A model-based framework for classifying and diagnosing usability problems

A great deal of study has been devoted to the problem of how to identify and categorize usability problems; however, there is still a lack of studies dealing with the problem of how to diagnose the causes of usability problems and how to feed them back into design process. The value of classifying usability problems can be enhanced when they are interpreted in connection with design process and activities. Thus, it is necessary to develop a systematic way of diagnosing usability problems in terms of design aspects and applying diagnosis results to improve design process and activities. With this issue in mind, this paper proposes a conceptual framework that supports a systematic classification and diagnosis of usability problems. This paper firstly reviews seven approaches to classifying usability problems. Then, we point out the needs of adopting a model-based approach to classifying and diagnosing usability problems and of developing a comprehensive framework guiding the use of model-based approaches. We then propose a conceptual framework that specifies how a model-based classification and diagnosis of usability problems should be conducted and suggests the combined use of three different types of models, each of which addresses context of use, design knowledge and design activities. Last, we explain how a sound classification scheme of usability problems can be systematically developed, and how the classification of usability problems can be connected to design process and activities on the basis of the framework

Identifying Dyscalculia Symptoms Related to Magnocellular Reasoning using Smartphones

This thesis presents a research that has developed and evaluated a software application for smartphones, called MagnoMath. MagnoMath has been developed for assisting diagnosis of dyscalculia, a learning disability in mathematics, and for identifying dyscalculia symptoms possibly related to magnocellular reasoning. Typical software aids developed for individuals with learning disabilities are focused on both assisting diagnosis and teaching the material. The software developed in this project however maintains a specific focus on the former, while attempting to capture alleged correlations between dyscalculia symptoms and possible underlying causes of the condition. This was achieved by applying k-Nearest Neighbor algorithm classifying five parameters used in evaluating users' mathematical and magnocellular reasoning. Evaluation results were then utilized to support diagnosis and measure correlations between performances across the two task categories. To test MagnoMath's validity, an experiment involving seven dyscalculic and four non-dyscalculic volunteers was conducted. Results show that the software was able to reveal dyscalculic behavior. A strong correlation between deficiencies in arithmetic and spatial reasoning was identified, revealing a possible dependency valuable for detecting early signs of developmental dyscalculia. Learning disability experts at Linköping University, Sweden, University of Oslo, Norway and Dyslexia Norway have found the application to be appropriate and valuable for its intended purposes. Thus, results prove that mobile software is a suitable and valuable tool for assisting dyscalculia diagnosis and identifying possible root causes of developing the condition. Additional evaluation would be necessary to further test mobile softwares' ability to confirm the theory of magnocellular reasoning's involvement in developing mathematical deficiencies.INFO390MASV-INF

On-Cloud Motherhood Clinic: A Healthcare Management Solution for Rural Communities in Developing Countries

Background: Modern telecommunication infrastructure enables bridging of the digital divide between rural and urban healthcare services, promoting the provision of suitable medical care and support. Thus far, there has been some positive impacts to applying mobile health (m-Health) solutions, but their full potential in relation to cloud computing has is yet to be realised. It is imperative to develop an innovative approach for addressing the digital divide in a context of developing country. Method: Adopting a design science research approach (DSR), this study describes an innovative m-Health solution utilising cloud computing that enables healthcare professionals and women in rural areas to achieve comprehensive maternal healthcare support. We developed the solution framework through iterative prototyping with stakeholders’ participation, and evaluated the design using focus groups. Results: The cloud-based solution was positively evaluated as supporting healthcare professionals and service providers. It was perceived to help provide a virtual presence for evaluating and diagnosing expectant mothers’ critical healthcare data, medical history, and in providing necessary service support in a virtual clinic environment. Conclusions: The new application offers benefits to target stakeholders enabling a new practice-based paradigm applicable in other healthcare management. We demonstrated utilities to address target problems as well as the mechanism propositions for meeting the information exchange demand for better realisation of practical needs of the end users. Available at: https://aisel.aisnet.org/pajais/vol12/iss1/3

What to Fix? Distinguishing between design and non-design rules in automated tools

Technical debt---design shortcuts taken to optimize for delivery speed---is a critical part of long-term software costs. Consequently, automatically detecting technical debt is a high priority for software practitioners. Software quality tool vendors have responded to this need by positioning their tools to detect and manage technical debt. While these tools bundle a number of rules, it is hard for users to understand which rules identify design issues, as opposed to syntactic quality. This is important, since previous studies have revealed the most significant technical debt is related to design issues. Other research has focused on comparing these tools on open source projects, but these comparisons have not looked at whether the rules were relevant to design. We conducted an empirical study using a structured categorization approach, and manually classify 466 software quality rules from three industry tools---CAST, SonarQube, and NDepend. We found that most of these rules were easily labeled as either not design (55%) or design (19%). The remainder (26%) resulted in disagreements among the labelers. Our results are a first step in formalizing a definition of a design rule, in order to support automatic detection.Comment: Long version of accepted short paper at International Conference on Software Architecture 2017 (Gothenburg, SE

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments