A School-Based Intervention for Third Grade Students Experiencing Test Anxiety

With the advent of the No Child Left Behind Act (NCLB, 2001), students are regularly faced with high stakes tests and classroom-based assessments to determine if they are meeting grade level educational standards. Estimates suggest that up to 40% of children may experience significant anxiety surrounding evaluations (e.g., McDonald, 2001; Turner, Beidel, Hughes, & Turner, 1993) and research shows that this test anxiety can negatively impact school performance (e.g., Abu-Rabia, 2004; Putwain, 2008) and mental health (e.g., Barksdale-Ladd & Thomas, Weems et al., 2010). As a result, test anxiety has become a topic of concern for researchers, educators, and mental health practitioners. The construct of test anxiety can be defined as the experience of marked psychological distress when faced with evaluative situations (McDonald, 2001). While researchers have discussed effective methods used to reduce test anxiety symptoms, much of this literature has focused on intervention within clinic settings rather than within the school environment (Gregor, 2005). Research in this area also tends to concentrate on older children and adults instead of elementary-aged students (Gregor, 2005; Weems et al., 2010). To address these gaps within the intervention literature, the purpose of the current pilot study was to develop, implement, and evaluate a school-based small group intervention designed to reduce test anxiety and increase coping skills in third grade students. The intervention was hypothesized to increase students’ awareness and use of stress management strategies, improve cognitive flexibility and inhibition of automatic anxious thoughts, decrease symptoms of anxiety, and increase confidence in their ability to face evaluative situations. Results of paired-sample t tests indicated that students reported significantly increased knowledge of test anxiety reduction strategies and a greater willingness to implement these strategies. Trend level gains in cognitive flexibility were discovered, though results were not statistically significant. Despite growth in student knowledge and cognitive flexibility, anxiety was not significantly reduced. Quantitative and qualitative findings suggested that the intervention was implemented with integrity and was acceptable to participants and facilitators. Results are discussed and implications for future directions in research and practice are suggested

Research in interactive scene analysis

Cooperative (man-machine) scene analysis techniques were developed whereby humans can provide a computer with guidance when completely automated processing is infeasible. An interactive approach promises significant near-term payoffs in analyzing various types of high volume satellite imagery, as well as vehicle-based imagery used in robot planetary exploration. This report summarizes the work accomplished over the duration of the project and describes in detail three major accomplishments: (1) the interactive design of texture classifiers; (2) a new approach for integrating the segmentation and interpretation phases of scene analysis; and (3) the application of interactive scene analysis techniques to cartography

Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders

Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework. Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training

Research in interactive scene analysis

An interactive scene interpretation system (ISIS) was developed as a tool for constructing and experimenting with man-machine and automatic scene analysis methods tailored for particular image domains. A recently developed region analysis subsystem based on the paradigm of Brice and Fennema is described. Using this subsystem a series of experiments was conducted to determine good criteria for initially partitioning a scene into atomic regions and for merging these regions into a final partition of the scene along object boundaries. Semantic (problem-dependent) knowledge is essential for complete, correct partitions of complex real-world scenes. An interactive approach to semantic scene segmentation was developed and demonstrated on both landscape and indoor scenes. This approach provides a reasonable methodology for segmenting scenes that cannot be processed completely automatically, and is a promising basis for a future automatic system. A program is described that can automatically generate strategies for finding specific objects in a scene based on manually designated pictorial examples

DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs

A major difficulty of solving continuous POMDPs is to infer the multi-modal distribution of the unobserved true states and to make the planning algorithm dependent on the perceived uncertainty. We cast POMDP filtering and planning problems as two closely related Sequential Monte Carlo (SMC) processes, one over the real states and the other over the future optimal trajectories, and combine the merits of these two parts in a new model named the DualSMC network. In particular, we first introduce an adversarial particle filter that leverages the adversarial relationship between its internal components. Based on the filtering results, we then propose a planning algorithm that extends the previous SMC planning approach [Piche et al., 2018] to continuous POMDPs with an uncertainty-dependent policy. Crucially, not only can DualSMC handle complex observations such as image input but also it remains highly interpretable. It is shown to be effective in three continuous POMDP domains: the floor positioning domain, the 3D light-dark navigation domain, and a modified Reacher domain.Comment: IJCAI 202