Bowen Hui

Requirements analysis for customizable software: a goals-skills-preferences framework

Software customization has been argued to benefit both the productivity of software engineers and end users. However, most customization methods rely on specialists to manually tweak individual applications for a specific user group. Existing software development methods also fail to acknowledge the importance of different kinds of user skills and preferences and how these might be incorporated into a customizable software design. We propose a framework for performing requirements analysis on user goals, skills, and preferences in order to generate a customizable software design. We illustrate our methodology with an email system and review an on-going case study involving users with traumatic brain injury.

A probabilistic mental model for estimating disruption

Adaptive software systems are intended to modify their appearance, performance or functionality to the needs and preferences of different users. A key bottleneck in building effective adaptive systems is accounting for the cost of disruption to a users mental model of the application caused by the systems adaptive behaviour. In this work, we propose a probabilistic approach to modeling the cost of disruption. This allows an adaptive system to tradeoff disruption cost with expected savings (or other benefits) induced by a potential adaptation in a principled, decision-theoretic fashion. We conducted two experiments with 48 participants to learn model parameters in an adaptive menu selection environment. We demonstrate the utility of our approach in simulation and usability studies. Usability results with 8 participants suggest that our approach is competitive with other adaptive menus w.r.t. task performance, while providing the ability to reduce disruption and adapt to user preferences.

The Need to Support Independent Student-Directed Learning

As new educational paradigms, such as blended learning, flipped classrooms, and flexible learning, become mainstream, it is important to have the proper tools in place to support methods of student-initiated and student-directed learning. In this paper, we present a prototype of an online educational tool called the Concept Navigator that is designed to provide students with an explicit and visual representation of the core concepts and learning outcomes of a course. Unlike typical course websites where materials are structured linearly based on an academic calendar, course materials in the Concept Navigator are associated to a set of core concepts presented via a visual, pedagogical tool called the concept map. By digitizing a course concept map and integrating it with existing online functionality such as individualized learning paths, real-time performance and progress monitoring, and data analytics for course management, the Concept Navigator will improve the learning experience in new student-centric paradigms.

The need for an interaction cost model in adaptive interfaces

The development of intelligent assistants has largely benefited from the adoption of decision-theoretic (DT) approaches that enable an agent to reason and account for the uncertain nature of user behaviour in a complex software domain. At the same time, most intelligent assistants fail to consider the numerous factors relevant from a human-computer interaction perspective. While DT approaches offer a sound foundation for designing intelligent agents, these systems need to be equipped with an interaction cost model in order to reason the impact of how (static or adaptive) interaction is perceived by different users. In a DT framework, we formalize four common interaction factors --- information processing, savings, visual occlusion, and bloat. We empirically derive models for bloat and occlusion based on the results of two users experiments. These factors are incorporated in a simulated help assistant where decisions are modeled as a Markov decision process. Our simulation results reveal that our model can easily adapt to a wide range of user types with varying preferences.

Engaging Higher Order Thinking Skills with a Personalized Physics Tutoring System

Recent research shows a lack of student interest and declined enrollment in physics. Our system offers four levels of difficulty with activities that enable students to exercise a range of lower and higher order cognitive skills. Moreover, we adopt existing methods in probabilistic user modeling to provide personalized help. Our work models both domain concepts as well as user attitudes.

Extracting Conceptual Relationships from Specialized Documents

Conceptual modeling has been fundamental to the management of structured data. However, its value is increasingly being recognized for knowledge management in general. In trying to develop suitable conceptual models for unstructured information, issues such as the level of representation and complexity of processing techniques arise. Here, we investigate the use of a conceptual model that is simple enough to allow efficient automatic extraction from documents. Our model focused on the problem-solution relationship that is central to the analysis of scientific papers. It also consists of supporting relationships such as benefits and drawbacks, assumptions, methods, extensions, and claims. Our study considered two kinds of documents - scientific research papers and patents. We evaluated the utility of the approach by building a prototype system and our user evaluation shows promising results.