Sergio Inzunza

Implementing adaptive interfaces: a user model for the development of usability in interactive systems

Current user-centered software development approaches make special emphasis on the characteristics of the user and the tasks that must be accomplished because it affects greatly the way the user interacts with the system. Most research works propose some user characteristics; however, there is not a model that integrates aspects such as psychological, cognitive, and physical. In this paper, we propose a user model integrating all the users characteristics, with the purpose of achieving an adaptive software interface improving the overall usability of any system. We have integrated this model gathering information from different disciplines such as medicine, sociology and their practitioners. In order to validate our model, we have implemented user interfaces for various real projects taking into account characteristics from real users. Also we present here an example of how a user interface can be adapted for a specific set of user capabilities.

Usability Operations on Touch Mobile Devices for Users with Autism

The Autistic Spectrum Disorder is a cognitive disorder that affects the cognitive and motor skills; due that, users cannot perform digital and fine motor tasks. It is necessary to create software applications that adapt to the abilities of these users. In recent years has been an increase in the research of the use of technology to support autistic users to develop their communication skills and to improve learning. However, the applications’ usability for disable users is not assessed objectively as the existing models do not consider interaction operators for disable users. This article focuses on identifying the operations that can easily be performed by autistic users following the metrics of KML-GOMS, TLM and FLM. In addition, users of typical development were included in order to do a comparison between both types of users. The experiment was carried out using four applications designed for autistic users. Participants were subjects divided in two groups: level 1 and level 2 autistic users, and a group of users of typical development. During the experimentation, users performed a use case for each application; the time needed to perform each task was measured. Results show that the easiest operations for autistic users are K (Keystroke), D (Drag), Initial Act (I) and Tapping (T).

A feedback system to provide affective support to students

Learning support systems need to provide direct and affective interaction to students; it can encourage them and improve their motivation to learn. However, several systems do not include this kind of interaction between students and tutors. In those cases, the student learning interest and motivation to learn could be negatively affected, and student profits from the system could be impoverished. Only a few works have examined the influence of written affective feedback, but these studies focused on student learning outcomes, not in student motivation to learn. The objective of this work is to analyze the impact of an affective feedback system on the students’ perception of two aspects: (i) the degree to which study time is enjoyable and (ii) the motivation to learn. Results show that the affective feedback system makes more enjoyable study time and motivates students to learn, particularly to students with low academic performance. These system enhancements eventually would improve student‐learning outcomes.

User Modeling Framework for Context-Aware Recommender Systems

Context-aware recommender systems (CARS) use data about the user and the context to enhance their recommendation outcomes, such data is stored in user models. As the is no generic data model, CARS developers and researchers need to design and develop their own model, with no model to use as reference, nor any tool that facilitate the design and development work. In this work we present a user modeling framework for context-aware recommender systems whose core is a generic user model for CARS. The framework is intended to facilitate the implementation of the models by providing a pre-implemented, working ready functionality, while the model itself can be used by developers and researchers as a basis while creating more specialized models.

User and Context Information in Context-Aware Recommender Systems: A Systematic Literature Review

Using contextual information inside recommendation systems is an effective approach to generate more accurate recommendations. This paper present a review conducted to identify what user’s and context’s information it’s considered relevant by researchers to generate contextual recommendations from 2012 to 2015, based on Kitchenham systematic literature review methodology. The results indicated that there is a large set of possible user’s and context’s information that can be used to do recommendations. This review can be taken as basis for future context-aware recommender systems development, as well as development of contextual user models.

An Empirical Study on Usability Operations for Autistic Children

Leo Kanner defined autism as a distinct personality disorder, a great difficulty to communicate with others. Some autistic persons show cognitive and motor difficulties. Due to the problem presented in users with autism, it is necessary to create interfaces and applications that fit the capabilities of this type of user. This paper identifies operations to perform tasks in mobile applications that an autistic child can execute with less complexity based on the metrics KLM-GOMS and TLM-GOMS. Mobile applications were tested to calculate such metrics in different use cases. The results presented in this paper show how autistics users from different autism levels perform different type of tasks; the results are compared with the model proposed by GOMS. The experimentation shows the total time in seconds that it is necessary to realize every use case and be able to complete the goal assigned for each application. Our results show that an autistic user need more time to interact with the technology; this address to adjust the classification of the operations for interacting with computer-based systems and develop applications that they adapt to the autistic user.

Affectivity Level for Intelligent Tutoring System Based on Student Stereotype

Affectivity is an important part of the learning process. Several authors integrate affectivity in ITS. However, they provided the same affectivity for all students. Authors realized that students were not benefit in the same way by affectivity. This could be because these systems provided the same affectivity to all students, and every student is different and has different needs. For that reason we propose to create a student stereotype to assign an affectivity level considering gender, academic performance and personality. Based on a questionnaire survey applied to 243 students we concluded that female irregular students are the students who need more affectivity and male regular extrovert students need moderate affectivity.

Automated Generation of User-Interface Prototypes Based on Controlled Natural Language Description

Most software applications are derived from customer needs, which mainly are expressed as functional requirements. These requirements are the basis for software construction. Identifying user actions, system actions, inputs and outputs from functional requirements, is a fundamental task for deriving usability aspects. In this paper we present a proposal for automating the generation of user interface prototypes with the main idea of minimize the task overload and take advantage of work that is already done in development cycle such as: requirements and use case description expressed in natural language. This proposal allows us to integrate functionality and usability since the beginning of the software development process. Also, we present a basis for the automation of adaptive user interfaces generation.

Assessing the Target’ Size and Drag Distance in Mobile Applications for Users with Autism

Users with Autism Spectrum Disorders (ASD) show great interest in, and operate with facility, technological devices like smartphones and tablets. As a result, the number of applications specially developed for these kinds of users keeps growing. Nevertheless, the creation of an application that adapts to user abilities is not a straightforward process. This article focuses on identifying the optimal target size and drag distance that developers and designers can use when creating applications for users with ASD to allow for easier interaction of users with screen elements. In the experiment performed, different target sizes and drag distances were compared. Based on the results, we suggest that 57 pixels is the minimum target size to support the interaction of level 1 and 2 for users diagnosed with Autism Spectrum Disorders. These results can be used as guidelines for interaction designers of mobile applications for autism. Nevertheless, the creation of an application that adapts to user abilities is not a straightforward process, because users with these conditions have significant sensory-motor problems.

Affective Evaluation of Educational Lexicon in Spanish for Learning Systems

In the last years, several educational systems integrate text-based affective feedback. However, the analysis of educational lexicon from the affective perspective is limited. Previous work classified words in three categories: positive, negative or neutral. For that reason, this work proposes the construction of an educational lexicon in the Spanish and the evaluation of its affectivity using the arousal-valence scale. The educational lexicon was setting up by the suggestions of 166 undergraduate students. Then another group of 185 undergraduate students evaluated each word/phrase in a valence and arousal scale. An analysis by student gender and personality was conducted. Also, a clusterization analysis was performed to categorize the words/phrases.