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Students have learning difficulties, mainly in processes that involve attention and interpretation of written or spoken language. Technological tools allow to create computational platforms with adaptation aspects depending on the student’s characteristics. It is also important to highlight the progress of the measurement of cognitive processes such as attention through NeuroSky’s MindWave EEG sensors. This paper aims to present the results of analyzing attention levels of children with learning difficulties, based on the acquired brain waves. As a final result, an adaptive computational system that displays educational activities regarding educational profiles of children is obtained.

The Kamachiy–Idukay platform was chosen to make the validation. The platform generates the educational activities according to the students’ profile. The validation phases were identification of the test environment, the first environment required a scenario that involved students with learning difficulties, to verify the functionality of the system, when analyzing cases of the students with learning difficulties; identification of two validation criteria, type of educational activity and attention difficulties of the students; and analysis of the brain signal when children interact with the educational content.

The adaptation of contents that include music and animations generate higher levels of attention in students with difficulty. The analysis of signals from the NeuroSky sensor to determine the attentional levels in children allowed a generation of content adapted to the characteristics of the difficulty in each child.

For the validation, it was necessary at the beginning of the activity to determine the stability of the signal emitted by the NeuroSky sensor. Two cases were studied in children with difficulty and their measure of attention versus adaptive contents.

A k-means algorithm was used to establish the attention levels of the children.

Children with learning difficulties have different learning styles, which implies an adaptation of content that generates an attentional process according to their characteristics.

Evaluation content adaptation taking into account the signal brain sensor NeuroSky for learning process. The signal brain of the student when interacting with the activities is include in the student profile.

Nowadays, an extra consumption of electric energy in the Colombian houses is generated due to electric or electronic elements plugged into the electric network. This fact produces a cost overrun in the user’s electricity bills. To reduce this extra cost, and also with a plus of reducing greenhouse gas emission, a monitoring system for the consumption of electric energy in a household will be designed and implemented to make electricity users realize how much money and energy is being wasted due to the unnecessary electric elements plugged into the network. This paper aims to show a monitoring system that allows the client to supervise the consumption of some appliances inside his/her home, remotely. It is also considered the HMI to be able to log in, choose the intervals of data and generate reports and graphics. The monitoring system is based on the integration of several technologies that are already used and implemented in houses and buildings, such as: measuring and treatment of data electronically using microcontrollers, Wi-Fi technology and dynamic graphic interface (website).

The methodology consists of several tasks, starting from documentation of the variables, instrumentation and methods for getting to the solution; the first part of the methodology focuses on selecting the electric and/or electronic elements to be monitored, so the instrumentation is able to monitor. Then, the power stage was implemented in this stage to measure signals from the sensors while sensing the electric nodes are adjusted, so does the transmission and reception. In the third stage, the design information system was implemented; this is where the received data from the sensors are stored and managed for further organization and visualization. Activities included the following: Analysis of the model of use cases: Identification of actors and actions that are involved in the system. Server selection: Study of the different server to manage the database. Design of the database: The variables, tables, fields, profiles are determined for managing the information. Connection between sensors and database: Correct data transmission and managing to the database from the sensors. Finally, the system is validated in a rural house for a month.

The monitoring system satisfies the main objective of making a tracing of the behavior of some appliances inside a house, showing graphically the instant current generated while connected, the cumulated energy consumed and the cost in Colombian pesos of the energy consumed so far, in real time.

The monitoring system requires the correct functioning of the sensors connected to each household appliance in the home.

The main approach in the monitoring platform is the real-time measurement of energy consumption by nodes (in each appliance) that allows the user to control the money. The innovative impact of the project will be based on the use of hardware and information systems in the measurement of electrical consumption.

This research has a direct impact on the economic aspects of the low-income population by allowing them to manage their energy consumption through the proposed system.

The main approach in the monitoring platform is the real-time measurement of energy consumption by nodes (in each appliance) that allows the user to control the money.

The purpose of this paper is to propose Kamachiy-Mayistru (KM), an adaptive module to support teaching to people with learning difficulties. In Colombia, learning disabilities and difficulties are frequent in the integration classroom. Proper learning can be achieved as long as teaching strategies and didactic tools are the most adequate to the specific student characteristics and follow the suggestions given by experts for each learning difficulty. This module assists the teacher to prepare a course taking into account the disability profile, the student profile and pedagogical model suggestions. In this way, the student can learn utilizing the format and didactic tools more appropriate to their specific necessities.

The design and implementation of the KM comprises the following phases: identify the most important student, teacher, difficulties and course parameters to take into account in the adaptation process; design the data model that supports activity adaptation, based on student characteristics and difficulties; implement the platform; and validate the approach through a case study of teachers and their students with difficulties.

The application of KM in the case study indicated the effectiveness of KM to assist teachers in organizing course activities for students with and without disabilities or difficulties.

KM addresses specific student difficulties: attention, memory and languages. KM does not address severe cognitive disabilities. Regarding the validation, it is recommended to pursue new case studies to further demonstrate the effectiveness of the approach in a broader population.

The main approach in KM is to suggest activities or pedagogical strategies to teachers to best support learning in students with difficulties or disabilities. The core of KM is an algorithm, called “Adapt Course”, that takes as input student and disability profiles, the course contents and the pedagogical model and creates course structures that are specially tailored to each student.

This model recommends teachers different activities, based on the specific student difficulties, to create personalized courses. It is able to address specific educational issues that are associated with learning difficulties and disabilities, such as educational integration, through content organization and personalized information display, which are based on the inherent characteristics of each student in the classroom.

It is based on a conceptual model that provides the essential architecture to design and implement virtual learning environments for students with learning difficulties or disabilities.

This article aims to propose an adaptation algorithm that combines the analytical hierarchy process (AHP), a rule-based system, and a k-means clustering algorithm. Informatic tools are very useful to enhance the learning process in the classroom. The large variety of these tools require advanced decision-making techniques to select parameters, such as student profiles and preferences, to adjust content and information display, according to specific characteristics and necessities of students. They are part of the Kamachiy–Idukay (KI), a platform to offer adaptative educational services to students with learning difficulties or disabilities.

The design and implementation of the adaptation algorithm comprises the following phases: utilization of the AHP to determine the most important student parameters, parameter to take into account in the adaptation process, such as preferences, learning styles, performance in language, attention and memory aspects and disabilities; designing the first part of the adaptation algorithm, based on a rule-based system; designing the second part of the adaptation algorithm, based on k-means clustering; integration of the adaptation algorithm to KI; and validation of the approach in a primary school in Bogotá (Colombia).

The main approach is the application of computational techniques, namely, rule-based systems and k-means clustering, plus an AHP prioritization at design time to yield a system to support the teaching–learning process for students with disabilities or learning difficulties.

The algorithm found several groups of students with specific learning difficulties that required adapted activities. The algorithm also prioritized activities according to learning style and preferences. The results of the application of this system in a real classroom yielded positive results.

The algorithm performs adaptation for students with mild disabilities or learning difficulties (language, attention and memory). The algorithm does not address severe disabilities that could greatly affect cognitive abilities.

The main contribution of this paper is an adaptation algorithm with the following distinctive characteristics, namely, designed utilizing the AHP, which ensures a proper prioritization of the student characteristics in the adaptation process, and utilizes a rule-based system to identify different adaptation scenarios and k-means clustering to group students with similar adaptation requirements.

This paper aims to present a detailed description of Tukuchiy, a framework to dynamically generate adapted user interfaces. Tukuchiy is based on Runa-Kamachiy, a conceptual integration model that combines human–computer interaction (HCI) standards to create user interfaces with user-centered concepts usually addressed by adaptation.

The first step was the definition of three profiles: user, context and interface. These profiles contain information, such as user disabilities, location characteristics (e.g. illumination) and preferences (e.g. interface color or type of system help). The next step is to define the rules that ensure usability for different users. All of this information is used to create the Tukuchiy framework, which generates dynamic user interfaces, based on the specified rules. The last step is the validation through a prototype called Idukay. This prototype uses Tukuchiy to provide e-learning services. The functionality and usability of the system was evaluated by five experts.

To validate the approach, a prototype of Tukuchiy, called Idukay, was created. Idukay was evaluated by experts in education, computing and HCI, who based their evaluation in the system usability scale (SUS), a standard usability test. According to them, the prototype complies with the usability criteria addressed by Tukuchiy.

This work was tested in an academic environment and was validated by different experts. Further tests in a production environment are required to fully validate the approach.

Tukuchiy generates adapted user interfaces based on user and context profiles. Tukuchiy uses HCI standards to ensure usability of interfaces that dynamically change during execution time. The interfaces generated by Tukuchiy adapt to context, functionality, disabilities (e.g. color blindness) and preferences (usage and presentation) of the user. Tukuchiy enforces specific HCI standards for color utilization, button size and grouping, etc., during execution.