Usability Problem Reports for Comparative Studies: Consistency and Inspectability

ABSTRACT

This study explores issues of consistency and inspectability in usability test data analysis processes and reports. Problem reports resulting from usability tests performed by three professional usability labs in three different countries are compared. Each of the labs conducted a usability test on the same product, applying an agreed test protocol that was collaboratively developed by the labs. Each lab first analyzed their own findings as they always do in their regular professional practice. A few weeks later, they again analyzed their findings, but then everyone applied the same method (SlimDEVAN: a simplified version of DEVAN, a method developed for facilitating comparison of findings from usability tests in an academic setting). It was found that levels of agreement between labs did not improve when they all used SlimDEVAN, suggesting that there was inherent subjectivity in their analyses. It was found that consistency of single analyst teams varied considerably and that a method like SlimDEVAN can help in making the analysis process and findings more inspectable. Inspectability is helpful in comparative studies based on identified usability problems because it allows for tracing back findings to original observations as well as for laying bare the subjective parts of the data analysis.     

协同过滤推荐系统中数据稀疏问题的解决

介绍了现有协同过滤推荐的几种主要算法.它们对数据稀疏性问题都有一定的缓和作用.通过在数据集MovieLens上的实验,分析了各个算法在不同稀疏度下的推荐质量,为针对不同数据稀疏度的系统实现提供了可靠依据.     

Collaborative Problem Solving and Groupware for Software Development

When a group is working together toward a common goal, communication and collaboration are key. This is especially true in software development where a project of any significance involves groups of people. Collaborative problem-solving techniques and groupware technology can be a boon to software development because they enhance communication and cooperation. This article examines how software development is affected by collaborative problem solving and decision making, groupware theory and tools, and group cognition and psychology. It also analyzes collaboration tools and their correlation to problem solving and group characteristics.     

A Collaborative Usability Evaluation (CUE) Model for Health IT Design and Implementation

ABSTRACT

In this article, the authors describe a collaborative usability evaluation (CUE) model based on a participatory ergonomics framework. The model was applied to an organization’s health IT design and implementation processes. Individuals received education on usability evaluation principles and methods and had an opportunity for hands-on experience with usability testing. Actual usability evaluations increased participant confidence in their ability to independently design and lead such evaluations. The CUE model has since been applied to the organization’s iterative process of health IT design, has been institutionalized by the health IT department, and is integrated in its health IT implementation timelines.     

Decidability of the Analysis Problem for Dataflow Models of Programs

In the paper, algorithmic decidability of the analysis problem for dataflow models of programs is studied. The solution of the problem is the least stable marking on upper semi-lattice of labels of arcs and vertices of the generalized dataflow graph of the program. The labels are allowed to be of arbitrary semantic nature. Whether the analysis problem is decidable depends on properties of the marking function that interprets the analysis scheme given by a system of functional equations.     

Rough Problem Settings for ILP Dealing With Imperfect Data

This paper applies rough set theory to Inductive Logic Programming (ILP, a relatively new method in machine learning) to deal with imperfect data occurring in large real-world applications. We investigate various kinds of imperfect data in ILP and propose rough problem settings to deal with incomplete background knowledge (where essential predicates/clauses are missing), indiscernible data (where some examples belong to both sets of positive and negative training examples), missing classification (where some examples are unclassified) and too strong declarative bias (hence the failure in searching for solutions). The rough problem settings relax the strict requirements in the standard normal problem setting for ILP, so that rough but useful hypotheses can be induced from imperfect data. We give simple measures of learning quality for the rough problem settings. For other kinds of imperfect data (noise data, too sparse data, missing values, real-valued data, etc.), while referring to their traditional handling techniques, we also point out the possibility of new methods based on rough set theory.     

Awareness Support for Combining Individual and Collaborative Process Design in Co-located Meetings

The collaborative design of complex systems is a challenging task. It requires phases of linear as well as creativity oriented work. Also phases of collaborative work have to alternate with work in solitude, requiring a smooth transition between them. This in turn results in awareness becoming a crucial factor. Within the context of designing socio-technical processes through modeling we have developed tools and methods to integrate individual and collaborative creativity into modeling with special respect to awareness thus allowing for a smooth transition between phases of working in solitude and phases of collaboration. We have conducted multiple experiments on the subject following an action research approach which allowed for reflecting on the influence of awareness on collaborative process design in co-located meetings while also improving the socio-technical setting they were applied in. Derived from our findings we show requirements for further development of the socio-technical setting and show future directions such as the integration of the described setting into other areas of design.     

Accelerated Problem Solving Sessions in University Laboratory Settings

Current engineering students receive an extensive education in the technical principles associated with the design and manufacturing of products and services. However, collaboration, communication and innovative thinking are not often stressed in traditional engineering curriculums. This study proposes a new interdisciplinary, group-based educational approach to build additional competence for accelerated problem solving and innovative thinking in the future generation of engineers and support the United States’ transition towards a knowledge economy.     

房地产行业诚信信息系统研究与实现

针对市场经济下的信用意识和信用道德观念不强,房地产市场存在监督机制不健全,致使房地产企业和从业人员诚信缺失现象严重的现状,研发一套房地产行业诚信系统,为推动房地产市场的良好发展提供平台。基于该系统的房地产企业诚信指标体系,为综合评价房地产企业诚信度打下基础,科学合理地构建房地产行业诚信指标体系极具现实意义。     

协同环境下CAD模型的多层次动态安全访问控制

提出一个专门针对协同环境下CAD模型的多层次动态的安全访问控制(multi-level and dynamic security access control,简称MLDAC)模型.该模型利用一种多层次的权限模型,以简化权限定义及其分配过程,丰富了权限表达能力,实现了产品模型的多粒度访问控制.通过参照工作流的基本理念,引入权限的依赖关系及权限状态迁移概念,实现了权限的动态授权管理.通过实践证明,MLDAC模型可以对协同设计操作进行更加有效的控制,符合设计任务间的分工性、依赖性和交互性的特点.     

Verification of Compliance for Multilevel Models in Individual Trace Semantics

Programming and Computer Software - This paper presents new verification techniques that target to prove multimodel compliance in terms of individual trace semantics. As compared to traditional...     

Data-Inferred Personalized Human-Robot Models for Iterative Collaborative Output Tracking

This article studies collaborative human- robot output tracking when the desired output is only known to the human but not to the robot controller. The main contribution of this article is to propose and establish convergence conditions for an iterative learning algorithm that updates the robot input using (i) the effect of the human action on the combined human-robot output tracking (which includes the effect of the human-response dynamics) and (ii) data-inferred human-robot models. This allows the iterative learning control (ILC) to be personalized for each individual human operator. Additionally, experimental results are presented to illustrate the iterative learning approach. Results show that, with the proposed approach, the robot can learn to collaboratively track the output with 10.0% error, which is close to twice the robot noise of 4.6% of the desired output. Furthermore, the data-inferred models provided evidence of the effect of the human operator’s dynamics on the co-tracking task.     

Individual Differences and Conceptual Models in Training Novice Users

Although there is a strong theoretical basis for concluding that conceptual models are effective in aiding users build mental models of computer systems, very little empirical evidence exists to support such a conclusion. Frequently, the effect has been weak. Subjects trained with a conceptual model often perform better than control group subjects in learning tests, but seldom at a statistically significant level. One possible reason for this is the influence of individual differences such as basic cognitive abilities. This study examined the influence of two cognitive variables - visual ability and learning mode - in the mental model formation process of novice users of an electronic mail filing system. We compared the effectiveness of two types of conceptual models-analogical and abstract. It was found that high-visual subjects performed significantly better than low-visual subjects. Abstract learners also performed better than concrete learners. More important, interaction effects were observed. Low-visual subjects were severely hampered by abstract models but performed as well as high-visual subjects when provided with analogical models. Abstract learners benefited from the abstract model but were hampered by the analogical model. On the other hand, concrete learners performed better with the analogical models compared to abstract models. The findings indicate that there is a need to consider individual differences, such as visual ability and learning mode, in research on mental models and on human-computer interaction in general.     

The Realization Problem for Hidden Markov Models

If {X _t} is a finite-state Markov process, and {Y _t} is a finite-valued output process with Y _t+1 depending (possibly probabilistically) on X _t, then the process pair is said to constitute a hidden Markov model. This paper considers the realization question: given the probabilities of all finite-length output strings, under what circumstances and how can one construct a finite-state Markov process and a state-to-output mapping which generates an output process whose finite-length strings have the given probabilities? After reviewing known results dealing with this problem involving Hankel matrices and polyhedral cones, we develop new theory on the existence and construction of the cones in question, which effectively provides a solution to the realization problem. This theory is an extension of recent theoretical developments on the positive realization problem of linear system theory. Date received: December 13, 1996. Date revised: October 9, 1998.     

Constraint Models for the Covering Test Problem

Covering arrays can be applied to the testing of software, hardware and advanced materials, and to the effects of hormone interaction on gene expression. In this paper we develop constraint programming models of the problem of finding an optimal covering array. Our models exploit global constraints, multiple viewpoints and symmetry-breaking constraints. We show that compound variables, representing tuples of variables in our original model, allow the constraints of this problem to be represented more easily and hence propagate better. With our best integrated model, we are able to either prove the optimality of existing bounds or find new optimal solutions, for arrays of moderate size. Local search on a SAT-encoding of the model is able to find improved solutions and bounds for larger problems.     

A型变尺寸装箱问题之模型及算法研究

作为对经典一维装箱问题的推广,提出一种A型变尺寸装箱问题（A-shaped Variable-sized BinPacking Problem,简称A SVBP）,即在物品的装箱过程中,每样物品有高度和横截面积两个参数,并且箱子的大小不一。该问题在文件系统管理和日常生活中的运输等问题中有着广泛的应用背景。把装箱问题的经典算法以及遗传算法推广到A型变尺寸装箱问题,实验结果表明：按照本文提出的求解模式,离线情况下求解A型变尺寸装箱问题最终结果的质量取决于预先求解其退化为经典装箱问题时的算法,求解物品装箱序列时用首次适应混合遗传算法比用Next Fit算法、First Fit算法、Best Fit算法最终得到的结果要好。     

基于集成协同PSO算法的车辆路径优化仿真

为提高物流配送效率,减小配送车辆的运输成本,提出采用改进的集成协同粒子群优化(PSO)算法来对路径进行优化。根据车辆路径问题的特点,采用极坐标对路径上的节点编码,并用权重表示其先后顺序,将其转化为连续PSO算法解决该问题。并且针对标准PSO算法存在的早熟问题,通过划分子种群来提高粒子的多样性,并利用集成学习,将粒子的每个维度视为个体学习者进行结合,提高搜索精度,构建了集成协同PSO算法。理论分析和实验表明,所采用的编码方式结合改进的集成协同PSO算法可以有效解决车辆路径问题。     

Collaborative Problem Solving Through Creativity in Problem Definition: Expanding the Pie

The classic models of two party problem solving in situations of potential conflict are reviewed and the growing impetus for a process that would encourage collaborative win‐win solutions is summarized. In is demonstrated that in‐win collaboration requires innovative thinking, and that a four stage process of deliberate creativity with a track record of success is described. The process, called Simplex, emphasizes ‘out of the box’ thinking in problem defining (before solving) as the key to making a perceived ‘fixed pie’ larger, moving beyond the shackles of zero‐sum, win‐lose, compromise thinking. If a problem can be conceptualized from a new angle in such a way that each party believes its resolution would provide a high level of satisfaction, then the parties will be more likely to work together collaboratively. This process uses four specific creative thinking skills. A case study is described in which the Simplex process was used in union management bargaining. In the case study, when the creative process was deliberately applied, success was achieved in building trust and developing expanded pies and new solutions. However, when the process was abandoned, the trust was lost, no creative solutions were developed, and a sub‐optimal lose‐lose situation resulted. A two‐dimensional diagnostic model which shows the relationship between skill level in the process and motivation to use it is provided. This model defines four modes of pure and mixed distributive and integrative bargaining.