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For a long time, software engineering has been considered as a highlight driver of development and business support. Software development carries on some issues like high cost and failure projects. These issues have opened new challenges for several scholars and researchers; however, today software crisis continues. Software Factories presents a solution to improve software production and, to face those challenges especially to face de new develop AI-based applications requirements. This work aimed to develop a road map for building Software Factories inside higher education institutions. A design-based research (DBR) methodological approach has been used to build the proposed model. The methodological approach includes four steps: i) design, ii) test, iii) evaluate, and iv) reflect; these steps are repeated several times until they get the best outcomes and learning; a couple of iterations have been executed for testing the model. Early outcomes showed that: i) Building business software components have been the base for developing Software Factories (SF); hence the SF could permit to increase the students' professional skills and, at the same time to, get a broad of opportunities for linking with the industry. ii) The process for building components recommended a set of steps: model requirements, data collection, data cleaning, data labeling, feature engineering, training model, model evaluation, model deployment, and model monitoring. And, iii) during testing time, students showed an increase in their academic and professional skills in the field of the software industry. In conclusion, new trends increasing the fast-changing technical environments foster the development of components nowadays; therefore, a more substantial nexus between higher education and software development practice should be created to improve software mass production. [ABSTRACT FROM AUTHOR]

The growth of development of transactional systems to automate administrative management processes has grown substantially, a situation that has led companies to have data models and components programmed to cover a layer of document services, creating duplication of information and integration difficulties. Therefore, Workflow Management Systems is a transversal solution to improve these drawbacks. This research developed a transactional component to automate any administrative process through Application Programming Interface (API) that follows the representational state transfer (REST) software architecture. Which theoretical and general scientific methods were used in the literature review related to process automation and the international standard for Workflow system: Reference Model, published by the Workflow Management Coalition (WfMC), generating two main phases: 1) focused on the analysis of requirements and the selection of technological tools; 2) focused on product quality, where the life cycle of the system is carried out, from modeling to the application of functional and data load tests. The functionality was validated through the implementation of the core and the integration of the interfaces to automate an administrative process, demonstrating the validity of the standard in conjunction with the technological tools proposed in this work. The created API establishes a baseline so all administrative processes can be automated without developing new document process modules; therefore, it can be replicated with distributed architectures in multiple layers. [ABSTRACT FROM AUTHOR]

Currently, educational organizations have sought to meet the needs of students during the Covid-19 pandemic by incorporating information and communication technologies in the teaching-learning process. The purpose of this research is to offer higher education institutions, professors, and researchers, a peer feedback sentiment analysis model as an alternative to analyze subjectivity and improve the opportunity for formative and summative evaluation. The experimental method was applied and a model was designed, in which both the professor and the student evaluate the activities in a peer assessment scenario providing textual feedback. Then, was performed the sentiment analysis of the textual feedback. The model was tested at the Technical University of Manabi (Ecuador) using the machine learning method with a data set in Spanish. The best-performing classification algorithm was support vector machine with 0.870 in F-Measure for its training features on short texts. The early results show that the model classifies feedback as positive or negative. Is postulated as a useful tool to encourage reflection and criticality skills in the teaching-learning process. Students, with the feedback given in the first round, will be able to correct the task and improve performance in the second round. The level of satisfaction of the students was high since they expressed that both the feedback given and the one received will help them in future learning and improve their tasks. [ABSTRACT FROM AUTHOR]