Learning in the Liminal Space: A Semiotic Approach to Threshold Concepts

The threshold concepts approach to student learning and curriculum design now informs an empirical research base comprising over 170 disciplinary and professional contexts. It draws extensively on the notion of troublesomeness in a ‘liminal’ space of learning. The latter is a transformative state in the process of learning in which there is a reformulation of the learner’s meaning frame and an accompanying shift in the learner’s ontology or subjectivity. Within the extensive literature on threshold concepts, however, the notion of liminal space has remained relatively ill-defined. This paper explores this spatial metaphor to help clarify the difficulties that some teachers observe in the classroom in regard to their students’ understanding. It employs a novel and distinctive approach drawn from semiotic theory to to provide some explanatory insight into learning within the liminal space and render it more open to analysis. The paper develops its argument through four distinct phases. Firstly it explores the spatial metaphor of liminality to gain further purchase on the nature of this transformative space. The second section introduces semiotic theory and indicates how this will be used through a series of graphical and visual devices to render the liminal space more open to analysis. The third section then employs semiotic analysis to nine dimensions of pedagogical content knowledge to gain further insight into what may characterise student conceptual difficulty within the liminal state. The fourth and concluding section emphasises the role of context in conceptual discrimination before advocating a transactional curriculum inquiry approach to future research in this field

Identifying Factors that Impact the Educational Success of Veterans at IUPUI

poster abstractIncreased post-secondary enrollment among US military veterans using benefits from the Post-9/11 Veterans Educational Assistance Act of 2008 has led to a newfound emphasis on expanded student services on college campuses. We examined academic performance, social support, and mental health through a cross-sectional survey of 101 Veterans who were enrolled in at least one course at IUPUI in the last 12 months in order to identify barriers and facilitators to academic success. In addition to educational outcomes, we also assessed a variety of measures related to community reintegration, quality of life, and resilience. We conceptualized academic success as higher GPA, student status, and lower levels of reported difficulty in reintegration, concentrating in the classroom, and completing coursework. We hypothesized that use of student services and financial aid, involvement with student affairs, perceived social support, encountered barriers, and completion of transition assistance programs were expected to influence success variables. More than half of participants reported experiencing educational barriers unique to their Veteran status, including moderate difficulties with concentration and completing tasks for school. Although high mean scores of grit, resilience, and perceived social support were recorded, high scores of reintegration difficulty suggest that the sample may be at probable risk for post-traumatic stress disorder and substance abuse. Despite these difficulties, most student Veterans did not report using Veteran specific resources, with only one in ten participants reporting any use of campus-based adaptive education services and psychological services. No significant difference was found between groups utilizing or failing to utilize 6 separate sources of financial aid, 6 services, and 9 types of student affairs. Our findings suggest potential ways to enhance current support programs. Future research that tracks longitudinal change and explores student experience in-depth may help explore mechanisms related to the academic success of military Veterans

Mixed methods study examining work reintegration experiences from perspectives of Veterans with mental health disorders

Recent findings have demonstrated that reintegration for Veterans is often challenging. One difficult aspect of reintegration—transitioning into the civilian workplace—has not been fully explored in the literature. To address this gap and examine work reintegration, this mixed methods study examined the perspectives of Veterans with mental health disorders receiving Department of Veterans Affairs healthcare. Forty Veterans rated factors that affect work success; participants also provided narratives on their most and least successful work experiences. We used t-tests and qualitative analysis to compare participants who did and did not serve in combat. Several themes relevant to work reintegration emerged in the narratives, particularly for Veterans who served in combat. An array of work difficulties were reported in the months following military discharge. In addition, Veterans who served in combat reported significantly more work barriers than Veterans who did not serve in combat, particularly health-related barriers. In conclusion, Veterans with mental health disorders who served in combat experienced more work reintegration difficulty than their counterparts who did not serve in combat. The role of being a Veteran affected how combat Veterans formed their self-concept, which also shaped their work success and community reintegration, especially during the early transition period

Evolution of IgE responses to multiple allergen components throughout childhood

BACKGROUND: There is a paucity of information about longitudinal patterns of IgE responses to allergenic proteins (components) from multiple sources. OBJECTIVE: To investigate temporal patterns of component-specific IgE responses from infancy to adolescence, and their relationship with allergic diseases. METHODS: In a population-based birth cohort, we measured IgE to 112 components at 6 follow-ups during childhood. We used a Bayesian method to discover cross-sectional sensitization patterns and their longitudinal trajectories, and related these patterns to asthma and rhinitis in adolescence. RESULTS: We identified one sensitization cluster at age one, 3 at age three, 4 at ages five and eight, 5 at age 11, and six at age 16 years. "Broad" cluster was the only cluster present at every follow-up, comprising of components from multiple sources. "Dust mite" cluster formed at age three and remained unchanged to adolescence. At age three, a single-component "Grass" cluster emerged, which at age five absorbed additional grass components and Fel d 1 to form the "Grass/cat" cluster. Two new clusters formed at age 11: "Cat" cluster and "PR-10/profilin" (which divided at age 16 into "PR-10" and "Profilin"). The strongest contemporaneous associate of asthma at age 16 years was sensitization to "Dust mite" cluster (OR [95% CI]: 2.6 [1.2-6.1], P<0.05), but the strongest early-life predictor of subsequent asthma was sensitization to "Grass/cat" cluster (3.5 [1.6-7.4], P<0.01). CONCLUSIONS: We describe the architecture of the evolution of IgE responses to multiple allergen components throughout childhood, which may facilitate development of better diagnostic and prognostic biomarkers for allergic diseases

Improved Reconstruction of In Silico Gene Regulatory Networks by Integrating Knockout and Perturbation Data

We performed computational reconstruction of the in silico gene regulatory networks in the DREAM3 Challenges. Our task was to learn the networks from two types of data, namely gene expression profiles in deletion strains (the ‘deletion data’) and time series trajectories of gene expression after some initial perturbation (the ‘perturbation data’). In the course of developing the prediction method, we observed that the two types of data contained different and complementary information about the underlying network. In particular, deletion data allow for the detection of direct regulatory activities with strong responses upon the deletion of the regulator while perturbation data provide richer information for the identification of weaker and more complex types of regulation. We applied different techniques to learn the regulation from the two types of data. For deletion data, we learned a noise model to distinguish real signals from random fluctuations using an iterative method. For perturbation data, we used differential equations to model the change of expression levels of a gene along the trajectories due to the regulation of other genes. We tried different models, and combined their predictions. The final predictions were obtained by merging the results from the two types of data. A comparison with the actual regulatory networks suggests that our approach is effective for networks with a range of different sizes. The success of the approach demonstrates the importance of integrating heterogeneous data in network reconstruction

Subjective Experiences of the Benefits and Key Elements of a Cognitive Behavioral Intervention Focused on Community Work Outcomes in Persons With Mental Illness

New research suggests that group-based cognitive behavioral therapy (CBT) may help improve employment outcomes in persons with mental illness, yet the effects and potential key elements facilitating change in such interventions are unclear. Using a mixed methods approach, this study examined the perspectives of persons with mental illness after participating in a pilot study of the “CBT for Work Success” intervention. Findings demonstrate that participants valued the intervention and perceived that it assisted them in achieving work goals. Therapeutic effects included improved self-efficacy, work motivation, enhanced sense of self as workers, and increased beliefs that work success is attainable. CBT for Work Success elements perceived to be important in facilitating work goals included cognitive restructuring, behavioral coping strategies, problem solving work barriers, meaningful reflection on oneself as a worker, and important factors associated with the group process. The authors discuss the implications of these findings and future research directions

Annealing schedule from population dynamics

We introduce a dynamical annealing schedule for population-based optimization algorithms with mutation. On the basis of a statistical mechanics formulation of the population dynamics, the mutation rate adapts to a value maximizing expected rewards at each time step. Thereby, the mutation rate is eliminated as a free parameter from the algorithm.Comment: 6 pages RevTeX, 4 figures PostScript; to be published in Phys. Rev.

Noise, regularizers, and unrealizable scenarios in online learning from restricted training sets

We study the dynamics of on-line learning in multilayer neural networks where training examples are sampled with repetition and where the number of examples scales with the number of network weights. The analysis is carried out using the dynamical replica method aimed at obtaining a closed set of coupled equations for a set of macroscopic variables from which both training and generalization errors can be calculated. We focus on scenarios whereby training examples are corrupted by additive Gaussian output noise and regularizers are introduced to improve the network performance. The dependence of the dynamics on the noise level, with and without regularizers, is examined, as well as that of the asymptotic values obtained for both training and generalization errors. We also demonstrate the ability of the method to approximate the learning dynamics in structurally unrealizable scenarios. The theoretical results show good agreement with those obtained by computer simulations