# Mixed, Augmented and Virtual, Reality Applied to the Teaching of Mathematics for Architects
## Research Carried Out
### Information Collection Tools  
The instrument used to gather information consisted of two parts: one meant to analyze the degree of acceptance that technology aroused amongst students; and another one that enabled students to evaluate the objects in MR with which they interacted. In order to examine the degree of acceptance of MR, a decision was made to utilize the tool devised by [PERSON] [28]--\"Technology Acceptance Model\"--TAM--which collects information from five dimensions: perceived usefulness (PU); perceived ease of use (PEU); perceived enjoyment (PE); attitude towards its use (AU); and intention to use it (IU). The theory about the degree of acceptance of a technology suggests that the potential predisposition for the use of any technology largely depends on the attitude towards it, which is in turn determined by the \"perceived ease of use,\" the \"perceived usefulness\" and the \"perceived enjoyment\" (Figure 1). The instrument included 14 Likert-type items (4 for PU, 3 for PE, 2 for AU, and 2 for IU), with seven response options ranging from \"Extremely unlikely/I disagree\" (1) to \"Extremely likely/I agree\" (7). The TAM model appeared to be quite consolidated in its application to AR when it was analyzed using the Structural Equation Model [29]. These variables may be simultaneously influenced by a series of external predictor variables, amongst which usually appear: type of user, gender, age, experience in the utilization of technology, level of education, professional qualification, or personal proneness towards innovation [30; 31; 32]. The Technology Acceptance Model has proved effective to ascertain people's attitudes towards specific technologies, such as the future intention to utilize it, and has been applied to a variety of technologies: Augmented Reality [33; 34], mobile devices used for online training [35], b-learning [36], engineering [37] or virtual training platforms [38]. The following predictor variables were identified for our research: gender; students' perception about the quality of the MR object; and students' perception about the technical mastery of audiovisual, computer and internet resources. This allowed us to build the model shown in Figure 2 below. This model enabled us to pose the following hypotheses for their statistical contrast:  
Figure 1: TAM model developed by [PERSON] [28]. Figure 2: Model based on the TAM developed by [PERSON] [28] formulated for our research. (Translation: Gender//Quality//Internet technical mastery//Audiovisual technical mastery//Computer technical mastery; Perception about ease of use//Perceived self-efficacy//Perception about success; Attitude by use//Intention to use). H1-H2-H3: The technical mastery of computers that the student claims to have impacts positively on the perception of success in relation to the use of MR, on the perceived self-efficacy regarding MR utilization, and on the perception of MR's ease of use. H4-H5-H6: The technical mastery of audiovisual resources that the student claims to have impacts positively on the perception of success in relation to the use of MR, on the perceived self-efficacy regarding MR utilization, and on the perception of MR's ease of use. H7-H8-H9: The technical mastery of the Internet that the student claims to have impacts positively on the perception of success in relation to the use of MR, on the perceived self-efficacy regarding MR utilization, and on the perception of MR's ease of use. H10-H11-H12: The quality of the system produced in MR has a positive effect on the perception of success in relation to the use of MR, on the perceived self-efficacy regarding MR utilization, and on the perception of MR's ease of use. H13-H14-H15: The student's gender has a positive effect on the perception of success in relation to the use of MR, on the perceived self-efficacy regarding MR utilization, and on the perception of MR's ease of use.