Today, companies are subject to the absolute digitization of consumer behavior and their internal stakeholders. To successfully meet this challenge, companies need to define a digital transformation strategy. Unfortunately, most companies do not have a methodology to guide this transformation. As a result, the conduction of the process is complex, and there is no adequate diagnosis or route consistent with the company’s objectives. All this leads to disordered and inefficient technological implementations, which generate a high level of uncertainty. In this article, we present the results of a literature review analysis that compiles evidence regarding how companies are addressing the challenge of digitally transforming themselves for Industry 4.0. The findings have allowed us to formulate new research questions and hypotheses based on the results reported in the selected primary studies. We recovered a total of 21 primary studies, which we classified according to three criteria: guidelines, assessments, and agile method. The increase in the number of publications in recent years shows the attractiveness of the subject. The results obtained allow us to draw important conclusions that will help to conduct future research on this topic. In future work, we plan to extend this work further and propose usability principles based on Lean. Another line of work is to explore the artificial intelligence techniques that Industry 4.0 uses in its digital transformation processes.

Nowadays, in spite of the enormous advances and the irruption of agile methods, software development projects have not been able to increase success rates. In the literature, it is possible to find information regarding the main causes of failure in this type of project. Among the main causes identified are mistakes made in project management, insufficient support from top management, low stakeholder commitment, low level of collaboration among team members, and skill development levels of professionals. A key aspect in the execution of a software project is the technical and technological factors that appear as relevant in the literature, but have not been studied in sufficient depth compared to the factors mentioned above. The objective of this work is to identify the technical and technological factors that influence the success of software development projects. In order to identify these factors, a systematic mapping of the literature was carried out. Seventy-seven primary studies were selected and classified according to the defined protocol. The results obtained are quite encouraging since it was possible to identify and categorize a set of technical and technological factors that will allow conducting future research on this topic.

Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.

Agile frameworks continue to provide positive evidence regarding the benefits of their use in the software products. Since these methods develop professional skills in those who practice them, their knowledge and use will acquire greater demand in areas other than software development. For this reason, it is essential to recognize the key skills for agile team building. The goal of this paper is to identify the agile professional skills that the Chilean industry considers key to conform high-performance agile teams. A survey was applied to agile community professionals in Chile to validate the results of previous work and to identify relevant information regarding learning processes, techniques, and tools for working with agile frameworks. The results allowed to establish three key skills for high-performance teams with their respective levels of achievement.

Contemporary education is a vast field that is concerned with the performance of education systems. In a formal e-learning context, student dropout is considered one of the main problems and has received much attention from the learning analytics research community, which has reported several approaches to the development of models for the early prediction of at-risk students. However, maximizing the results obtained by predictions is a considerable challenge. In this work, we developed a solution using only students' interactions with the virtual learning environment and its derivative features for early predict at-risk students in a Brazilian distance technical high school course that is 103 weeks in duration. To maximize results, we developed an elitist genetic algorithm based on Darwin's theory of natural selection for hyperparameter tuning. With the application of the proposed technique, we predicted the student at risk with an Area Under the Receiver Operating Characteristic Curve (AUROC) above 0.75 in the initial weeks of a course. The results demonstrate the viability of applying interaction count and derivative features to generate prediction models in contexts where access to demographic data is restricted. The application of a genetic algorithm to the tuning of hyperparameters classifiers can increase their performance in comparison with other techniques.

While technology has helped improve process efficiency in several domains, it still has an outstanding debt to education. In this article, we introduce NAIRA, a Multimodal Learning Analytics platform that provides Real-Time Feedback to foster collaborative learning activities’ efficiency. NAIRA provides real-time visualizations for students’ verbal interactions when working in groups, allowing teachers to perform precise interventions to ensure learning activities’ correct execution. We present a case study with 24 undergraduate subjects performing a remote collaborative learning activity based on the Jigsaw learning technique within the COVID-19 pandemic context. The main goals of the study are (1) to qualitatively describe how the teacher used NAIRA’s visualizations to perform interventions and (2) to identify quantitative differences in the number and time between students’ spoken interactions among two different stages of the activity, one of them supported by NAIRA’s visualizations. The case study showed that NAIRA allowed the teacher to monitor and facilitate the learning activity’s supervised stage execution, even in a remote learning context, with students working in separate virtual classrooms with their video cameras off. The quantitative comparison of spoken interactions suggests the existence of differences in the distribution between the monitored and unmonitored stages of the activity, with a more homogeneous speaking time distribution in the NAIRA supported stage.

Classroom teaching methodologies are gradually changing from masterclasses to active learning practices, and peer collaboration emerges as an essential skill to be developed. However, there are several challenges in evaluating collaborative activities more objectively, as well as to generate valuable information to teachers and appropriate feedback to students about their learning processes. In this context, multimodal learning analytics facilitate the evaluation of complex skills using data from multiple data sources. In this work, we propose the use of beacons to collect geolocation data from students who carry out collaborative tasks that involve movement and interactions through space. Furthermore, we suggest new ways to analyze, visualize, and interpret the data obtained. As a first practical approach, we carried out an exploratory, collaborative activity with sixteen undergraduate students working in a library, with bookshelves and work tables monitored by beacons. From the analysis of student movement dynamics, three types of well-differentiated student roles were identified: the collectors, those who go out to collect data from the bookshelves, ambassadors, those who communicate with other groups, and the secretaries, those who stay at their work desk to shape the requested essay. We believe these findings are valuable feedback for the enhancement of the learning activity and the first step towards MMLA-driven Teaching Process Improvement method.

Nowadays, the massive use of agile approaches brings enormous challenges to the training of professionals with the skills required by the industry. In the educational area of software engineering, professors must have the support of technology to facilitate the process of acquisition of skills by students. The use of technology should help teachers to fulfill their role as learning facilitators and students as active agents of their learning. This article presents a web platform capable of supporting the traceability of key agile skills. This platform allows the professor to guide the process of acquiring key agile skills by defining improvement objectives, selecting agile practices and determining the expected performance levels for each activity. The platform invites students to commit to their training process and to improve in those aspects observed during the implementation of educational activities.

Currently, the improvement of core skills appears as one of the most significant educational challenges of this century. However, assessing the development of such skills is still a challenge in real classroom environments. In this context, Multimodal Learning Analysis techniques appear as an attractive alternative to complement the development and evaluation of core skills. This article presents an exploratory study that analyzes the collaboration and communication of students in a Software Engineering course, who perform a learning activity simulating Scrum with Lego® bricks. Data from the Scrum process was captured, and multidirectional microphones were used in the retrospective ceremonies. Social network analysis techniques were applied, and a correlational analysis was carried out with all the registered information. The results obtained allowed the detection of important relationships and characteristics of the collaborative and Non-Collaborative groups, with productivity, effort, and predominant personality styles in the groups. From all the above, we can conclude that the Multimodal Learning Analysis techniques offer considerable feasibilities to support the process of skills development in students.

Nowadays, companies and organizations require highly competitive professionals that have the necessary skills to confront new challenges. However, current evaluation techniques do not allow detection of skills that are valuable in the work environment, such as collaboration, teamwork, and effective communication. Multimodal learning analytics is a prominent discipline related to the analysis of several modalities of natural communication (e.g., speech, writing, gestures, sight) during educational processes. The main aim of this work is to develop a computational environment to both analyze and visualize student discussion groups working in a collaborative way to accomplish a task. ReSpeaker devices were used to collect speech data from students, and the collected data were modeled by using influence graphs. Three centrality measures were defined, namely permanence, persistence, and prompting, to measure the activity of each student and the influence exerted between them. As a proof of concept, we carried out a case study made up of 11 groups of undergraduate students that had to solve an engineering problem with everyday materials. Thus, we show that our system allows to find and visualize nontrivial information regarding interrelations between subjects in collaborative working groups; moreover, this information can help to support complex decision-making processes.

Computational thinking has become a required capability in the student learning process, and digital games as a teaching approach have presented promising educational results in the development of this competence. However, properly evaluating the effectiveness and, consequently, student progress in a course using games is still a challenge. One of the most widely implemented ways of evaluation is with an automated analysis of the code developed in the classes during the construction of digital games. Nevertheless, this topic has not yet been explored in aspects such as incremental learning, the model and teaching environment and the influences of acquiring skills and competencies of computational thinking. Motivated by this knowledge gap, this paper introduces a framework proposal to analyze the evolution of computational thinking skills in digital games classes. The framework is based on a data mining technique that aims to facilitate the discovery process of the patterns and behaviors that lead to the acquisition of computational thinking skills, by analyzing clusters with an unsupervised neural network of self-organizing maps (SOM) for this purpose. The framework is composed of a collection of processes and practices structured in data collection, data preprocessing, data analysis, and data visualization. A case study, using Scratch, was executed to validate this approach. The results point to the viability of the framework, highlighting the use of the visual exploratory data analysis, through the SOM maps, as an efficient tool to observe the acquisition of computational thinking skills by the student in an incremental course.

Este artículo presenta la creación de un ambiente de aprendizaje colaborativo mediante la integración del método BYOS (Build your own Scrum, Crea tu propio Scrum) al modelo pedagógico Flipped Classroom en Ingeniería de Software. Esta experiencia aprovecha los beneficios del modelo Flipped Classroom con la incorporación de un método que estimule el aprendizaje colaborativo de los estudiantes en el aula. El objetivo de esta investigación es propiciar el aprendizaje colaborativo mediante la integración del método BYOS al modelo Flipped Classroom en la enseñanza de los conceptos de Scrum. Los resultados obtenidos son alentadores debido a que el método aplicado facilitó la comprensión de los contenidos y la percepción de los alumnos fue positiva respecto al aprendizaje colaborativo. Los resultados también señalan que el tiempo debe ser bien controlado para lograr los objetivos en este tipo de innovaciones pedagógicas realizadas en el aula.

Este trabajo describe las principales características de diagramas de secuencia UML, la noción de falla o error y tolerancia a fallas, y algunos tipos de fallas comunes y sus acciones de corrección en un diagrama de secuencias UML. Así, el principal objetivo de este trabajo es proponer un algoritmo para la transformación de diagramas de secuencia UML en código Spin / Promela, una herramienta de verificación formal y de detección de errores en el chequeo de modelos para un sistema de tolerancia a fallas, y así entregar explicaciones de los pasos necesarios para ajustar y corregir los diagramas afectados. El algoritmo para transformar diagramas de secuencia UML en código Spin / Promela es útil para la detección de fallas en secuencias de mensajes. Se aplica la solución propuesta sobre un diagrama simple y general de secuencias UML para analizar su código Promela y garantizar la efectividad del chequeo de modelos sobre diagramas de secuencia UML. Además, se presentan ideas de extensión de la propuesta para el análisis de diagramas de secuencias UML con la inclusión de fragmentos combinados de iteraciones.

Speaking and presenting in public are critical skills for academic and professional development. These skills are demanded across society, and their development and evaluation are a challenge faced by higher education institutions. There are some challenges to evaluate objectively, as well as to generate valuable information to professors and appropriate feedback to students. In this paper, in order to understand and detect patterns in oral student presentations, we collected data from 222 Computer Engineering (CE) fresh students at three different times, over two different years (2017 and 2018). For each presentation, using a developed system and Microsoft Kinect, we have detected 12 features related to corporal postures and oral speaking. These features were used as input for the clustering and statistical analysis that allowed for identifying three different clusters in the presentations of both years, with stronger patterns in the presentations of the year 2017. A Wilcoxon rank-sum test allowed us to evaluate the evolution of the presentations attributes over each year and pointed out a convergence in terms of the reduction of the number of features statistically different between presentations given at the same course time. The results can further help to give students automatic feedback in terms of their postures and speech throughout the presentations and may serve as baseline information for future comparisons with presentations from students coming from different undergraduate courses.

A key problem in social network analysis is identifying influential users within a social network. To address this problem, numerous centrality measures have been defined to automatically state rankings of the users. In this article, we define the MilestonesRank, a new measure to detect opinion leaders, an important type of influential users focused on specific topics. This measure considers two parameters that can be freely adjusted depending on the needs of the analyst, namely, the interest and the exclusivity of the users regarding some specific topic. Every topic is bounded by a list of milestones over a period of time of several weeks or even months. We compare this measure with other classic measures to find opinion leaders in a real case study using the Twitter network. Our experiments show that the new measure allows us to find relevant opinion leaders that other measures are not able to detect.

Learning analytics consists of gathering and analyzing data from students in order to understand complex aspects of the learning process and promote its improvement. Currently, to the best of our knowledge, there is a lack of tools aimed at displaying multimodal data in an integrated way for general purpose analysis. In this paper, we present a free software tool based on the Microsoft Kinect sensor for automatic capture, identification, and visualization of ten body postures for posterior analysis. It is also possible to incorporate the identification of new postures if necessary. Learning and recognition is based on the AdaBoost algorithm. Posture recognition reached accuracy rates as high as 80% for 8 of the 10 identified postures. Concerning the software usability, a heuristic evaluation with three specialists was performed, as well as a usability test with five volunteer students. Results indicated that the software interface, based on the metaphor of a video editor, may allow its effective use by end users, though some adjustments are still necessary, such as the terminology used in some commands and the help system.

The irruption and wide adoption of the agile methods have generated tremendous challenges to innovate in matters of education in Software Engineering. These pedagogical innovations seek to strengthen the skills of students to achieve optimum performance in the industry, however, recent research still points out differences with respect to what the industry requires. This article's main objective, to collect and analyze scientific evidence on the skills required for the formation of agile high performance teams. A systematic mapping of the literature was carried out to obtain a visualization of the scientific contributions existing in this topic. Twenty-two primary studies were selected, which were classified according to the defined protocol. It was possible to identify a set of necessary skills and some methodological proposals aimed at stimulating and strengthening them. The results obtained allowed to identify and classify the skills for the formation of agile teams, which will allow conducting future investigations on this subject.

INTRODUCTION:A better understanding of the relationships between Computational Thinking and disciplines already present in the school curriculum may help the identification of possible educational benefits.This is particularly relevant in the case of Mathematics, which is a subject that constitutes itself as a hurdle to students in many Latin-American countries.OBJECTIVE: Identify and analyze didactic activities related to Computational Thinking and Mathematics reported in the literature regarding the target public, developed skills and contents, as well as research methods used to identify learning outcomes.METHODS: A Systematic Literature Review (SLR) was conducted, including studies published between 2006 and 2015 that included a description of didactic activities that developed Computational Thinking together with skills or contents related to Mathematics as well as an evaluation of learning outcomes.RESULTS: 59 studies were included in the revision.A wide variety of mathematical topics is being developed, with some emphasis on Algebra, Calculus and also higher-order thinking skills.Still 32.2% of included studies only present informal or anecdotal evidences of learning.CONCLUSION: In the last two years of the revision there was an increase in the number of activities focused on basic educational levels.Even though recently more rigorous methodological procedures were used to evaluate learning effects, still half of the studies with formal evaluation of learning outcomes use a single data source.Also, there are few studies focused on Math modelling and teacher training.

To design and build computer systems for the acquisition and treatment of environment signals often requires knowledge about electronics for a proper use of usually no compatible and costly hardware components. In the area of computer science did not exist a clear hegemony nor direct joint work with the electronic area in knowledge lines, specialization areas, and courses in their education process. Arduino reduces highly these barriers. This work details students experience using Arduino for developing projects to acquire environmental variables, visualize such variables, and for defined values range to perform certain actions on the scanned environment. Students are from the Computer Engineering major of the Universidad Viña del Mar (UVM), Chile who were able to develop competencies for the use of Arduino and implement prototype systems to reach the planned goal in 6 weeks. That proves that electronics is now very accessible to everyone.