Size as Diversity Absent from Multicultural Textbooks

Abstract

The current clinical approach adopted by many mental health clinicians is the medical model, which, in the case of size diversity, has been connected with weight stigma. Mental health practitioners need to be exposed to the critique of the medicalized view that fat is a disease that can be cured through dieting. Weight stigma and fat-shaming may be reduced through adoption of a social justice perspective on sizeism as a form of oppression. An analysis of textbooks designed for use in graduate multicultural courses indicates that size is not currently addressed as a diversity or social justice issue within psychology. It is recommended that fat be conceptualized as a social identity that intersects with other diversity dimensions within coursework for mental health trainees as opposed to a sizeist paradigm where fat is depicted as pathological.     

Response interference under near-concurrent presentation of safety and non-safety information

The growing advent of Connected Vehicle Systems (CVS) is changing the information environment within the vehicle cab. As vehicles add the capability to receive data from the road infrastructure or share data with other vehicles, there is an expanding amount of safety and non-safety information from these systems with which the driver must contend. The information processing demands for the driver may become more complex, especially under conditions that present multiple information signals to the driver at the same time. To manage this complexity, the design of CVS will need to incorporate information management functions to prioritize information so as to be compatible with the processing capacity of the driver. The objective of this research was to examine the potential interference effect of non-safety critical information on driver responses to near concurrent, critical safety warnings. The study design was based on theory and evidence that there is a “bottle neck” in a human’s central processing of information, such that the processing of early signals (S1) in the environment may delay the response to a later signal (S2) until such time as the response to the first stimulus has been initiated. The results of the study suggest that there is the possibility that near concurrent presentation of safety and non-safety critical information may generate interference effects, but the combination of signal parameters (modality, timing) that are most likely to create this interference may be infrequent in most real world conditions. Future research should focus on the specific parameters that increase the probability of such interference. Such research would then provide a better estimate of the potential frequency of this interference and also guide the formulation of design guidelines to minimize this potential.     

MetrIntMeas a novel metric for measuring the intelligence of a swarm of cooperating agents

We propose a novel metric called MetrIntMeas (Metric for the Intelligence Measuring) for an accurate and robust measurement of the difficult problem-solving intelligence of a swarm system. The metric allows the classification if a swarm system belongs to the same class with the systems which have a specific reference intelligence value. For proving the efficiency of the proposed metric we realized a case study on a swarm system specialized in solving a NP-hard problem. As an application of the proposed metric, we present the measurement of the swarm systems’ evolution in intelligence. We gave a new definition to the intelligent evolving systems. The evolution of intelligent systems can be verified using the proposed MetrIntMeas metric.     

Designing and evaluating an intelligent augmented reality system for assisting older adults’ medication management

Intelligent Assistive Systems (IASs) provide personalised support to individuals to increase their autonomy and competence in performing activities. The personalisation of such technology represents a particular challenge, for the individuals abilities typically change over time. The aim of this research was to explore a theory-based, activity-centred framework for the development of IASs that both facilitate the active involvement of caregivers and target users and allow for adaptation to the individual over time.Medication management was focused on as a case study. Three older adults and two caregivers were involved in a co-design process of an IAS prototype system, the MED-AR, which uses projection-based augmented reality as the user interface technology. The theoretical framework for the design and evaluation of the MED-AR was activity theory. For the decision-making process of the MED-AR, formal argumentation theory was used. The major contributions of the study are (i) a formal framework for understanding the level of independence in activities and the way an IAS can tailor support to an individual and a situation, including caregivers; and (ii) a model for involving older adults and caregivers in a co-design process in the first phases of developing IASs.     

Highlights as an Early Predictor of Student Comprehension and Interests

When engaging with a textbook, students are inclined to highlight key content. Although students believe that highlighting and subsequent review of the highlights will further their educational goals, the psychological literature provides little evidence of benefits. Nonetheless, a student’s choice of text for highlighting may serve as a window into her mental state—her level of comprehension, grasp of the key ideas, reading goals, and so on. We explore this hypothesis via an experiment in which 400 participants read three sections from a college-level biology text, briefly reviewed the text, and then took a quiz on the material. During initial reading, participants were able to highlight words, phrases, and sentences, and these highlights were displayed along with the complete text during the subsequent review. Consistent with past research, the amount of highlighted material is unrelated to quiz performance. Nonetheless, highlighting patterns may allow us to infer reader comprehension and interests. Using multiple representations of the highlighting patterns, we built probabilistic models to predict quiz performance and matrix factorization models to predict what content would be highlighted in one passage from highlights in other passages. We find that quiz score prediction accuracy reliably improves with the inclusion of highlighting data (by about 1%–2%), both for held-out students and for held-out student questions (i.e., questions selected randomly for each student), but not for held-out questions. Furthermore, an individual’s highlighting pattern is informative of what she highlights elsewhere. Our long-term goal is to design digital textbooks that serve not only as conduits of information into the reader’s mind but also allow us to draw inferences about the reader at a point where interventions may increase the effectiveness of the material.     

Spanning seven orders of magnitude: a challenge for cognitive modeling

Much of cognitive psychology focuses on effects measured in tens of milliseconds while significant educational outcomes take tens of hours to achieve. The task of bridging this gap is analyzed in terms of Newell's (1990) bands of cognition—the Biological, Cognitive, Rational, and Social Bands. The 10 millisecond effects reside in his Biological Band while the significant learning outcomes reside in his Social Band. The paper assesses three theses: The Decomposition Thesis claims that learning occurring at the Social Band can be reduced to learning occurring at lower bands. The Relevance Thesis claims that instructional outcomes at the Social Band can be improved by paying attention to cognition at the lower bands. The Modeling Thesis claims that cognitive modeling provides a basis for bridging between events on the small scale and desired outcomes on the large scale. The unit‐task level, at the boundary of the Cognitive and Rational Bands, is useful for assessing these theses. There is good evidence for all three theses in efforts that bridge from the unit‐task level to educational applications. While there is evidence for the Decomposition Thesis all the way down to the 10 millisecond level, more work needs to be done to establish the Relevance Thesis and particularly the Modeling Thesis at the lower levels.     

An effective metacognitive strategy: learning by doing and explaining with a computer‐based Cognitive Tutor

Recent studies have shown that self‐explanation is an effective metacognitive strategy, but how can it be leveraged to improve students' learning in actual classrooms? How do instructional treatments that emphasizes self‐explanation affect students' learning, as compared to other instructional treatments? We investigated whether self‐explanation can be scaffolded effectively in a classroom environment using a Cognitive Tutor, which is intelligent instructional software that supports guided learning by doing. In two classroom experiments, we found that students who explained their steps during problem‐solving practice with a Cognitive Tutor learned with greater understanding compared to students who did not explain steps. The explainers better explained their solutions steps and were more successful on transfer problems. We interpret these results as follows: By engaging in explanation, students acquired better‐integrated visual and verbal declarative knowledge and acquired less shallow procedural knowledge. The research demonstrates that the benefits of self‐explanation can be achieved in a relatively simple computer‐based approach that scales well for classroom use.