (Salmon, 2000). Perception of student characteristics can be a part of the construct: facilitating circumstances (Ø. Sørebø & A. M. Sørebø, 2008).
