# A Model to Measure U-Learning in Virtual Higher Education: U-CLX
## 7 Conclusions and Future Work  
The U-Learning model, supported by the learning experiences and learning of the Virtual Higher Education Connective, proposes a way of measuring ubiquitous learning. We describe the concepts, characterization, and mathematical foundation which allow the level of U-Learning in virtual higher education to be assessed. This information will allow institutions to improve their educational processes and technologies for ubiquitous learning. It allows us to know the current state of U-Learning in an institution, and what is needed to make it a reality. The present proposal is the result of several iterations. As we found results, these were published, and they strengthened our initial hypotheses. We highlighted mainly three. In the first article, we provided evidence of the common elements of selected models and their mechanisms of integration [7]. In the second, we exposed theoretical bases on connective learning and xAPI to develop the u-Learning framework [9]. Finally, we explored the possibility of integrating the xAPI standard to manage the learning experiences from ubiquitous teaching-learning processes. The proposal presents two components: pedagogical and technological [42]. The U-CLX Model proposes a U-Learning ecosystem composed of four dimensions: Time, Place, Medium, and Context. This ecosystem contains a Pedagogical Component based on ubiquitous learning and a Technological Component based on ubiquitous computing. Each component includes six elements. The elements of the Pedagogical Component are Learning Paradigms, Learning Scenarios, Academic Levels, Knowledge Domains, Physical Characteristics, and Effects; the elements of the Technological Component are Learning Experiences, Technologies, Learning Analytics, Functionality, Devices, and Management. Some of these 12 elements contain items and subitems. The mathematical basis of the U-CLX Model is based on the concepts of lines, line segments, hyperplanes, convex sets, and hyperspheres in four dimensions, R4; the four dimensions become the model variables. Equation (7) allows the level of ubiquitous learning to be calculated through the volume calculation in a hypersphere, R4. The positive values of each variable between 0 and 100 (20) are used; the data for each variable are  
Figure 7: Comparison of the results [2]. obtained from the self-assessment surveys. With these data, the volume is calculated to measure the level of ubiquitous learning. The results indicate that the U-CLX Model performs the ubiquitous learning level measurement considering the model conceptualization, dimensions, components, elements and items, the evaluation processes, and the roles of the individuals. In other words, the U-CLX measures the level of ubiquitous learning of a virtual higher education institution, presenting the current state of U-Learning and how it could be improved. It is essential to clarify that other models measure learning, online education, blended learning, the nature of education, and the forms of knowledge. However, our previous literature reviews didn't find any initiative that measured ubiquitous learning in virtual education institutions. Only the TAG model measured the institutions' ubiquity level. Even when the U-CLX Model was developed, no models or methodologies were found to measure U-Learning in virtual education institutions. Future work for the U-CLX Model might be focused on including new dimensions, and allowing measurement of other variables; this could be performed by using a hypersphere with more dimensions. Another important area for research would be how to carry out self-evaluation in real-time, taking the data automatically and having a permanent display of the results of the dimensions; this will allow constant monitoring of progress. Finally, it is vital in the future to implement AI in the development of U-Learning evaluation; this will allow better results to be obtained from the data submitted in the evaluation surveys. Conceptualization, G.M.R.V. and C.A.C. ; methodology, G.M.R.V.