The Cross Examiner

https://scholarship.shu.edu/law_newspapers/1210/thumbnail.jp

“Our Brokenness Kind of Connects Us”: Exploring Social Justice Topics Through Read-Alouds in a Ninth-Grade Classroom

Doctor of PhilosophyCurriculum and Instruction ProgramsLotta LarsonFor decades, K-12 teachers across the United States have read aloud to their students, whether it be to model fluent reading, to promote vocabulary acquisition, or out of pure enjoyment. As social justice becomes a more prevalent topic in classrooms across the country, interactive read-alouds are being used to introduce and discuss complex and delicate topics, like human rights and social justice. While students at all junctures of development and learning embrace and benefit from reading aloud, existing research primarily takes place in elementary school settings. Furthermore, literature used to explore social justice issues usually involves picture books rather than longer texts like chapter books. This study was designed to gain insight into how a classroom teacher facilitated a nonfiction chapter book read-aloud and how the students responded to the social justice themes represented in the chapter book. The study took place over the span of 18 days in a Midwest ninth-grade classroom. The theoretical underpinnings that framed the study were constructivism, transactional theory of reader response and critical literacy. Data was collected and analyzed using qualitative case study principles. Study results reveal four emerging themes across the research questions, including expressive reading; spontaneity; redemption; empathy; and awareness

Kenyon Collegian - November 20, 2014

https://digital.kenyon.edu/collegian/3366/thumbnail.jp

Direct Animation Interfaces : an Interaction Approach to Computer Animation

Creativity tools for digital media have been largely democratised, offering a range from beginner to expert tools. Yet computer animation, the art of instilling life into believable characters and fantastic worlds, is still a highly sophisticated process restricted to the spheres of expert users. This is largely due to the methods employed: in keyframe animation dynamics are indirectly specified over abstract descriptions, while performance animation suffers from inflexibility due to a high technological overhead. The reverse trend in human-computer interaction to make interfaces more direct, intuitive, and natural to use has so far hardly touched the animation world: decades of interaction research have scarcely been linked to research and development of animation techniques. The hypothesis of this work is that an interaction approach to computer animation can inform the design and development of novel animation techniques. Three goals are formulated to illustrate the validity of this thesis. Computer animation methods and interfaces must be embedded in an interaction context. The insights this brings for designing next generation animation tools must be examined and formalised. The practical consequences for the development of motion creation and editing tools must be demonstrated with prototypes that are more direct, efficient, easy-to-learn, and flexible to use. The foundation of the procedure is a conceptual framework in the form of a comprehensive discussion of the state of the art, a design space of interfaces for time-based visual media, and a taxonomy for mappings between user and medium space-time. Based on this, an interaction-centred analysis of computer animation culminates in the concept of direct animation interfaces and guidelines for their design. These guidelines are tested in two point designs for direct input devices. The design, implementation and test of a surface-based performance animation tool takes a system approach, addressing interaction design issues as well as challenges in extending current software architectures to support novel forms of animation control. The second, a performance timing technique, shows how concepts from video browsing can be applied to motion editing for more direct and efficient animation timing

Neural representations of social and non-social uncertainty in human decision making

The social landscape is filled with an intricate web of species-specific desired objects and course of actions. Humans are highly social animals and, as they navigate this landscape, they need to produce adapted decision-making behaviour. Traditionally social and non-social neural mechanisms affecting choice have been investigated using different approaches. Recently, in an effort to unite these findings, two main theories have been proposed to explain how the brain might encode social and non-social motivational decision-making: the extended common currency and the social valuation specific schema (Ruff & Fehr 2014). One way to test these theories is to directly compare neural activity related to social and non-social decision outcomes within the same experimental setting. Here we address this issue by focusing on the neural substrates of social and non-social forms of uncertainty. Using functional magnetic resonance imaging (fMRI) we directly compared the neural representations of reward and risk prediction and errors (RePE and RiPE) in social and non- social situations using gambling games. We used a trust betting game to vary uncertainty along a social dimension (trustworthiness), and a card game (Preuschoff et al. 2006) to vary uncertainty along a non-social dimension (pure risk). The trust game was designed to maintain the same structure of the card game. In a first study, we exposed a divide between subcortical and cortical regions when comparing the way these regions process social and non-social forms of uncertainty during outcome anticipation. Activity in subcortical regions reflected social and non-social RePE, while activity in cortical regions correlated with social RePE and non-social RiPE. The second study focused on outcome delivery and integrated the concept of RiPE in non-social settings with that of fairness and monetary utility maximisation in social settings. In particular these results corroborate recent models of anterior insula function (Singer et al. 2009; Seth 2013), and expose a possible neural mechanism that weights fairness and uncertainty but not monetary utility. The third study focused on functionally defined regions of the early visual cortex (V1) showing how activity in these areas, traditionally considered only visual, might reflect motivational prediction errors in addition to known perceptual prediction mechanisms (den Ouden et al 2012). On the whole, while our results do not support unilaterally one or the other theory modeling the underlying neural dynamics of social and non-social forms of decision making, they provide a working framework where both general mechanisms might coexist

Online Build-Order Optimization for Real-Time Strategy Agents Using Multi-Objective Evolutionary Algorithms

The investigation introduces a novel approach for online build-order optimization in real-time strategy (RTS) games. The goal of our research is to develop an artificial intelligence (AI) RTS planning agent for military critical decision- making education with the ability to perform at an expert human level, as well as to assess a players critical decision- making ability or skill-level. Build-order optimization is modeled as a multi-objective problem (MOP), and solutions are generated utilizing a multi-objective evolutionary algorithm (MOEA) that provides a set of good build-orders to a RTS planning agent. We de ne three research objectives: (1) Design, implement and validate a capability to determine the skill-level of a RTS player. (2) Design, implement and validate a strategic planning tool that produces near expert level build-orders which are an ordered sequence of actions a player can issue to achieve a goal, and (3) Integrate the strategic planning tool into our existing RTS agent framework and an RTS game engine. The skill-level metric we selected provides an original and needed method of evaluating a RTS players skill-level during game play. This metric is a high-level description of how quickly a player executes a strategy versus known players executing the same strategy. Our strategic planning tool combines a game simulator and an MOEA to produce a set of diverse and good build-orders for an RTS agent. Through the integration of case-base reasoning (CBR), planning goals are derived and expert build- orders are injected into a MOEA population. The MOEA then produces a diverse and approximate Pareto front that is integrated into our AI RTS agent framework. Thus, the planning tool provides an innovative online approach for strategic planning in RTS games. Experimentation via the Spring Engine Balanced Annihilation game reveals that the strategic planner is able to discover build-orders that are better than an expert scripted agent and thus achieve faster strategy execution times

Vista: March 27, 2014

https://digital.sandiego.edu/vista/1710/thumbnail.jp