Exploring the Use of Cognitive Apprenticeship for Teachers and Students in Science Classrooms

The primary goal of this dissertation is to explore the use of cognitive apprenticeship (CA) with teachers and students in science classrooms. In particular, studies that make up this dissertation explore ways that teachers can improve the quality of students’ written scientific explanations and the supports that teachers need in order to promote such growth in their students. CA is a complex instructional model that is challenging for both teachers and students to use, especially in secondary classrooms. Other reports indicate the potential of CA for teaching disciplinary literacy in history classrooms, but this approach has not often been used to teach scientific writing. This project explores that, in inclusive settings with heterogeneous learners, and in an afterschool program, with students with learning disabilities (LD) and those who are English learners (ELs). The first part of the work reported here involved a systematic review of the literature on science writing instruction with these populations and with struggling learners. A total of 14 studies (three randomized control trials, nine quasi-experimental, and two single case design studies) that met established criteria as high quality studies were identified and examined to determine whether researchers were including instructional elements that have been found to be effective for these learners (e.g., cognitive and linguistic supports) and to determine learning and writing outcomes that resulted from the science writing interventions. The next project focused on an in-depth study of two middle school science teachers who participated in PD that was focused on science writing, culminating in the implementation of a CA on constructing and critiquing explanations for scientific phenomena in writing. The goal in this work was to examine how doing so impacted the teachers’ beliefs and their subsequent choice of writing tasks for their science instruction. After this PD, both teachers expressed changes in their beliefs about learners that had lasting effects on their subsequent teaching. They also believed the CA led to improved writing in their students, including their ability to engage in argumentative reasoning. This realization led to changes in other beliefs about their students in general, and about the importance of incorporating writing instruction in class. Ultimately, these changes may have affected the types of tasks they assigned in class. Prior to implementing CA, they assigned writing tasks that were close-ended, but after, they assigned analytical writing tasks like a Claim, Evidence, and Reasoning (CER) that promoted scientific reasoning. The third project in this dissertation was an intervention study (using single-case design methodology) that focused on teaching middle school students with LD and who are EL to write scientific explanations. The intervention provided cognitive supports such as procedural facilitators to guide students’ thinking. In addition, linguistic supports, such as the use of contextualized instruction on text structure, vocabulary, and grammar, and instruction on how language is used in a science was also provided to meet the needs of the sixth- and seventh-grade participants. After delivering instruction using CA (and four weeks later), students produced explanations that were rated as higher in overall quality, grammatical and lexical sophistication, and in the length of their writing. Of importance, they also made substantial gain in causal and mechanistic reasoning, which is central to good scientific writing. These findings lead us to believe that middle school science teachers who work with students with LD and those who are EL may underestimate their students’ ability to write. Contrary to their beliefs, findings from these projects suggest otherwise. Given sufficient and appropriate support such as those afforded by CA, our findings provide tentative support for the conjecture that all students, regardless of their disability status or language needs may be able to improve their reasoning and writing skills in science. CAs can be a powerful vehicle that can transform both teacher practices and student learning outcome

Towards Explainable AI Writing Assistants for Non-native English Speakers

We highlight the challenges faced by non-native speakers when using AI writing assistants to paraphrase text. Through an interview study with 15 non-native English speakers (NNESs) with varying levels of English proficiency, we observe that they face difficulties in assessing paraphrased texts generated by AI writing assistants, largely due to the lack of explanations accompanying the suggested paraphrases. Furthermore, we examine their strategies to assess AI-generated texts in the absence of such explanations. Drawing on the needs of NNESs identified in our interview, we propose four potential user interfaces to enhance the writing experience of NNESs using AI writing assistants. The proposed designs focus on incorporating explanations to better support NNESs in understanding and evaluating the AI-generated paraphrasing suggestions.Comment: CHI In2Writing Workshop 2023 camera-ready versio

BitE : Accelerating Learned Query Optimization in a Mixed-Workload Environment

Although the many efforts to apply deep reinforcement learning to query optimization in recent years, there remains room for improvement as query optimizers are complex entities that require hand-designed tuning of workloads and datasets. Recent research present learned query optimizations results mostly in bulks of single workloads which focus on picking up the unique traits of the specific workload. This proves to be problematic in scenarios where the different characteristics of multiple workloads and datasets are to be mixed and learned together. Henceforth, in this paper, we propose BitE, a novel ensemble learning model using database statistics and metadata to tune a learned query optimizer for enhancing performance. On the way, we introduce multiple revisions to solve several challenges: we extend the search space for the optimal Abstract SQL Plan(represented as a JSON object called ASP) by expanding hintsets, we steer the model away from the default plans that may be biased by configuring the experience with all unique plans of queries, and we deviate from the traditional loss functions and choose an alternative method to cope with underestimation and overestimation of reward. Our model achieves 19.6% more improved queries and 15.8% less regressed queries compared to the existing traditional methods whilst using a comparable level of resources.Comment: This work was done when the first three author were interns in SAP Labs Korea and they have equal contributio

A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs

Nephrolithiasis is one of the most common urinary disorders in dogs. Although a majority of kidney calculi are non-obstructive and are likely to be asymptomatic, they can lead to parenchymal loss and obstruction as they progress. Thus, early diagnosis of kidney calculi is important for patient monitoring and better prognosis. However, detecting kidney calculi and monitoring changes in the sizes of the calculi from computed tomography (CT) images is time-consuming for clinicians. This study, in a first of its kind, aims to develop a deep learning model for automatic kidney calculi detection using pre-contrast CT images of dogs. A total of 34,655 transverseimage slices obtained from 76 dogs with kidney calculi were used to develop the deep learning model. Because of the differences in kidney location and calculi sizes in dogs compared to humans, several processing methods were used. The first stage of the models, based on the Attention U-Net (AttUNet), was designed to detect the kidney for the coarse feature map. Five different models–AttUNet, UTNet, TransUNet, SwinUNet, and RBCANet–were used in the second stage to detect the calculi in the kidneys, and the performance of the models was evaluated. Compared with a previously developed model, all the models developed in this study yielded better dice similarity coefficients (DSCs) for the automatic segmentation of the kidney. To detect kidney calculi, RBCANet and SwinUNet yielded the best DSC, which was 0.74. In conclusion, the deep learning model developed in this study can be useful for the automated detection of kidney calculi

Intelligent upper-limb exoskeleton using deep learning to predict human intention for sensory-feedback augmentation

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper extremities. Although there are a few examples of exoskeletons, they need manual operations due to the absence of sensor feedback and no intention prediction of movements. Here, we introduce an intelligent upper-limb exoskeleton system that uses cloud-based deep learning to predict human intention for strength augmentation. The embedded soft wearable sensors provide sensory feedback by collecting real-time muscle signals, which are simultaneously computed to determine the user's intended movement. The cloud-based deep-learning predicts four upper-limb joint motions with an average accuracy of 96.2% at a 200-250 millisecond response rate, suggesting that the exoskeleton operates just by human intention. In addition, an array of soft pneumatics assists the intended movements by providing 897 newton of force and 78.7 millimeter of displacement at maximum. Collectively, the intent-driven exoskeleton can augment human strength by 5.15 times on average compared to the unassisted exoskeleton. This report demonstrates an exoskeleton robot that augments the upper-limb joint movements by human intention based on a machine-learning cloud computing and sensory feedback.Comment: 15 pages, 6 figures, 1 table, Submitted for possible publicatio

Dietary Zinc Supplementation Prevents Autism Related Behaviors and Striatal Synaptic Dysfunction in Shank3 Exon 13–16 Mutant Mice

The SHANK family of synaptic proteins (SHANK1–3) are master regulators of the organizational structure of excitatory synapses in the brain. Mutations in SHANK1–3 are prevalent in patients with autism spectrum disorders (ASD), and loss of one copy of SHANK3 causes Phelan-McDermid Syndrome, a syndrome in which Autism occurs in >80% of cases. The synaptic stability of SHANK3 is highly regulated by zinc, driving the formation of postsynaptic protein complexes and increases in excitatory synaptic strength. As ASD-associated SHANK3 mutations retain responsiveness to zinc, here we investigated how increasing levels of dietary zinc could alter behavioral and synaptic deficits that occur with ASD. We performed behavioral testing together with cortico-striatal slice electrophysiology on a Shank3−/− mouse model of ASD (Shank3ex13–1616−/−), which displays ASD-related behaviors and structural and functional deficits at striatal synapses. We observed that 6 weeks of dietary zinc supplementation in Shank3ex13–16−/− mice prevented ASD-related repetitive and anxiety behaviors and deficits in social novelty recognition. Dietary zinc supplementation also increased the recruitment of zinc sensitive SHANK2 to synapses, reduced synaptic transmission specifically through N-methyl-D-aspartate (NMDA)-type glutamate receptors, reversed the slowed decay tau of NMDA receptor (NMDAR)-mediated currents and occluded long term potentiation (LTP) at cortico-striatal synapses. These data suggest that alterations in NMDAR function underlie the lack of NMDAR-dependent cortico-striatal LTP and contribute to the reversal of ASD-related behaviors such as compulsive grooming. Our data reveal that dietary zinc alters neurological function from synapses to behavior, and identifies dietary zinc as a potential therapeutic agent in ASD

Dose response relationship of cumulative anticholinergic exposure with incident dementia: validation study of Korean anticholinergic burden scale

Abstract Background The dose response relationship of nine-year cumulative anticholinergic exposure and dementia onset was investigated using the Korean version anticholinergic burden scale (KABS) in comparison with the Anticholinergic Cognitive Burden Scale (ACB). We also examined the effect of weak anticholinergics in the prediction of dementia. Methods A retrospective case-control study was conducted comprising 86,576 patients after 1:2 propensity score matching using the longitudinal national claims database. For cumulative anticholinergic burden estimation, average daily anticholinergic burden score during the 9 years prior to dementia onset was calculated using KABS and ACB and categorized as minimal, < 0.25; low, 0.25–1; intermediate, 1–2; and high, ≥ 2. Adjusted odds ratio (aOR) between cumulative anticholinergic burden and incident dementia was estimated. Results Patients with high exposure according to KABS and ACB comprised 3.2 and 3.4% of the dementia cohort and 2.1 and 2.8% of the non-dementia cohort, respectively. Dose-response relationships were observed between anticholinergic burden and incident dementia. After adjusting covariates, compared with minimal exposure, patients with high exposure according to KABS and ACB had a significantly higher risk for incident dementia with aOR of 1.71 (95% confidence interval (CI) 1.55–1.87) and 1.22 (CI 1.12–1.33), respectively. With the exclusion of weak anticholinergics, the association became stronger, i.e., 1.41 (CI 1.14–1.75) with ACB whereas the association became slightly weaker with KABS, i.e., 1.60 (CI 1.38–1.86). Conclusion This study confirmed the dose response relationship for cumulative anticholinergic burden measured using the Korean specific anticholinergic burden scale with incident dementia

Novel Smart N95 Filtering Facepiece Respirator with Real-time Adaptive Fit Functionality and Wireless Humidity Monitoring for Enhanced Wearable Comfort

The widespread emergence of the COVID-19 pandemic has transformed our lifestyle, and facial respirators have become an essential part of daily life. Nevertheless, the current respirators possess several limitations such as poor respirator fit because they are incapable of covering diverse human facial sizes and shapes, potentially diminishing the effect of wearing respirators. In addition, the current facial respirators do not inform the user of the air quality within the smart facepiece respirator in case of continuous long-term use. Here, we demonstrate the novel smart N-95 filtering facepiece respirator that incorporates the humidity sensor and pressure sensory feedback-enabled self-fit adjusting functionality for the effective performance of the facial respirator to prevent the transmission of airborne pathogens. The laser-induced graphene (LIG) constitutes the humidity sensor, and the pressure sensor array based on the dielectric elastomeric sponge monitors the respirator contact on the face of the user, providing the sensory information for a closed-loop feedback mechanism. As a result of the self-fit adjusting mode along with elastomeric lining, the fit factor is increased by 3.20 and 5 times at average and maximum respectively. We expect that the experimental proof-of-concept of this work will offer viable solutions to the current commercial respirators to address the limitations.Comment: 20 pages, 5 figures, 1 table, submitted for possible publicatio