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Analogical Transfer, 2014

Analogical transfer is a theory in psychology concerned with overcoming fixed ways of viewing particular problems or objects. In security, this problem is manifested in one example by system developers and administrators overlooking critical security requirements due to lack of tools and techniques that allow them to tailor security knowledge to their particular context. The works cited here use analogy and simulations to achieve break-through thinking. The topic relates to the hard problem of human factors in the Science of Security.  These works were presented in 2014.

Ashwini Rao, Hanan Hibshi, Travis Breaux, Jean-Michel Lehker, Jianwei Niu; “Less Is More?: Investigating The Role Of Examples In Security Studies Using Analogical Transfer;” HotSoS '14 Proceedings of the 2014 Symposium and Bootcamp on the Science of Security, April 2014, Article No. 7. Doi: 10.1145/2600176.2600182
Abstract: Information system developers and administrators often overlook critical security requirements and best practices. This may be due to lack of tools and techniques that allow practitioners to tailor security knowledge to their particular context. In order to explore the impact of new security methods, we must improve our ability to study the impact of security tools and methods on software and system development. In this paper, we present early findings of an experiment to assess the extent to which the number and type of examples used in security training stimuli can impact security problem solving. To motivate this research, we formulate hypotheses from analogical transfer theory in psychology. The independent variables include number of problem surfaces and schemas, and the dependent variable is the answer accuracy. Our study results do not show a statistically significant difference in performance when the number and types of examples are varied. We discuss the limitations, threats to validity and opportunities for future studies in this area.
Keywords: analogical transfer, human factors, psychology, security (ID#: 15-5697)


Lixiu Yu, Aniket Kittur, Robert E. Kraut; “Distributed Analogical Idea Generation: Inventing With Crowds;” CHI '14 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, April 2014,Pages 1245-1254. Doi: 10.1145/2556288.2557371
Abstract: Harnessing crowds can be a powerful mechanism for increasing innovation. However, current approaches to crowd innovation rely on large numbers of contributors generating ideas independently in an unstructured way. We introduce a new approach called distributed analogical idea generation, which aims to make idea generation more effective and less reliant on chance. Drawing from the literature in cognitive science on analogy and schema induction, our approach decomposes the creative process in a structured way amenable to using crowds. In three experiments we show that distributed analogical idea generation leads to better ideas than example-based approaches, and investigate the conditions under which crowds generate good schemas and ideas. Our results have implications for improving creativity and building systems for distributed crowd innovation.
Keywords: analogy, creativity, crowdsourcing, innovation, schema (ID#: 15-5698)


Lixiu Yu, Aniket Kittur, Robert E. Kraut; “Searching For Analogical Ideas with Crowds;” CHI '14 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, April 2014, Pages 1225-1234. Doi: 10.1145/2556288.2557378
Abstract: Seeking solutions from one domain to solve problems in another is an effective process of innovation. This process of analogy searching is difficult for both humans and machines. In this paper, we present a novel approach for re-presenting a problem in terms of its abstract structure, and then allowing people to use this structural representation to find analogies. We propose a crowdsourcing process that helps people navigate a large dataset to find analogies. Through two experiments, we show the benefits of using abstract structural representations to search for ideas that are analogous to a source problem, and that these analogies result in better solutions than alternative approaches. This work provides a useful method for finding analogies, and can streamline innovation for both novices and professional designers.
Keywords: analogy searching, creativity, crowdsourcing, schema (ID#: 15-5699)


Ian Dunwell, Sara de Freitas, Panagiotis Petridis, Maurice Hendrix, Sylvester Arnab, Petros Lameras, Craig Stewart; “A Game-Based Learning Approach To Road Safety: The Code Of Everand;” CHI '14 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, April 2014, Pages 3389-3398. Doi: 10.1145/2556288.2557281
Abstract: Game and gamification elements are increasingly seeing use as part of interface designs for applications seeking to engage and retain users whilst transferring information. This paper presents an evaluation of a game-based approach seeking to improve the road safety behaviour amongst children aged 9-15 within the UK, made available outside of a classroom context as an online, browser-based, free-to-play game. The paper reports on data for 99,683 players over 315,882 discrete logins, supplemented by results from a nationally-representative survey of children at UK schools (n=1,108), an incentivized survey of the player-base (n=1,028), and qualitative data obtained through a series of one-to-one interviews aged 9-14 (n=28). Analysis demonstrates the reach of the game to its target demographic, with 88.13% of players within the UK. A 3.94 male/female ratio was observed amongst players surveyed, with an age distribution across the target range of 9-15. Noting mean and median playtimes of 93 and 31 minutes (n=99,683), it is suggested such an approach to user engagement and retention can surpass typical contact times obtained through other forms of web-based content. The size of the player-base attracted to the game and players' qualitative feedback demonstrates the potential for serious games deployed on a national scale.
Keywords: attitudinal change, e-learning, game-based interfaces, gamification, road safety, serious games (ID#: 15-5700)


Dina Zayan, Michał Antkiewicz, Krzysztof Czarnecki; “Effects Of Using Examples On Structural Model Comprehension: A Controlled Experiment;” ICSE 2014 Proceedings of the 36th International Conference on Software Engineering, May 2014, Pages 955-966. Doi: 10.1145/2568225.2568270
Abstract: We present a controlled experiment for the empirical evaluation of Example-Driven Modeling (EDM), an approach that systematically uses examples for model comprehension and domain knowledge transfer. We conducted the experiment with 26 graduate and undergraduate students from electrical and computer engineering (ECE), computer science (CS), and software engineering (SE) programs at the University of Waterloo. The experiment involves a domain model, with UML class diagrams representing the domain abstractions and UML object diagrams representing examples of using these abstractions. The goal is to provide empirical evidence of the effects of suitable examples in model comprehension, compared to having model abstractions only, by having the participants perform model comprehension tasks. Our results show that EDM is superior to having model abstractions only, with an improvement of 39% for diagram completeness, 30% for questions completeness, 71% for efficiency, and a reduction of 80% for the number of mistakes. We provide qualitative results showing that participants receiving model abstractions augmented with examples experienced lower perceived difficulty in performing the comprehension tasks, higher perceived confidence in their tasks' solutions, and asked fewer clarifying domain questions, a reduction of 90%. We also present participants' feedback regarding the usefulness of the provided examples, their number and types, as well as, the use of partial examples.
Keywords: EDM, Structural domain model comprehension, abstraction, controlled experiment, example, example-driven modeling (ID#: 15-5701)


Paul André, Aniket Kittur, Steven P. Dow; “Crowd Synthesis: Extracting Categories And Clusters From Complex Data;” CSCW '14 Proceedings of the 17th ACM Conference On Computer Supported Cooperative Work & Social Computing, February 2014, Pages 989-998. Doi: 10.1145/2531602.2531653
Abstract: Analysts synthesize complex, qualitative data to uncover themes and concepts, but the process is time-consuming, cognitively taxing, and automated techniques show mixed success. Crowdsourcing could help this process through on-demand harnessing of flexible and powerful human cognition, but incurs other challenges including limited attention and expertise. Further, text data can be complex, high-dimensional, and ill-structured. We address two major challenges unsolved in prior crowd clustering work: scaffolding expertise for novice crowd workers, and creating consistent and accurate categories when each worker only sees a small portion of the data. To address these challenges we present an empirical study of a two-stage approach to enable crowds to create an accurate and useful overview of a dataset: A) we draw on cognitive theory to assess how re-representing data can shorten and focus the data on salient dimensions; and B) introduce an iterative clustering approach that provides workers a global overview of data. We demonstrate a classification-plus-context approach elicits the most accurate categories at the most useful level of abstraction.
Keywords: categorization, classification, clustering, crowd, synthesis (ID#: 15-5702)


Susan G. Campbell, J. Isaiah Harbison, Petra Bradley, Lelyn D. Saner; “Cognitive Engineering Analysis Training: Teaching Analysts To Use Expert Knowledge Structures As A Tool To Understanding;” HCBDR '14 Proceedings of the 2014 Workshop on Human Centered Big Data Research, April 2014, Pages 9. Doi: 10.1145/2609876.2609879
Abstract: One of the challenges of using big data to produce useful intelligence is that the task of intelligence analysis is hard to conceptualize and to learn. This extended abstract from the Human-Centered Big Data Research workshop describes a research program for eliciting experts' representations of problems in intelligence analysis and transferring those representations to other analysts. The program has five steps: (1) identify experts, (2) elicit experts' mental models, (3) represent experts' mental models, (4) create training to teach those mental models, and (5) include those mental models in tools designed to help analysts. Similar types of training, which use cognitive task analysis to produce curricula that allow novices to perform tasks using methods derived from expert performance, have been successful in other cognitively complex domains. We propose a way to use this kind of elicitation and training to extend expertise in intelligence analysis.
Keywords: Big Data, cognitive engineering, expert performance, expert representation, human cognition, instructional design, intelligence analysis, interface design, mental models (ID#: 15-5703)


Susannah B. F. Paletz; “Multidisciplinary Teamwork and Big Data;” HCBDR '14 Proceedings of the 2014 Workshop on Human Centered Big Data Research, April 2014, Pages 32. Doi: 10.1145/2609876.2609884
Abstract: In this presentation, I discuss four constructs vital to successful multidisciplinary teamwork: shared mental models, communicating unique information, conflict, and analogy. I highlight the literature and provide lessons learned for each.
Keywords: Teams, analogy, communication, conflict, disagreement, shared mental models, teamwork, unique information, unshared information (ID#: 15-5704)


Julie S. Hui, Michael D. Greenberg, Elizabeth M. Gerber; “Understanding the Role of Community In Crowdfunding Work;” CSCW '14 Proceedings of the 17th ACM Conference On Computer Supported Cooperative Work & Social Computing, February 2014, Pages 62-74. Doi: 10.1145/2531602.2531715
Abstract: Crowdfunding provides a new opportunity for entrepreneurs to launch ventures without having to rely on traditional funding mechanisms, such as banks and angel investing. Despite its rapid growth, we understand little about how crowdfunding users build ad hoc online communities to undertake this new way of performing entrepreneurial work. To better understand this phenomenon, we performed a qualitative study of 47 entrepreneurs who use crowdfunding platforms to raise funds for their projects. We identify community efforts to support crowdfunding work, such as providing mentorship to novices, giving feedback on campaign presentation, and building a repository of example projects to serve as models. We also identify where community efforts and technologies succeed and fail at supporting the work in order to inform the design of crowdfunding support tools and systems.
Keywords: community, crowd work, distributed work, entrepreneurship, rowdfunding, support tools (ID#: 15-5705)



Elie Raad, Joerg Evermann; “Is Ontology Alignment Like Analogy?: Knowledge Integration with LISA;” SAC '14 Proceedings of the 29th Annual ACM Symposium on Applied Computing, March 2014, Pages 294-301. Doi: 10.1145/2554850.2554853
Abstract: Ontologies are formal descriptions of a domain. With the growth of the semantic web, an increasing number of related ontologies with overlapping domain coverage are available. Their integration requires ontology alignment, a determination of which concepts in a source ontology are like concepts in a target ontology. This paper presents a novel approach to this problem by applying analogical reasoning, an area of cognitive science that has seen much recent work, to the ontology alignment problem. We investigate the performance of the LISA cognitive analogy algorithm and present results that show its performance relative to other algorithms.
Keywords: LISA, alignment, analogy, cognition, ontology (ID#: 15-5706)


Jens Kaasbøll; “Suitability of Diagrams for IT User Learning;” ISDOC '14 Proceedings of the International Conference on Information Systems and Design of Communication, May 2014, Pages 56-62. Doi: 10.1145/2618168.2618177
Abstract: Training and user documentation aim at people being able to use IT when returning from training. Such transfer is in general difficult to achieve. Based on a model of IT use learning, two types of diagrams in documentation were compared in a field study; instructions showing the sequence of how to carry out an operation by means of screen shots and structural models showing data structures without user interface elements. Instructions were in general favoured. Even if the instructions only to a small extent were presented with projector during training, the trainees stated that they learnt a lot from these presentations. The learning outcome might have been the existence of an operation and where in the software to locate it. While primarily intended as help for understanding data structures, the trainees also used structural models as guides for carrying out operations. Instructions in particular, but also structural models were utilised by the trainees after the training sessions, hence helping transfer. Trainers should include both types of models in courses.
Keywords: competence, conceptual model, mental model, skill, training, transfer, understanding, user documentation (ID#: 15-5707)


Carine Lallemand, Kerstin Bongard-Blanchy, Ioana Ocnarescu; “Enhancing the Design Process by Embedding HCI Research into Experience Triggers;” Ergo'IA '14 Proceedings of the 2014 Ergonomie et Informatique Avancée Conference - Design, Ergonomie et IHM: Quelle Articulation Pour La Co-Conception De L'interaction, October 2014, Pages 41-48. Doi: 10.1145/2671470.2671476
Abstract: Over the last decade, User Experience (UX) has become a core concept in the field of Human-Computer Interaction (HCI). Beyond the fact of understanding and assessing the User Experience derived from the use of interactive systems, practitioners and researchers from a wide range of disciplines are now facing the challenges of designing for User Experience.  Some authors have pinpointed the existence of a gap between the theoretical knowledge developed in HCI Research and the practical knowledge actually used by designers to create rich experiences with interactive artefacts. A special focus of this paper is to translate theoretical work into experiential objects (or situations) called "Experience Triggers" [1]. Through their materiality, these artefacts bring emotions and sensations to the design process and designers can immerge into and understand the theories on experience. As a consequence of this immersion, the final product designed by the team is assumed to be more experiential. Experience Triggers are introduced here as a new tool for science-based UX design.
Keywords: HCI research, design, experience triggers, materiality, science-based design, user experience (ID#: 15-5708)


Michael Nebeling, Matthias Geel, Moira C. Norrie; “Engineering Information Management Tools by Example;” AVI '14 Proceedings of the 2014 International Working Conference on Advanced Visual Interfaces, May 2014, Pages 313-320.  Doi: 10.1145/2598153.2598164
Abstract: While there are many established methodologies for information systems development, designing by example has not been formally explored and applied previously. Our work is also motivated by the desire to explore interface-driven development techniques that could complement existing approaches such as model-driven engineering with the goal of reducing the need for modelling and reengineering of existing applications and interfaces, while still supporting the development task. We explore the example-based technique for rapid development of powerful and flexible information management tools based on the example of Adobe Photoshop Lightroom, a system that was originally designed to support the workflow of digital photographers in a flexible way. We analyse experiments in which two new systems---one for managing collections of research papers and another for software project management---were developed based on the Lightroom paradigm. We derive a conceptual framework for engineering by example and assess the method by comparing it to traditional model-driven engineering.
Keywords: engineering by example, lightroom paradigm (ID#: 15-5709)


Hannu Jaakkola, Timo Mäkinen, Anna Eteläaho; “Open Data: Opportunities and Challenges;” CompSysTech '14 Proceedings of the 15th International Conference on Computer Systems and Technologies, June 2014, Pages 25-39. Doi: 10.1145/2659532.2659594
Abstract: Open data is seen as a promising source of new business, especially in the SME sector, in the form of new products, services and innovative solutions. High importance is seen also in fostering citizens' participation in political and social life and increasing the transparency of public authorities. The forerunners of the open data movement in the public sector are the USA and the UK, which started to open their public data resources in 2009. The first European Union open data related directive was drawn up as early as 2003; however progress in putting the idea into practice has been slow and adoptions by the wider member states are placed in the early 2010s. The beneficial use of open data in real applications has progressed hand in hand with the improvement of other ICT-related technologies. The (raw) data itself has no high value. The economic value comes from a balanced combination of high quality open (data) resources combined with the related value chain. This paper builds up a "big picture" of the role of open data in current society. The approach is analytical and it clarifies the topic from the viewpoints of both opportunities and challenges. The paper covers both general aspects related to open data and results of the research and regional development project conducted by the authors.
Keywords: big data, data analysis, networking, open data, public data (ID#: 15-5710)


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