Using recommendations to help novices to reuse design knowledge

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-21530-8_35. Copyright @ Springer 2011.The use of pattern languages is not so straightforward since its users have to identify the patterns they need, browsing the language and understanding both the benefits and trade-offs of each pattern as well as the relations and interactions it has with other patterns. Novice designers might benefit from tools that assist them in this learning task. In this paper we describe a recommendation tool embedded in a visual environment for pattern-based design which aims at suggesting patterns to help novice designers to produce better designs and understand the language.Spanish Ministry of Science and Innovatio

Knowledge Reuse for Customization: Metamodels in an Open Design Community for 3d Printing

Theories of knowledge reuse posit two distinct processes: reuse for replication and reuse for innovation. We identify another distinct process, reuse for customization. Reuse for customization is a process in which designers manipulate the parameters of metamodels to produce models that fulfill their personal needs. We test hypotheses about reuse for customization in Thingiverse, a community of designers that shares files for three-dimensional printing. 3D metamodels are reused more often than the 3D models they generate. The reuse of metamodels is amplified when the metamodels are created by designers with greater community experience. Metamodels make the community's design knowledge available for reuse for customization-or further extension of the metamodels, a kind of reuse for innovation

EXPLORING INTENTION TO REUSE RECOMMENDATION AGENTS FROM ACCESSIBILITY-DIAGNOSTICITY PERSPECTIVE: THE MODERATING EFFECT OF DOMAIN KNOWLEDGE

Recommendation agents help users reduce information overload and improve decision quality. Yet, many online shoppers have negative reaction or have no motivation to use recommendation agents, since they have no idea of whether users can achieve their shopping goals with less effort. We think information is fundamental to using recommendation agents. This study develops a research framework from the accessibility-diagnosticity perspective and proposes explanation facility, perceived similarity and information diagnosticity are important determinants of users’ intention to reuse RAs. We think explanation facility could persuade users of RAs’ performance, similarity could move users to agree with RAs, and information diagnosticity could let users be capable of evaluating RAs. We also consider the moderating role of domain knowledge on relationship of similarity and information diagnosticity. This study conducted a 2*2 factorial experiment for data collection. Results show that decision process and outcome similarity indirectly influence reuse intention by information diagnosticity and the effects of process and outcome similarity varies with degrees of users’ domain knowledge. The influence of explanation facility on similarity is not obvious. The effect of “why” explanation facility on outcome explanation is significantly contrary to our expectation. Explanation facility may have to be utilized carefully. Implications are discussed

Automatic refinement of user requirements : a case study in software tool evaluation

This paper presents an assessment of system effectiveness in automatic requirements refinement by comparing results obtained from experts and novices with those achieved by the system. As the investigated system was a combination of a tightly inter-connected methods and a tool, the evaluation framework melded together a number of distinct methodological approaches structured into three empirical studies, which aimed at the construction of a case problem domain, calibrating the system using this defined domain elements and finally using the calibrated system to assess its effectiveness. In consequence, it was concluded that the evaluated methods and tools were effective in supporting requirements refinement.<br /

Designing for change: mash-up personal learning environments

Institutions for formal education and most work places are equipped today with at least some kind of tools that bring together people and content artefacts in learning activities to support them in constructing and processing information and knowledge. For almost half a century, science and practice have been discussing models on how to bring personalisation through digital means to these environments. Learning environments and their construction as well as maintenance makes up the most crucial part of the learning process and the desired learning outcomes and theories should take this into account. Instruction itself as the predominant paradigm has to step down. The learning environment is an (if not 'the�) important outcome of a learning process, not just a stage to perform a 'learning play'. For these good reasons, we therefore consider instructional design theories to be flawed. In this article we first clarify key concepts and assumptions for personalised learning environments. Afterwards, we summarise our critique on the contemporary models for personalised adaptive learning. Subsequently, we propose our alternative, i.e. the concept of a mash-up personal learning environment that provides adaptation mechanisms for learning environment construction and maintenance. The web application mash-up solution allows learners to reuse existing (web-based) tools plus services. Our alternative, LISL is a design language model for creating, managing, maintaining, and learning about learning environment design; it is complemented by a proof of concept, the MUPPLE platform. We demonstrate this approach with a prototypical implementation and a – we think – comprehensible example. Finally, we round up the article with a discussion on possible extensions of this new model and open problems

A review and assessment of novice learning tools for problem solving and program development

There is a great demand for the development of novice learning tools to supplement classroom instruction in the areas of problem solving and program development. Research in the area of pedagogy, the psychology of programming, human-computer interaction, and cognition have provided valuable input to the development of new methodologies, paradigms, programming languages, and novice learning tools to answer this demand. Based on the cognitive needs of novices, it is possible to postulate a set of characteristics that should comprise the components an effective novice-learning tool. This thesis will discover these characteristics and provide recommendations for the development of new learning tools. This will be accomplished with a review of the challenges that novices face, an in-depth discussion on modem learning tools and the challenges that they address, and the identification and discussion of the vital characteristics that constitute an effective learning tool based on these tools and personal ideas

Towards personalization in digital libraries through ontologies

In this paper we describe a browsing and searching personalization system for digital libraries based on the use of ontologies for describing the relationships between all the elements which take part in a digital library scenario of use. The main goal of this project is to help the users of a digital library to improve their experience of use by means of two complementary strategies: first, by maintaining a complete history record of his or her browsing and searching activities, which is part of a navigational user profile which includes preferences and all the aspects related to community involvement; and second, by reusing all the knowledge which has been extracted from previous usage from other users with similar profiles. This can be accomplished in terms of narrowing and focusing the search results and browsing options through the use of a recommendation system which organizes such results in the most appropriate manner, using ontologies and concepts drawn from the semantic web field. The complete integration of the experience of use of a digital library in the learning process is also pursued. Both the usage and information organization can be also exploited to extract useful knowledge from the way users interact with a digital library, knowledge that can be used to improve several design aspects of the library, ranging from internal organization aspects to human factors and user interfaces. Although this project is still on an early development stage, it is possible to identify all the desired functionalities and requirements that are necessary to fully integrate the use of a digital library in an e-learning environment

A Mixed Method Approach for Evaluating and Improving the Design of Learning in Puzzle Games

Despite the acknowledgment that learning is a necessary part of all gameplay, the area of Games User Research lacks an established evidence based method through which designers and researchers can understand, assess, and improve how commercial games teach players game-specific skills and information. In this paper, we propose a mixed method procedure that draws together both quantitative and experiential approaches to examine the extent to which players are supported in learning about the game world and mechanics. We demonstrate the method through presenting a case study of the game Portal involving 14 participants, who differed in terms of their gaming expertise. By comparing optimum solutions to puzzles against observed player performance, we illustrate how the method can indicate particular problems with how learning is structured within a game. We argue that the method can highlight where major breakdowns occur and yield design insights that can improve the player experience with puzzle games

Assessing technical candidates on the social web

This is the pre-print version of this Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEThe Social Web provides comprehensive and publicly available information about software developers: they can be identified as contributors to open source projects, as experts at maintaining weak ties on social network sites, or as active participants to knowledge sharing sites. These signals, when aggregated and summarized, could be used to define individual profiles of potential candidates: job seekers, even if lacking a formal degree or changing their career path, could be qualitatively evaluated by potential employers through their online contributions. At the same time, developers are aware of the Web’s public nature and the possible uses of published information when they determine what to share with the world. Some might even try to manipulate public signals of technical qualifications, soft skills, and reputation in their favor. Assessing candidates on the Web for technical positions presents challenges to recruiters and traditional selection procedures; the most serious being the interpretation of the provided signals. Through an in-depth discussion, we propose guidelines for software engineers and recruiters to help them interpret the value and trouble with the signals and metrics they use to assess a candidate’s characteristics and skills