Why Stumble Into Academic Success? Use Pedagogy That Is Intentional

Too often students \u27stumble\u27 into academic success. Instead of tailoring academic activities to promote student success, we often create activities that deliver content with the expectation of student academic success. Why not design activities that are intentional in their design to promote academic success. This workshop will share information from my research that views student academic success from a student persistence and constructivist lens. Participants will discuss how academic pedagogy and student experiences can engender student academic success, particularly in STEM majors. Participants will walk away from this session with information that will support the development of classroom pedagogy to enhance the student’s learning experiences and assist with persistence in the sciences

The Cognitive Atlas: Employing Interaction Design Processes to Facilitate Collaborative Ontology Creation

The Cognitive Atlas is a collaborative knowledge-building project that aims to develop an ontology that characterizes the current conceptual framework among researchers in cognitive science and neuroscience. The project objectives from the beginning focused on usability, simplicity, and utility for end users. Support for Semantic Web technologies was also a priority in order to support interoperability with other neuroscience projects and knowledge bases. Current off-the-shelf semantic web or semantic wiki technologies, however, do not often lend themselves to simple user interaction designs for non-technical researchers and practitioners; the abstract nature and complexity of these systems acts as point of friction for user interaction, inhibiting usability and utility. Instead, we take an alternate interaction design approach driven by user centered design processes rather than a base set of semantic technologies. This paper reviews the initial two rounds of design and development of the Cognitive Atlas system, including interactive design decisions and their implementation as guided by current industry practices for the development of complex interactive systems

Teachers\u27 Perceptions of Differentiated Learning for At-Risk Second-Grade Students in Reading

Students were performing below grade level in reading, fluency, and comprehension in a suburban school in South Carolina. The purpose of this study was to explore the perceptions of teachers about their preferred differentiated instruction approach (face-to-face vs. computer-based) to meet the needs of at-risk students in 2nd grade. The underlying theoretical framework was drawn from constructivist theory, observation theory, and social development theory. The study questions were on teachers\u27 perceptions of the best form of differentiation, improved reading based on peer socialization in face-to-face instruction, and benefits and limitations of a computer-based approach. The study was a single case study design, with qualitative data from 10 participants; the tools included an attitudinal questionnaire, focus groups, and interviews. Data sources were triangulated and analyzed for emergent themes. The results showed that teachers perceived differentiation as a positive approach to meeting students\u27 needs with a preference for a face-to-face approach because it provided direct contact with the student, but computer-based approach had an advantage in compiling data. The teachers faced challenges using face-to-face instruction, including time management, planning, administrative support, and lack of professional development opportunities. The challenges led to a recommendation for professional development. This study supports positive social change in that educators may apply the results to their efforts to develop student skills in reading, fluency, and comprehension, thus increasing students\u27 opportunities for success and productivity in society

Poster 216: Utility of Ultrasound in Evaluation of a Spontaneous Plantar Fascia Rupture: A Case Report

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147055/1/pmr2s98.pd

Executive Function in Pediatric Bipolar Disorder and Attention-Deficit Hyperactivity Disorder: In Search of Distinct Phenotypic Profiles

Often, there is diagnostic confusion between bipolar disorder (BD) and attention-deficit hyperactivity disorder (ADHD) in youth due to similar behavioral presentations. Both disorders have been implicated as having abnormal functioning in the prefrontal cortex; however, there may be subtle differences in the manner in which the prefrontal cortex functions in each disorder that could assist in their differentiation. Executive function is a construct thought to be a behavioral analogy to prefrontal cortex functioning. We provide a qualitative review of the literature on performance on executive function tasks for BD and ADHD in order to determine differences in task performance and neurocognitive profile. Our review found primary differences in executive function in the areas of interference control, working memory, planning, cognitive flexibility, and fluency. These differences may begin to establish a pediatric BD profile that provides a more objective means of differential diagnosis between BD and ADHD when they are not reliably distinguished by clinical diagnostic methods

Literature Mapping with PubAtlas — extending PubMed with a ‘BLASTing interface’ *

PubAtlas (www.pubatlas.org) is a web service and standalone program providing literature maps for the biomedical research literature. It accepts user-defined sets of terms (PubMed queries) as input, and permits ‘BLASTing’ of one set against another: for all terms x and y in these sets, deriving the results of the pairwise intersections x AND y. This all vs. all capability extends PubMed with a literature analysis interface. Correspondingly, the basic form of literature map that PubAtlas provides for exploring associations among sets of terms is an interactive tabular display, in heatmap/microarray format

Decoding Continuous Variables from Neuroimaging Data: Basic and Clinical Applications

The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods