Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. This Indexical Model incorporates multiple information sources, including perceptions, domain knowledge, and short-term and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of underspecific referring expressions and unexpressed argument alternations of verbs. The model is being used to support linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.Comment: Advances in Cognitive Systems 3 (2014

From profiles to rich tasks : the situated nature of \u27authenticity\u27 in the context of reforming curriculum and assessment practices

Outcome based education that has dominated Australian education in the 1990s is under review in the early years of the twenty first century. The available historical \u27texts\u27 produced during the first half of the 1990s, which include the national Statements and Profiles, and the state Curriculum and Standards Frameworks, provide us with documents that we can engage with not simply for \u27history\u27s sake\u27, but with an opportunity to, in the words of the feminist author Dorothy Smith, \u27displace[s] the analysis from the text as originating in writer or thinker, to the discourse itself as an ongoing intertextual process\u27 bringing into view the social relations in which texts are embedded and which they organise\u27 (1990, p. 161-2). Most Australian states and territories have now commenced significant situated, local curriculum renewal and reform. This renewed interest in curriculum offers insights into the character of recent assessment practices in Australia, recognising the tensions inherent in assessment practices and authentic assessment models. This paper explores, by way of an overview of the broad curriculum and assessment practices adopted in Australia over the past twenty-five years, the situated nature of \u27authenticity\u27 in the context of curriculum and assessment practices and how as teacher educators we are responding through our everyday work. <br /

From conditioning to learning communities: Implications of fifty years of research in e‐learning interaction design

This paper will consider e‐learning in terms of the underlying learning processes and interactions that are stimulated, supported or favoured by new media and the contexts or communities in which it is used. We will review and critique a selection of research and development from the past fifty years that has linked pedagogical and learning theory to the design of innovative e‐learning systems and activities, and discuss their implications. It will include approaches that are, essentially, behaviourist (Skinner and Gagné), cognitivist (Pask, Piaget and Papert), situated (Lave, Wenger and Seely‐Brown), socio‐constructivist (Vygotsky), socio‐cultural (Nardi and Engestrom) and community‐based (Wenger and Preece). Emerging from this review is the argument that effective e‐learning usually requires, or involves, high‐quality educational discourse, that leads to, at the least, improved knowledge, and at the best, conceptual development and improved understanding. To achieve this I argue that we need to adopt a more holistic approach to design that synthesizes features of the included approaches, leading to a framework that emphasizes the relationships between cognitive changes, dialogue processes and the communities, or contexts for e‐learning

The practitioner perspective on the modeling of pedagogy and practice

The promotion of e-learning in policies internationally has led to questions about how best to employ technology in support of learning. A range of models has since been developed that attempts to relate pedagogy to technology. However, research into the effectiveness of such models in changing teaching practice is sparse, and work that compares these models to practitioners’ own representations of their practice is absent. The study described here involved asking practitioners to model their own practice, and to compare these with a model developed by a government organisation. Practitioners were adept at using existing models and repurposing them to suit their own context. Our research provided evidence of broad acceptance of the existing model with practitioners, but indicated that practitioners would take this tool and remodel it for their own contexts of learning to make it meaningful, relevant and useful to them

Cómo los corpus pueden asistir a los estudiantes de traducción jurídica : la plataforma GENTT TransTools Corpora y Sketch Engine

 This paper analyses the application of corpora to the teaching of legal translation in higher education settings combining the use of both the GENTT TransTools Corpora platform and Sketch Engine. A review of previous teaching experiences with legal textual corpora is presented, followed by a descriptive overview of GENTT?s research group 10 years? experience using corpus in the classroom with a translation training approach that promotes scaffolded education as well as constructive and cooperative situated learning. These suggest that classroom activities with monolingual, multilingual and translated corpora of legal documents may prove useful to students of legal translation, improving their strategic competence and providing them with text models and patterns to be used as terminological, textual and legal/conceptual references

Modeling Course-Based Undergraduate Research Experiences: An Agenda for Future Research and Evaluation

Course-based undergraduate research experiences (CUREs) are being championed as scalable ways of involving undergraduates in science research. Studies of CUREs have shown that participating students achieve many of the same outcomes as students who complete research internships. However, CUREs vary widely in their design and implementation, and aspects of CUREs that are necessary and sufficient to achieve desired student outcomes have not been elucidated. To guide future research aimed at understanding the causal mechanisms underlying CURE efficacy, we used a systems approach to generate pathway models representing hypotheses of how CURE outcomes are achieved. We started by reviewing studies of CUREs and research internships to generate a comprehensive set of outcomes of research experiences, determining the level of evidence supporting each outcome. We then used this body of research and drew from learning theory to hypothesize connections between what students do during CUREs and the outcomes that have the best empirical support. We offer these models as hypotheses for the CURE community to test, revise, elaborate, or refute. We also cite instruments that are ready to use in CURE assessment and note gaps for which instruments need to be developed.Howard Hughes Medical InstituteScience and Mathematics Educatio

Self-critical Sequence Training for Image Captioning

Recently it has been shown that policy-gradient methods for reinforcement learning can be utilized to train deep end-to-end systems directly on non-differentiable metrics for the task at hand. In this paper we consider the problem of optimizing image captioning systems using reinforcement learning, and show that by carefully optimizing our systems using the test metrics of the MSCOCO task, significant gains in performance can be realized. Our systems are built using a new optimization approach that we call self-critical sequence training (SCST). SCST is a form of the popular REINFORCE algorithm that, rather than estimating a "baseline" to normalize the rewards and reduce variance, utilizes the output of its own test-time inference algorithm to normalize the rewards it experiences. Using this approach, estimating the reward signal (as actor-critic methods must do) and estimating normalization (as REINFORCE algorithms typically do) is avoided, while at the same time harmonizing the model with respect to its test-time inference procedure. Empirically we find that directly optimizing the CIDEr metric with SCST and greedy decoding at test-time is highly effective. Our results on the MSCOCO evaluation sever establish a new state-of-the-art on the task, improving the best result in terms of CIDEr from 104.9 to 114.7.Comment: CVPR 2017 + additional analysis + fixed baseline results, 16 page

Modeling the perceptual component of conceptual learning—A coordination perspective

Although a picture may be worth a thousand words, modeling diagrams as propositions and modeling visual processing as search through a database of verbal descriptions obscures what is problematic for the learner. Cognitive modeling of language learning and geometry has obscured the learner's problem of knowing where to look—what spaces, markings, and orientations constitute the objects of interest? Today we are launching into widespread use of multimedia instructional technology, without an adequate theory to relate perceptual processes to conceptual learning. Does this matter? In this article, I review the symbolic approach to modeling perceptual processing and show its limitations for explaining difficulties children encounter in interpreting a graphic display. I present an alternative analysis by which perceptual categorization is coupled to behavior sequences, where gesturing and emotional changes are essential for resolving impasses and breaking out of loops. I conclude by asking what kind of cognitive theory we need to exploit communication technology. Have we been correct to assume that pedagogy must be grounded in an accurate psychological model of knowledge, memory, and learning

Building machines that learn and think about morality

Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also discuss how work in embodied and situated cognition could provide a valu- able perspective on future research