Non-restricted Access to Model Solutions : A Good Idea?

In this article, we report an experiment where students in an introductory programming course were given the opportunity to view model solutions to programming assignments whenever they wished, without the need to complete the assignments beforehand or to wait for the deadline to pass. Our experiment was motivated by the observation that some students may spend hours stuck with an assignment, leading to non-productive study time. At the same time, we considered the possibility of students using the sample solutions as worked examples, which could help students to improve the design of their own programs. Our experiment suggests that many of the students use the model solutions sensibly, indicating that they can control their own work. At the same time, a minority of students used the model solutions as a way to proceed in the course, leading to poor exam performance.Peer reviewe

Workshop: Cognitive Architectures for Social Human-Robot Interaction

Social HRI requires robots able to use appropriate, adaptive and contingent behaviours to form and maintain en- gaging social interactions with people. Cognitive Architectures emphasise a generality of mechanism and application, making them an ideal basis for such technical developments. Following the successful first workshop on Cognitive Architectures for HRI at the 2014 HRI conference, this second edition of the workshop focusses specifically on applications to social interaction. The full-day workshop is centred on participant contributions, and structured around a set of questions to provide a common basis of comparison between different assumptions, approaches, mechanisms, and architectures. These contributions will be used to support extensive and structured discussions, with the aim of facilitating the development and application of cognitive architectures to social HRI systems. By attending, we envisage that participants will gain insight into how the consideration of cognitive architectures complements the development of au- tonomous social robots

Studying Examples and Solving Problems: Contributions to Skill Acquisition

There is little doubt that examples play a major role in acquiring a new skill. How examples improve learning, however, is subject to some debate. Recently, two different classes of theories have been proposed to explain why examples are such an effective manner of learning. Example Generalization models suggest that problem solving rules are acquired while studying examples. Knowledge Compilation models, on the other hand, suggest that examples are useful because they guide future problem solving, where the necessary rules are created. General support for the knowledge compilation model was found and tradeoffs between studying examples and solving problems are discussed. Guidelines for when to study examples and when to solve problems are also presented. Introduction Typical instruction in problem solving domains includes expository text, annotated examples, and problems to solve. Text expositions usually consist of history, terminology, and descriptions of procedures for solving p..

The Contributions of Studying Examples and Solving Problems to Skill Acquisition

: There is little doubt that examples play a major role in acquiring a new skill. How examples improve learning, however, is subject to some debate. Recently, two different classes of theories have been proposed to explain why examples are such an effective manner of learning. Example Generalization models suggest that problem solving rules are acquired while studying examples. Knowledge Compilation models, on the other hand, suggest that examples are useful because they guide future problem solving, where the necessary rules are created. Consistent with knowledge compilation models, we found that separating target problems from source examples hindered learning because the source examples could not be remembered to guide problem solving. We also found that if sources are not accessible or remembered during problem solving, learning occurs best when the sources are problems to be solved, rather than examples. Taken together, these results provide strong support for the knowledge comp..

Interactive spatiotemporal cognition:Data, theories, architectures, and autonomy

Everyday interactions often depend on thinking about spaceand time: collaborators need to know where events takeplace – and in what order – to, e.g., communicate drivingdirections, build pieces of furniture, or carry out strategicoperations in military and sports settings (Núñez &Cooperrider, 2013). A simple set of driving directions mayrequire a listener to interpret and reason about the spatialrelations – such as next to and behind – and the temporalrelations – such as after and during – that a speakerdescribes. The speaker may also use gestures to substitute,supplement, or disambiguate linguistic descriptions (Holle& Gunter, 2007; Perzanowski, Schultz, & Williams, 1998).Such rapid, rich, and productive interactions are transientand difficult to analyze behaviorally, and so they pose achallenge for experimenters. They are grounded in thephysical world, and accordingly challenge computationalmodels that cannot digest rich perceptual and environmentalinput in real time. Robotic systems are geared towardsprocessing and acting upon the physical world – and theyincreasingly support human-robotic interaction (e.g., Fonget al., 2006; Kawamura et al., 2003; Kortenkamp et al.,1999). But they, too, are uniquely challenged in maintainingproductive interactive exchanges with human teammates,because they must be tolerant of human idiosyncrasies,preferences, limitations, and errors (Trafton et al., 2013).Because these challenges cut across broad interests incognitive science – such as linguistics, artificial intelligence,robotics, and psychology – progress is unlikely without theengagement of multiple approaches, from psychologicalexperimentation to the construction of autonomous,embodied systems. In recent years, progress towardsunderstanding interactive spatiotemporal cognition hasaccelerated along parallel paths: there exist new behavioraland imaging methodologies to study event segmentation(e.g., Radvansky & Zacks, 2014), spatial inference (e.g.,Knauff & Ragni, 2013), and gestural cognition (e.g.,Novack et al., 2016); novel computational theories ofunderstanding physical reasoning (e.g., Battaglia et al.,2013) and mental simulation (e.g., Khemlani & Johnson-Laird, 2013); cognitive architectures that support richinteractivity (Huffman & Laird, 2014; Trafton et al., 2013);and a wide variety of technological platforms on which totransform theory into embodied interaction.The goal of the workshop is to allow these parallelapproaches to converge. Discussants will share recent dataand theory, consider novel architectural approaches, anddemonstrate burgeoning technological advances thatadvance the science of spatiotemporal inference. Theworkshop will promote interdisciplinary collaboration byfocusing on three unifying theme

Memory for Goals: An Architectural Perspective

The notion that memory for goals is organized as a stack is central in cognitive theory in that stacks are core constructs leading cognitive architectures. However, the stack over-predicts the strength of goal memory and the precision of goal selection order, while under-predicting the maintenance cost of both. A better way to study memory for goals is to treat them like any other kind of memory element. This approach makes accurate and wellconstrained predictions and reveals the nature of goal encoding and retrieval processes. The approach is demonstrated in an ACT-R model of human performance on a canonical goal-based task, the Tower of Hanoi. The model and other considerations suggest that cognitive architectures should enforce a two-element limit on the depth of the stack to deter its use for storing task goals while preserving its use for attention and learning