An Improved Usability Measure Based on Novice and Expert Performance

The novice–expert ratio method (NEM) pinpoints user interface design problems by identifying the steps in a task that have a high ratio of novice to expert completion time. This study tested the construct validity of NEM's ratio measure against common alternatives. Data were collected from 337 participants who separately performed 10 word-completion tasks on a cellular phone interface. The logarithm, ratio, Cohen's d, and Hedges's ? measures had similar construct validity, but Hedges's ? provided the most accurate measure of effect size. All these measures correlated more strongly with self-reported interface usability and interface knowledge when applied to the number of actions required to complete a task than when applied to task completion time. A weighted average of both measures had the highest correlation. The relatively high correlation between self-reported interface usability and a weighted Hedges's ? measure as compared to the correlations found in the literature indicates the usefulness of the weighted Hedges's ? measure in identifying usability problems.     

Towards Collaboration Maturity in Business Processes: An Exploratory Study in Oil Production Processes

Abstract

Organizations have been relying on collaboration for knowledge sharing and productivity improvement in order to reduce costs or boost revenue. However, organizations still cannot assure that collaboration is properly conducted in daily work. This paper presents an approach to stimulating collaboration between professionals in an organization. The approach, combining a BPM methodology with the CollabMM collaboration maturity model and its corresponding method, is a result of an exploratory study in a real setting in an oil company in Brazil. The project is a move towards improving decision-making during one of the company's business processes and establishing collaboration among professionals through information sharing.     

Opening up the Solution Space: The Role of Analogical Thinking for Breakthrough Product Innovation

The purpose of this paper is to investigate the approach of analogical thinking for product innovation. We collected data on projects from four engineering firms where analogical thinking was successfully applied for the development of breakthrough innovations. Results show that abstracting the problem by in‐depth technical and contextual analysis is pivotal when searching for analogical solutions. Furthermore, the chances of identifying highly novel analogous solutions are increased if the problem is abstracted to the level of its structural similarities to other settings. We also found that the identification of structural similarities is supported when firms not only rely on the cognitive abilities of the individual but also employ an active search based on abstract search terms. Based on these insights, we propose a process model for the development of product innovations by means of analogical thinking.     

Evocation: analyzing and propagating a semantic link based on free word association

Studies of lexical–semantic relations aim to understand the mechanism of semantic memory and the organization of the mental lexicon. However, standard paradigmatic relations such as “hypernym” and “hyponym” cannot capture connections among concepts from different parts of speech. WordNet, which organizes synsets (i.e., synonym sets) using these lexical–semantic relations, is rather sparse in its connectivity. According to WordNet statistics, the average number of outgoing/incoming arcs for the hypernym/hyponym relation per synset is 1.33. Evocation, defined as how much a concept (expressed by one or more words) brings to mind another, is proposed as a new directed and weighted measure for the semantic relatedness among concepts. Commonly applied semantic relations and relatedness measures do not seem to be fully compatible with data that reflect evocations among concepts. They are compatible but evocation captures MORE. This work aims to provide a reliable and extendable dataset of concepts evoked by, and evoking, other concepts to enrich WordNet, the existing semantic network. We propose the use of disambiguated free word association data (first responses to verbal stimuli) to infer and collect evocation ratings. WordNet aims to represent the organization of mental lexicon, and free word association which has been used by psycholinguists to explore semantic organization can contribute to the understanding. This work was carried out in two phases. In the first phase, it was confirmed that existing free word association norms can be converted into evocation data computationally. In the second phase, a two-stage association-annotation procedure of collecting evocation data from human judgment was compared to the state-of-the-art method, showing that introducing free association can greatly improve the quality of the evocation data generated. Evocation can be incorporated into WordNet as directed links with scales, and benefits various natural language processing applications.     

Infodynamics: Analogical analysis of states of matter and information

This paper expands and consolidates the use of analogies in thermodynamics to explore concepts in the characterization of information systems. The analogy spans the range of information systems to include databases, knowledge bases and model bases. It includes but is not limited to pressure, expressiveness, temperature, tractability, degrees of order, systems of liquid-liquid equilibrium and disjunction in information-systems integration. By taking advantage of the isomorphism that exists between states of matter and states of information, we can understand new ways to characterize and measure information systems. This paper is the fourth in a series describing new aspects of “infodynamics.”.     

Adapting Information Search Tools for use by Health Consumers: Challenges and Lessons for Software Designers

Consumers are increasingly using the Internet to find information to support health-related decisions. This study evaluated two software tools, Mr. Taggy and SparTag.us, which are designed to help users filter and identify relevant information and organize and make sense of information. Participants included 80 adults, aged 35–82, who performed health information search tasks. Twenty participants used Mr. Taggy and another 20 used Spartag.us. The search tasks varied according to the tool characteristics. Two corresponding control groups, each with 20 participants, performed the tasks using the Google search engine. Participants also completed cognitive ability tests and a usability questionnaire. The findings indicated that participants using the tools found them useful and usable. However, participants using Google had significantly better task performance. Although cognitive abilities were related to task performance, a wider variety of abilities were related to performance in the aided conditions compared with the unaided conditions. The findings underscore the importance of ensuring that the cognitive demands associated with software tools do not outweigh the benefits of these tools, especially for users who are not highly practiced at using them.     

Story Planning: Creativity Through Exploration,Retrieval, and Analogical Transformation

Storytelling is a pervasive part of our daily lives and culture. The task of creating stories for the purposes of entertaining, educating, and training has traditionally been the purview of humans. This sets up the conditions for a creative authoring bottleneck where the consumption of stories outpaces the production of stories by human professional creators. The automation of story creation may scale up the ability to produce and deliver novel, meaningful story artifacts. From this practical perspective, story generation systems replicate the creative abilities of humans and can thus be considered instances of computational creativity. Computational systems that are purported to be creative typically utilize one of three general approaches: exploration of a space of concepts, combination of concepts, and transformation of concepts. In this article we present an approach to story generation that utilize exploration, combination, and transformation. Our approach, implemented in the Vignette Based Partial Order Causal Link story planner, is an algorithm that searches through a space of possible story solutions, guided by combinations of existing story fragments called vignettes. The vignettes are made relevant to novel story generation contexts through an automated transformation pre-process. Through these processes, we show that story generation can incorporate multiple perspectives on computational creativity. Our approach is presented at both the theoretical and technical levels.     

Legitimate by design: Towards trusted socio-technical systems

Legitimacy or 'fairness' seems a key requirement for trust in computer-mediated social environments. Trust in turn seems necessary for productive community interactions like e-commerce. But unless legitimacy is built into social software, achieving trust may not be possible. This means expressing apparently vague social 'rights' as specific information system (IS) requirements, i.e. carrying out a legitimacy analysis. We suggest a framework for the systematic analysis of who 'owns' what in IS design, assuming basic object types and actions. This analysis not only allows social legitimacy concepts to be expressed in IS design terms, but could also reveal socio-technical system design choices for public review. The technique is illustrated by case examples. Legitimacy analysis can apply to wide variety of social software, from chat rooms to virtual realities. It could lead to future global standards for virtual social environment design, perhaps necessary for the emergence of a global online community.     

Committees, collectives and individuals: Expert visual classification by neural network

It has been established that committee classifiers, in which the outputs of different, individual network classifiers are combined in various ways, can produce better accuracy than the best individual in the committee. We describe results showing that these advantages are obtained when neural networks are applied to a taxonomic problem in marine science: the classification of images of marine phytoplankton. Significant benefits were found when individual networks, trained on different classes of input, having comparable individual performances, were combined. Combining networks of very different accuracy did not improve performance when measured against the best single network, but nor was it reduced. An alternative architecture, which we term a collective machine, in which the different data types are combined in a single network, was found to have significantly better accuracy than the committee machine architectures. The performance gains and resilience to non-discriminatory types of data suggest the techniques have great utility in the development of general purpose, network classifiers.     

CSCL for intellectually disabled pupils: Stimulating interaction by using a floor control mechanism

Computer-supported collaborative learning has an unexploited potential of becoming an effective learning method for pupils with intellectual disabilities. This paper aims at showing how some specific requirements of this target group may be met by structuring a learning situation with the help of floor control, which restricts the opportunities of a learning dyad to act simultaneously within the learning environment. It was expected that floor control could improve communication between pupils with intellectual disabilities by explicitly structuring and restricting activities in the learning environment and making it necessary for the pupils to communicate. To examine if floor control really supports the collaboration process in the hypothesized way, two different versions of a CSCL environment were implemented and compared. The results revealed improved task-related communication and a higher quality of interaction outcomes.     

Expert systems and organizations: Predictions and evidence

Expert systems have recently become more commonplace in the United States workplace. Although little is known about the impacts of expert systems in the workplace, many predictions about the nature of these impacts, and the effects of expert systems generally, have been made. We examine some of these predictions, noting that they are very similar to predictions made about the effects of conventional data processing in organizations. We examine how most of the predictions about data processing have been shown to be unfounded, based on empirical research. We also show, using the limited empirical evidence available relating to expert systems and their organizational effects, that most of the predictions made about them are also unfounded.     

Pushdown Processes: Games and Model-Checking

A pushdown game is a two player perfect information infinite game on a transition graph of a pushdown automaton. A winning condition in such a game is defined in terms of states appearing infinitely often in the play. It is shown that if there is a winning strategy in a pushdown game then there is a winning strategy realized by a pushdown automaton. An EXPTIME procedure for finding a winner in a pushdown game is presented. The procedure is then used to solve the model-checking problem for the pushdown processes and the propositional μ-calculus. The problem is shown to be DEXPTIME-complete.     

Stimulating the Potential: Creative Performance and Communication in Innovation Teams

Creativity is essential to successful new product development efforts. Teams constitute the organizing principle in most modern innovation activities. Although creativity research has revealed many factors influencing individual creativity, little is known about how team‐level creativity is determined. Since the creative innovation task requires teams to combine and integrate input from multiple team members, the team's communication pattern is an important determinant of team creativity. Based on a sample of 44 NPD teams in eleven companies, this study examines the effects of team‐member communication on team creativity. It is found that both interaction frequency and subgroup‐formation of communication have a negative relationship to team creativity. Theoretical and practical implications are discussed, and further research is indicated.     

LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning

A new class of evolutionary computation processes is presented, called Learnable Evolution Model or LEM. In contrast to Darwinian-type evolution that relies on mutation, recombination, and selection operators, LEM employs machine learning to generate new populations. Specifically, in Machine Learning mode, a learning system seeks reasons why certain individuals in a population (or a collection of past populations) are superior to others in performing a designated class of tasks. These reasons, expressed as inductive hypotheses, are used to generate new populations. A remarkable property of LEM is that it is capable of quantum leaps (insight jumps) of the fitness function, unlike Darwinian-type evolution that typically proceeds through numerous slight improvements. In our early experimental studies, LEM significantly outperformed evolutionary computation methods used in the experiments, sometimes achieving speed-ups of two or more orders of magnitude in terms of the number of evolutionary steps. LEM has a potential for a wide range of applications, in particular, in such domains as complex optimization or search problems, engineering design, drug design, evolvable hardware, software engineering, economics, data mining, and automatic programming.     

Facet Grammars: Towards static semantic analysis by context-free parsing

Use of executable declarative metalanguages has simplified programming language syntax specification and implementation, whereas existing formalisms for static semantics are still relatively procedural. A working hypothesis is that the context-sensitivity of languages (under static semantic rules) is derived in significant part from the interleaved presences therein of sentences in implicitly-defined and effectively invisible context-free languages. Procedures by which these sentences and context-free grammars for their languages can be respectively derived from the original sentence and the combination of the original language's grammar and semantic rules, lead to the possibility of automatic generation of static semantic analysers from the purely context-free specifications of “Facet Grammars” (FG)!

We show that the utility of FG for static semantic analysis has a non-trivial lower bound, by specifying the relatively complicated identifier scope and accessibility rules for Dijkstra's Guarded Commands Language.     

Expert systems in manufacturing: Promises and perils

Expert systems play a key role as a tool to enhance productivity, improve quality, increase profits and capture expertise in many business and industrial environments. Although the potential of expert systems is well-understood, it has been a challenging, and often elusive, task to make an expert system operational in the “real-world” environment. One of the major reasons why many expert systems fail to be integrated into the operational environment is a lack of knowledge and techniques on how to test an expert system. The implications of an inadequately-tested expert system in a manufacturing environment are serious. In this paper, we take a look at the many applications of expert systems in manufacturing and utilize a three-pronged approach to develop robust and reliable expert systems.     

Geometry and Rhetoric: Thinking about Thinking in Pictures

Thinking about thinking is tricky business. Pitfalls include a tendency to confuse our metaphors with the act itself, difficulties attendant to discredited notions of introspection as a source of evidence and the twin unreasonablenesses of reductive scientists and mystical humanists. Engaging geometry and rhetoric in a common frame presents the opportunity, especially in the context of architecture, to consider discourse and image in ways that are mutually reinforcing.