Josh D. Tenenberg

The anatomy of prototypes: Prototypes as filters, prototypes as manifestations of design ideas

The role of prototypes is well established in the field of HCI and Design. A lack of knowledge, however, about the fundamental nature of prototypes still exists. Researchers have attempted to identify different types of prototypes, such as low- vs. high-fidelity prototypes, but these attempts have centered on evaluation rather than support of design exploration. There have also been efforts to provide new ways of thinking about the activity of using prototypes, such as experience prototyping and paper prototyping, but these efforts do not provide a discourse for understanding fundamental characteristics of prototypes. In this article, we propose an anatomy of prototypes as a framework for prototype conceptualization. We view prototypes not only in their role in evaluation but also in their generative role in enabling designers to reflect on their design activities in exploring a design space. We base this framework on the findings of two case studies that reveal two key dimensions: prototypes as filters and prototypes as manifestations. We explain why these two dimensions are important and how this conceptual framework can benefit our field by establishing more solid and systematic knowledge about prototypes and prototyping.

Learning Multiple Goal Behavior via Task Decomposition and Dynamic Policy Merging

An ability to coordinate the pursuit of multiple, time-varying goals is important to an intelligent robot. In this chapter we consider the application of reinforcement learning to a simple class ofdynamicmulti-goal tasks.Not surprisingly, we find that the most straightforward, monolithic approach scales poorly, since the size of the state space is exponential in the number of goals. As an alternative, we propose a simple modular architecture which distributes the learning and control task amongst a set of separate control modules, one for each goal that the agent might encounter. Learning is facilitated since each module learns the optimal policy associated with its goal without regard for other current goals. This greatly simplifies the state representation and speeds learning time compared to a single monolithic controller. When the robot is faced with a single goal, the module associated with that goal is used to determine the overall control policy. When the robot is faced with multiple goals, information from each associated module is merged to determine the policy for the combined task. In general, these merged strategies yield good but suboptimal performance. Thus, the architecture trades poor initial performance, slow learning, and an optimal asymptotic policy in favor of good initial performance, fast learning, and a slightly sub-optimal asymptotic policy. We consider several merging strategies, from simple ones that compare and combine modular information about the current state only, to more sophisticated strategies that use lookahead search to construct more accurate utility estimates.

A blind person's interactions with technology

Meaning can be as important as usability in the design of technology.

Making it Real

Some have proposed that realistic problem situations are better for learning. This issue contains two articles that examine the effects of “making it real” in computer architecture and human-computer interaction.

A non-reified temporal logic

A temporal logic is presented for reasoning about propositions whose truth values might change as a function of time. The temporal propositions consist of formulae in a sorted first-order logic, with each atomic predicate taking some set of temporal arguments as well as a set of nontemporal arguments. The temporal arguments serve to specify the predicate’s dependence on time. By partitioning the terms of the language into two sorts, temporal and non-temporal, time is given a special syntactic and semantic status without having to resort to reification. The benefits of this logic are that it has a clear semantics and a well studied prooftheory. Unlike the first-order logic presented by Shoham, propositions can be expressed and interpreted with respect to any number of temporal arguments, not just with respect to a pair of time points (an interval). We demonstrate the advantages of this flexibility. In addition, nothing is lost by this added flexibility and more standard and useable syntax. To prove this assertion we show that the logic completely subsumes Shoham’s temporal logic [1]. ∗This work was supported by a grant from the Faculty of Mathematics, University of Waterloo, and by NSERC grant OPG0041848. †This work was supported in part by the Air Force Systems Command, Rome Air Development Center, Griffiss Air Force Base, New York 13441–5700, and the Air Force Office of Scientific Research, Bolling AFB, DC 20332 under Contract Number F30602–85–C–0008 which supports the Northeast Artificial Intelligence Consortium (NAIC). ‡This work was supported by NSF research grant DCR–8351665.

Out of our minds: a review of sociocultural cognition theory

Theories of mind are implicitly embedded in educational research. The predominant theory of mind during the latter half of the twentieth century has focused primarily on the individual mind in isolation, context-free problem-solving and mental representations and reasoning, what we refer to as cognitivism. Over the last two decades, CS Education researchers have begun to incorporate recent research that extends, elaborates and sometimes challenges cognitivism. These theories, which we refer to collectively as sociocultural cognition theory, view minds as cultural products, biologically evolved to be extended by tools, social interaction and embodied interaction in the world. Learning, under this perspective, is viewed as tool-mediated participation in the ongoing practices of cultural communities. In this paper, we pursue three goals. First, we provide a summary of the key principles in sociocultural cognition theory, placing this theory within a historical context with respect to the cognitive theories that it extends and challenges. Second, we integrate across different but related research efforts that all fall under the sociocultural cognition umbrella, using a uniform terminology for describing ideas represented within different discourse communities. And third, we reference a number of canonical sources in sociocultural cognition theory so as to serve as an index into this diverse literature for those wanting to explore further.

ON THE IMPLEMENTATION AND EVALUATION OF AbTweak

In this paper, we describe the implementation and evaluation of the AbTweak planning system, a test bed for studying and teaching concepts in partial‐order planning, abstraction, and search control. We start by extending the hierarchical, precondition‐elimination abstraction of ABSTRIPS to partial‐order‐based, least‐commitment planners such as Tweak. The resulting system, AbTweak, illustrates the advantages of using abstraction to improve the efficiency of search. We show that by protecting a subset of abstract conditions achieved so far, and by imposing a bias on search toward deeper levels in a hierarchy, planning efficiency can be greatly improved. Finally, we relate AbTweak to other planning systems SNLP, ALPINE, and SIPE by exploring their similarities and differences.

Warren's question

In this paper, we present an extended examination of a specific, single, instance of transfer of teaching practice. The investigation uses a combination of interpretative analytic techniques from critical literary studies, and grounded theory. From this analysis we make conjectures about some of the ways in which educators change their teaching practice and suggest that these natural practices hold a challenge both for computing education research and educational development.

From I-Awareness to We-Awareness in CSCW

Awareness is one of the central concepts in Computer Supported Cooperative Work, though it has often been used in several different senses. Recently, researchers have begun to provide a clearer conceptualization of awareness that provides concrete guidance for the structuring of empirical studies of awareness and the development of tools to support awareness. Such conceptions, however, do not take into account newer understandings of shared intentionality among cooperating actors that recently have been defined by philosophers and empirically investigated by psychologists and psycho-linguists. These newer conceptions highlight the common ground and socially recursive inference that underwrites cooperative behavior. And it is this inference that is often seamlessly carried out in collocated work, so easy to take for granted and hence overlook, that will require computer support if such work is to be partially automated or carried out at a distance. Ignoring the inferences required in achieving common ground may thus focus a researcher or designer on surface forms of “heeding” that miss the underlying processes of intention shared in and through activity that are critical for cooperation to succeed. Shared intentionality thus provides a basis for reconceptualizing awareness in CSCW research, building on and augmenting existing notions. In this paper, we provide a philosophically grounded conception of awareness based on shared intentionality, demonstrate how it accounts for behavior in an empirical study of two individuals in collocated, tightly-coupled work, and provide implications of this conception for the design of computational systems to support tightly-coupled collaborative work.

Knowing what I know: An investigation of undergraduate knowledge and self-knowledge of data structures

This paper describes an empirical study that investigated the knowledge that Computer Science students have about the extent of their own previous learning. The study compared self-generated estimates of performance with actual performance on a data structures quiz taken by undergraduate students in courses requiring data structures as a prerequisite. The study was contextualized and grounded within a research paradigm in Psychology called calibration of knowledge that suggests that self-knowledge across a range of disciplines is highly unreliable. Such self-knowledge is important because of its role in meta-cognition, particularly in cognitive self-regulation and monitoring, as well as in the credence that instructors give to student self-reports. Our results indicated that Computer Science student self-estimates are highly correlated with performance, more so for estimates provided after the performance than before. This high level of calibration, however, was likely the result of a number of conditions that do not always hold: that the students already had domain expertise, that the quiz had unambiguous and verifiable answers, and that students expected their estimates to be validated. When these conditions are not met, it becomes more important for students to have direct feedback about their performance so as to uncover those areas where their intuitions might mislead them. Students also had weak knowledge about their standing relative to their peers, particularly those in the lower performance quartiles, exhibiting the well known better-than-average heuristic. There was, additionally, no correlation between calibration ability and degree of liking or difficulty with the data structures material, suggesting that instructors and researchers should not treat liking or difficulty as reliable indicators of the learning that has occurred.