to develop skills and competencies. Thus, it was
observed that the teaching of programming can be
organized in 2 phases: theory and simulation. In the
1st phase, instruments and materials can be used to
support the teaching, as tutorials. In the 2nd phase, a
tool can be inserted to support the teaching and
learning processes of programming (ref41).
In addition, other technologies were mentioned in
the selected papers, such as Robotics (17):
development and use of robots; 3D printing (12):
additive manufacturing process where a three-
dimensional model is created by successive layers of
material; Gamification (11): using game techniques
to captivate people through challenges and rewards;
Cloud computing (11): computing services, including
servers, storage, databases, among others, that
contribute to virtualization and availability of
resources and materials for teachers and students
through the internet; Augmented Reality (9):
integration of virtual elements to real-world
visualizations; Internet of Things (9): the digital
interconnection of everyday objects with the Internet;
Virtual Reality (7): interface between a user and an
operating system through 3D graphics or 360º
images; Virtual Learning Environment (7):
environments that assist in setting up courses on the
Internet; Simulation (6): software capable of
reproducing a process or operation in the real world;
Big Data (5): the knowledge of how to deal with large
data sets; Multimedia Resources (5): a range of
materials such as sounds, images, texts, and videos;
Cyber-Physical Systems (4): a system composed of
collaborative computational elements to control
physical entities; and Unplugged Computing (3):
teaching computing without using computers. Also,
other technologies have been identified in SMS, but
less frequent, such as Artificial Intelligence (2): use
of the computer to automate common tasks performed
by humans; Intelligent Teletutor (2): computational
environments used in metacognitive training; Chatbot
(2): a computer program that uses artificial
intelligence to imitate conversations with users;
Massive Open Online Course (2): open course
accessible through virtual learning environments;
Machine learning (1): data analysis method that
automates the construction of analytical models;
Learning Manager System (1): platforms that use
students, manage and monitor the classroom;
Learning objects (1): any digital resource that can
support the teaching and learning processes; Social
Networks (1): environment composed of people or
organizations, connected by one or more types of
relationships; and Storytelling (1): storytelling to
streamline and disseminate knowledge.
3.5 Ways of Working (SQ4)
The results of this sub-question show that the most
used form of work to support the training of students
and professionals is methodology. One of the
methodologies identified in this SMS was STEM (an
acronym for working in areas such as Science,
Technology, Engineering, and Mathematics). This
methodology was used to link the university with
high schools to prepare a workforce to fill in the gaps
of skills focused Industrial Internet of Things (ref53).
Also, 22 methods were found, such as CMTrain,
used for professional training (ref58); PICE, used to
improve the innovation process (ref77);
MINTReLab-MOOC, created to integrate theory with
practice (ref28); MEF, created to insert computational
resources in Physics classes (ref31); DMA, used to
assess the level of digital maturity in the industry
(ref24); TTD, used in training for decision making
(ref47); EPF, used to teach programming in
elementary schools (ref38); VET, used to support
educational vocation and vocational training (ref78);
CSCW, used to support Computer Supported
Collaborative Work (ref74); SCRUM, used to
support project management and planning (ref13);
SAHI, used to support Intelligent Hybrid Learning
(ref29); CHPL, used to support problem-based
cooperative learning (ref34), among others.
Eleven models were found, such as PILOT, used
to combine online learning and offline training
(ref75); ILM, Intelligent Laboratory Model,
supported by educational technologies) (ref08); DM,
Didactic Model inspired by the Learning Factory
(ref62); CM, Collaborative Model based on
innovation (ref30); and MI, Model to Integrate the
pillars of Industry 4.0 in engineering education
(ref20). Besides, 8 Learning Factories were found
aimed at enabling industrial production at
universities. In sequence, 7 approaches were
identified, such as DITA, used to guide the
production, selection, filtering, and sampling of
content for a business team (ref42); BW, used to
investigate the modification of Behavior the Work
(ref07); PSSC, used as a Potential Solution to the
Social Changes brought about by industry 4.0 (ref68);
AAP, used to assist in articulating ideas, organizing
steps for skill development (ref71); AAI, created to
support training in Industry 4.0 (ref36); LCA, used to
assist in the Sustainable Manufacturing Life Cycle
Assessment (ref51); and APC, used to support
Practice and Collaboration in the development and
use of applications (ref17).
Besides, 7 applications were found, such as
Collabora, an environment developed to support the