**3. Theory of rich media**

In their seminal work on rich media theory, Daft and Lengel [25] stated that managers can improve the performance of their organizational information processing tasks by matching media characteristics with needs. The theory of media richness is also based on the assumption that media have the capacity to facilitate information sharing. This possibility was later called media richness, from which various meanings can be derived. This theory claims that four factors affect media richness: The medium's ability to convey multiple signals (e.g., sound stimulation, gestures), immediate feedback, language diversity, and ideal personal focus. This theory claims that rich media lead to better performance in two-way communication by communicating faster and understanding ambiguous messages better, because rich media provide a lot of information and messages in communication; Therefore, Daft and Lengel [25] concluded that the use of richer media (such as face-to-face meetings) leads to better performance for interpersonal tasks (such as deciding on company achievements). However, using lean media (such as using notes and notes) less often leads to better performance.

At this point, media richness theory was a theory of media use, not media choice. This theory examines the conditions under which any medium is most effective (e.g., how managers should use the medium) rather than whether managers actually chose the medium. However, the first empirical tests of media richness theory ([26]; cited in [27]) studied media selection, not usage effects. The research method of their research was such that they asked the managers to say which media they use to see if the managers' understanding of the best media and the appropriate task matches them or not.

The following years have seen a number of experiments on media enrichment theory, many of which have provided revised interpretations (e.g., [28]). However, almost all of these studies have followed the empirical work of Daft and Lengel [25]. Daft and Wigginten (cited in [27]) identified nine different types of language: art, nonverbal signs, poetry, general verbal expression, specific accents, linguistic variables, computer languages, probability theory, and analytical mathematics.

*Perspective Chapter: Virtual Space and Curriculum of Technical and Vocational Training DOI: http://dx.doi.org/10.5772/intechopen.112189*

Daft et al. [26] broadly grouped these into two: natural language and numbers. Personal focus emphasizes the extent to which the message sender can customize the messages to meet the individual needs of the receiver. Generic ads cannot be personalized because they are broad, but personal messages can be (cited in [27]).

Using media richness theory to define and measure performance is incorrect. Daft and Lengel ([25], pp. 567–568) stated that organizations process information to achieve appropriate performance, without defining "performance" in a nutshell. Media richness theory explains the conceptual framework of performance in three terms: better decision-making (quality of decision-making), creation of shared systems of meaning (consensus among participants), and better use of participants' time (time required to reach conclusions). User satisfaction is also presented as a performance element; satisfaction has been one of the main elements in this for a long time. Therefore, we believe that the communication satisfaction of the sender and receiver(s) is another element of performance. We believe that this multidimensional definition (decision, quality, consensus, time, satisfaction) provides a reasonable way to conceptualize AI work [27].

### **4. Characteristics of e-learning based environments**

Space and location in virtual environments have features such as access to resources in any place based on the ecosystem and interactive communication and being a creator of opportunities, which will be explained further.

### **4.1 In virtual environments, access to information and educational resources is possible in any place**

E-learning environments, using the facilities and technological tools they have, make scientific learning not limited to the classroom, but their learning outside the classroom and in any place is possible [13], and informal learning is also random and spontaneous and occurs in any space [29]. This mobile and informal learning makes learners automatically self-directed to solve their scientific problems [30, 31]. Based on this, e-learning-based environments are responsive to learners' scientific problems by providing mobile and fluid communication tools, in any place. Therefore, it is necessary for the teacher to not to limited to classroom activities and to design activities to consolidate and deepen learning outside the classroom so that learners can enrich their learning informally.

#### **4.2 In virtual environments, the place of learning is based on the ecosystem**

The network environment or learning ecosystem is open, flexible, and collaborative among the environments based on e-learning [32], and knowledge in it is based on situations and contexts [33]. Also, based on the transpersonal theory, communication in a virtual social network arises from its situations and conditions and is not limited to the person establishing communication [34]. And a network instructor with knowledge of the learning context, by enriching the learning environment, in addition to creating an ecosystem, also helps the development and growth of ecosystem literacy [11]. Therefore, in environments based on e-learning, the place of formal and informal learning depends on the local environment, which requires technological literacy.
