A group of researchers and developers from Colombia and Mexico have recognised that the development of state-of-the-art Extended Reality software, a key technology for the Metaverse, has great potential to improve teaching-learning processes in educational institutions. However, the development process does not take into account accessibility, universal design and inclusion, especially for the deaf student community. An extended reality model is proposed for the creation of this type of software as a tool to support access to knowledge, based on information gathering, requirements analysis, user-centred design and video game programming, including the ludic and didactic. The aim is to minimise the barriers that limit the learning of programming logic by students with hearing disabilities through the use of new technologies, creating spaces in virtual worlds that are understandable, usable and practical in conditions of safety, comfort and as much autonomy as possible. To validate the model, a mixed reality software prototype was designed and programmed to train students in programming logic, both deaf and hearing. User and heuristic tests were carried out, showing how immersion can improve knowledge acquisition processes and develop skills in higher education students.

Disruption era had affected to every sector, including higher education sector. Between 2015 and 2019, more than 130 private university were closed for a different of reasons. Basically, they are not ready to face the change. Innovation disruption created new situations, thus created new demand while destroying prior situations. Many businesses collapsed suddenly because they are not aware of the inflection point that suddenly destroys the business model that has been run for years. Nevertheless, the current situation gave hints to new thoughts, including the development business model called infinite game theory. This paper examined how strategic inflection points can have occurred in the higher education sector and the infinite games model can be an alternative strategy in order the higher education sector withstand disruptive innovation era.

Astrology is a very ancient and traditional method of prediction that increases the interest of people continuously. The globe today, there are no common guidelines or principles for astrological prediction. Rather than setting universal principles and criteria for astrological prediction, astrologers focus on providing high-quality services to individuals but there is no guarantee of accuracy. Machine learning is providing the best result for analysis and prediction on many applications by the learning of computers. Prediction and classification make it possible for any learner to work on large, noisy, and complex datasets. The main motive of the paper is to introduce a scientific approach that reduces the drawback of the traditional approach and indicates the universal rules of prediction and proves the validity of astrology by the three classification techniques, Naïve Bayes, Logistic-R, and J48. It is a part of supervision learning that operates with cross-validation 10,12, and 14fold for calculating the terms 1) correctly classified instances (CCI), erroneously categorized instances (ECI), Mean absolute error (MAE), Root mean squared error (RMSE), and Relative absolute error (RAE). 2) True Positive Rate, False Positive Rate, Precision, and F-Measure values. 3) The MCC, ROC, and PRC area values. 4) To calculate the average weight of the three-class label professor, businessman, and doctor in terms of true positive rate, false-positive rate, precision, F-measure, PRC, and ROC area, 5) finally, we calculated the accuracy of each classification technique and compare which provide the better result. For this, we have collected the date of birth, place of birth, and time of birth of 100 persons who belong to different professions. 40 data of professors, 30 data of businessmen, and 30 data of doctors, prepare the horoscope of an individual with the help of software. For analysis, we create the datasheet in .csv format and apply this data sheet in the weka tool to check various parameters and the accuracy percentage of each classifier.

The project-based learning (PjBL) paradigm is often considered the most advanced in vocational education. The increasing use of the PjBL paradigm in vocational education is an intriguing topic of study. In line with the rapid growth of information technology, it enables PjBL in vocational education to help students develop problem-solving, critical thinking, and teamwork skills. In this study, a bibliometric method is used to provide insight into the structure of the subject, social networks, research trends, and issues reflecting project-based learning in vocational education. On November 27, 2022, the Scopus database was searched using project-based learning terms in the title. The second search field appears in the title, abstract, and keywords vocational education or TVET, restricted to journal articles or proceedings and in English to keep them current. This analysis revealed 60 articles in Scopus-indexed journals and proceedings between 2010 and 2022. Dwi Agus Sudjimat from Malang State University, Indonesia, was the most prolific author, having authored four articles on the subject. Indonesia is the nation investing the most in developing PjBL models. According to the thematic data, project-based learning is located in the first quadrant, has high centrality and density, and has well-developed questions related to the study topic. The results of this study show that the project-based learning model that is evolving in vocational education is likely to continue to be an important teaching approach in this field.

The need to change the professional practices in the nursing sciences obliges to accompany everyone towards a work on identity which engages a real process of professionalization. In addition, reflective practice (RP) has taken on more importance with this discourse on professionalization in nursing training settings. In this respect, reflection on practice is considered both as a competence of the professional teacher and upstream, in initial or continuing training, it is a tool for building one’s professional identity that can promote one’s professional development.
From this perspective, this study aims to study the impact of reflexive practice on 235 teachers working at the level of 23 ISPITS (Higher Institute of Nursing Professions and Health Technologies) of the Kingdom of Morocco, by means of a questionnaire, whose internal validity has been approved, transmitted through the Google Forms platform. As such, this quantitative study will focus on two strands the 1st aims to study the existence of an impact of reflective practice on the professionalization of teaching within Moroccan ISPITS. The second objective is to study the nature of a possible relationship between the professionalization of teaching by the RP and the strengthening of the professional identity of ISPITS teachers.
The results of this study show a positive impact of RP on the professionalization of nursing education; in addition, statistical tests have shown that there is a strong correlation between the professionalization of teaching in ISPITS and the strengthening of the professional identity of nursing teachers.
Also, this study could contribute to improving other vocational training. In addition, consideration of occupational identity, as well as the relationship between it and its interactive experience with others and in varied environments that elicit reflective feedback on its professional practice, are likely to promote the professional development of each practitioner.
The teacher's use of reflexivity is inseparable from his identity work. It will undoubtedly lead to the professionalization of training. In this perspective, similar studies can be carried out to deepen this theme and enhance their interventions.

A generalized model of population migration is proposed. On its basis, models of the set of directions of population flows, the duration of migration, which is determined by its nature in time, type and form of migration, are developed. The model of indicators of actual migration (resettlement) is developed and their groups are divided. The results of population migration are described, characterized by a number of absolute and relative indicators for the purpose of regression analysis of data. To obtain the results of migration, the author takes into account the power of migration flows, which depend on the population of the territories between which the exchange takes place and on their location on the basis of the coefficients of the effectiveness of migration ties and the intensity of migration ties. The types of migration intensity coefficients depending on the properties are formed. The lightgbm algorithm for predicting population migration is implemented in the intelligent geographic information system. The migration forecasting system is also capable of predicting international migration or migration between different countries. The significance of conducting this survey lies in the increasing need for accurate and reliable migration forecasts. With globalization and the connectivity of nations, understanding and predicting migration patterns have become crucial for various domains, including social planning, resource allocation, and economic development. Through extensive experimentation and evaluation, developed migration forecasting system has demonstrated results of human migration based on machine learning algorithms. Performance metrics of migration flow forecasting models are investigated, which made it possible to present the results obtained from the evaluation of these models using various performance indicators, including the mean square error (MSE), root mean square error (RMSE) and R-squared (R2). The MSE and RMSE measure the root mean square difference between predicted and actual values, while the R2 represents the proportion of variance explained by the model.