ChEDDAR: Student-ChatGPT Dialogue in EFL Writing Education

The integration of generative AI in education is expanding, yet empirical analyses of large-scale, real-world interactions between students and AI systems still remain limited. In this study, we present ChEDDAR, ChatGPT & EFL Learner's Dialogue Dataset As Revising an essay, which is collected from a semester-long longitudinal experiment involving 212 college students enrolled in English as Foreign Langauge (EFL) writing courses. The students were asked to revise their essays through dialogues with ChatGPT. ChEDDAR includes a conversation log, utterance-level essay edit history, self-rated satisfaction, and students' intent, in addition to session-level pre-and-post surveys documenting their objectives and overall experiences. We analyze students' usage patterns and perceptions regarding generative AI with respect to their intent and satisfaction. As a foundational step, we establish baseline results for two pivotal tasks in task-oriented dialogue systems within educational contexts: intent detection and satisfaction estimation. We finally suggest further research to refine the integration of generative AI into education settings, outlining potential scenarios utilizing ChEDDAR. ChEDDAR is publicly available at https://github.com/zeunie/ChEDDAR

RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Education

The integration of generative AI in education is expanding, yet empirical analyses of large-scale and real-world interactions between students and AI systems still remain limited. Addressing this gap, we present RECIPE4U (RECIPE for University), a dataset sourced from a semester-long experiment with 212 college students in English as Foreign Language (EFL) writing courses. During the study, students engaged in dialogues with ChatGPT to revise their essays. RECIPE4U includes comprehensive records of these interactions, including conversation logs, students' intent, students' self-rated satisfaction, and students' essay edit histories. In particular, we annotate the students' utterances in RECIPE4U with 13 intention labels based on our coding schemes. We establish baseline results for two subtasks in task-oriented dialogue systems within educational contexts: intent detection and satisfaction estimation. As a foundational step, we explore student-ChatGPT interaction patterns through RECIPE4U and analyze them by focusing on students' dialogue, essay data statistics, and students' essay edits. We further illustrate potential applications of RECIPE4U dataset for enhancing the incorporation of LLMs in educational frameworks. RECIPE4U is publicly available at https://zeunie.github.io/RECIPE4U/.Comment: arXiv admin note: text overlap with arXiv:2309.1324

Rethinking Annotation: Can Language Learners Contribute?

Researchers have traditionally recruited native speakers to provide annotations for widely used benchmark datasets. However, there are languages for which recruiting native speakers can be difficult, and it would help to find learners of those languages to annotate the data. In this paper, we investigate whether language learners can contribute annotations to benchmark datasets. In a carefully controlled annotation experiment, we recruit 36 language learners, provide two types of additional resources (dictionaries and machine-translated sentences), and perform mini-tests to measure their language proficiency. We target three languages, English, Korean, and Indonesian, and the four NLP tasks of sentiment analysis, natural language inference, named entity recognition, and machine reading comprehension. We find that language learners, especially those with intermediate or advanced levels of language proficiency, are able to provide fairly accurate labels with the help of additional resources. Moreover, we show that data annotation improves learners' language proficiency in terms of vocabulary and grammar. One implication of our findings is that broadening the annotation task to include language learners can open up the opportunity to build benchmark datasets for languages for which it is difficult to recruit native speakers.Comment: ACL 202

RECIPE: How to Integrate ChatGPT into EFL Writing Education

The integration of generative AI in the field of education is actively being explored. In particular, ChatGPT has garnered significant interest, offering an opportunity to examine its effectiveness in English as a foreign language (EFL) education. To address this need, we present a novel learning platform called RECIPE (Revising an Essay with ChatGPT on an Interactive Platform for EFL learners). Our platform features two types of prompts that facilitate conversations between ChatGPT and students: (1) a hidden prompt for ChatGPT to take an EFL teacher role and (2) an open prompt for students to initiate a dialogue with a self-written summary of what they have learned. We deployed this platform for 213 undergraduate and graduate students enrolled in EFL writing courses and seven instructors. For this study, we collect students' interaction data from RECIPE, including students' perceptions and usage of the platform, and user scenarios are examined with the data. We also conduct a focus group interview with six students and an individual interview with one EFL instructor to explore design opportunities for leveraging generative AI models in the field of EFL education

Assessment of the modulation degrees of intensity-modulated radiation therapy plans

Background To evaluate the modulation indices (MIs) for predicting the plan delivery accuracies of intensity-modulated radiation therapy (IMRT) plans. Methods A total of 100 dynamic IMRT plans that used TrueBeam STx and 102 dynamic IMRT plans that used Trilogy were selected. For each plan, various MIs were calculated, which included the modulation complexity score (MCS), plan-averaged beam area (PA), plan-averaged beam irregularity (PI), plan-averaged beam modulation (PM), MI quantifying multi-leaf collimator (MLC) speeds (MIs), MI quantifying MLC acceleration (MIa), and MI quantifying MLC acceleration and segment aperture irregularity (MIc,IMRT). To determine plan delivery accuracy, global gamma passing rates, MLC errors of log files, and dose-volumetric parameter differences between original and log file-reconstructed IMRT plans were obtained. To assess the ability of each MI for predicting plan delivery accuracy, Spearmans rank correlation coefficients (rs) between MIs and plan delivery accuracy measures were calculated. Results PI showed moderately strong correlations with gamma passing rates in MapCHECK2 measurements of both TrueBeam STx and Trilogy (rs = − 0.591 with p <  0.001 and − 0.427 with p <  0.001 to with gamma criterion of 2%/2 mm, respectively). For ArcCHECK measurements, PI also showed moderately strong correlations with the gamma passing rates in the ArcCHECK measurements of TrueBeam STx and Trilogy (rs = − 0.545 with p <  0.001 and rs = − 0.581 with p <  0.001 with gamma criterion of 2%/2 mm, respectively). The PI showed the second strongest correlation with MLC errors in both TrueBeam STx and Trilogy (rs = 0.861 with p <  0.001 and rs = 0.767 with p <  0.001, respectively). In general, the PI showed moderately strong correlations with every plan delivery accuracy measure. Conclusions The PI showed moderately strong correlations with every plan delivery accuracy measure and therefore is a useful predictor of IMRT delivery accuracy.This work was supported by a National Research Foundation of Korea (NRF) grant from the Korea government (MSIP). (No.2017M2A2A7A02020639, No.2017M2A2A7A02020640, No.2017M2A2A7A02020641, No.2017M2A2A7A02020643)

Optimal collimator rotation based on the outline of multiple brain targets in VMAT

Background The aim of this study was to investigate the dosimetric quality in volumetric modulated arc therapy (VMAT) plans with optimal collimator angles that can represent the outline of multiple brain targets. Methods Twenty patients with multiple target volumes in the brain cases were selected retrospectively. To better represent the outline of the multiple brain targets, four conformal arc plans were generated for each patient using one full arc with four collimator settings. The optimal collimator angles calculated from the integrated multi-leaf collimator (MLC) aperture that had the smallest aperture size for certain collimator settings of the conformal arc plan were selected. VMAT plans with the optimal collimator angles with angular sections of 40° and 60° (Colli-VMAT (40°), Colli-VMAT (60°)) were generated, followed by evaluation of field sizes, dose-volumetric parameters and total monitor units (MUs). Results Patient-averaged values of field sizes for Colli-VMAT (40°) (111.5 cm2) were lowest and 1.3 times smaller than those for Std-VMAT (143.6 cm2). Colli-VMAT plans improved sparing of most normal organs but for brain stem and left parotid gland. For the total MUs, the averaged values obtained with the Colli-VMAT (40°) (390 ± 148 MU) were smaller than those obtained with the Std-VMAT (472 ± 235 MU). Conclusions The Colli-VMAT plans with smaller angular sections could be suitable in the clinic for multiple brain targets as well as for irregularly shaped targets. Determination of the optimal collimator rotation generally showed good normal tissue sparing and MU reduction for multiple brain targets.This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03036093) and by a grant from the National R&D Program for Cancer Control, Ministry of Health and Welfare, Republic of Korea (HA16C0025)

Impact of Body Mass Index on the relationship of epicardial adipose tissue to metabolic syndrome and coronary artery disease in an Asian population

<p>Abstract</p> <p>Background</p> <p>In a previous study, we demonstrated that the thickness of epicardial adipose tissue (EAT), measured by echocardiography, was increased in patients with metabolic syndrome (MS) and coronary artery disease (CAD). Several studies on obese patients, however, failed to demonstrate any relationship between EAT and CAD. We hypothesized that body mass index (BMI) affected the link between EAT and MS and CAD.</p> <p>Methods</p> <p>We consecutively enrolled 643 patients (302 males, 341 females; 59 ± 11 years), who underwent echocardiography and coronary angiography. The EAT thickness was measured on the free wall of the right ventricle at the end of diastole. All patients were divided into two groups: high BMI group, ≥27 kg/m²(n = 165), and non-high BMI group, < 27 kg/m²(n = 478).</p> <p>Results</p> <p>The median and mean EAT thickness of 643 patients were 3.0 mm and 3.1 ± 2.4 mm, respectively. In the non-high BMI group, the median EAT thickness was significantly increased in patients with MS compared to those without MS (3.5 vs. 1.9 mm, p < 0.001). In the high BMI group, however, there was no significant difference in the median EAT thickness between patients with and without MS (3.0 vs. 2.5 mm, p = 0.813). A receiver operating characteristic (ROC) curve analysis predicting MS revealed that the area under the curve (AUC) of the non-high BMI group was significantly larger than that of the high BMI group (0.659 vs. 0.506, p = 0.007). When compared to patients without CAD, patients with CAD in both the non-high and high BMI groups had a significantly higher median EAT thickness (3.5 vs. 1.5 mm, p < 0.001 and 4.0 vs. 2.5 mm, p = 0.001, respectively). However, an ROC curve analysis predicting CAD revealed that the AUC of the non-high BMI group tended to be larger than that of the high BMI group (0.735 vs. 0.657, p = 0.055).</p> <p>Conclusions</p> <p>While EAT thickness was significantly increased in patients with MS and CAD, the power of EAT thickness to predict MS and CAD was stronger in patients with BMI < 27 kg/m². These findings showed that the measurement of EAT thickness by echocardiography might be especially useful in an Asian population with a non-high BMI, less than 27 kg/m².</p

Android Fat Depot Is More Closely Associated with Metabolic Syndrome than Abdominal Visceral Fat in Elderly People

BACKGROUND: Fat accumulation in android compartments may confer increased metabolic risk. The incremental utility of measuring regional fat deposition in association with metabolic syndrome (MS) has not been well described particularly in an elderly population. METHODS AND FINDINGS: As part of the Korean Longitudinal Study on Health and Aging, which is a community-based cohort study of people aged more than 65 years, subjects (287 male, 75.9±8.6 years and 278 female, 76.0±8.8 years) with regional body composition data using Dual energy X-ray absorptiometry for android/gynoid area, computed tomography for visceral/subcutaneous adipose tissue (VAT/SAT), and cardiometabolic markers including adiponectin and high-sensitivity CRP were enrolled. We investigated the relationship between regional body composition and MS in multivariate regression models. Mean VAT and SAT area was 131.4±65.5 cm(2) and 126.9±55.2 cm(2) in men (P = 0.045) and 120.0±46.7 cm(2) and 211.8±65.9 cm(2) in women (P<0.01). Mean android and gynoid fat amount was 1.8±0.8 kg and 2.5±0.8 kg in men and 2.0±0.6 kg and 3.3±0.8 kg in women, respectively (both P<0.01). VAT area and android fat amount was strongly correlated with most metabolic risk factors compared to SAT or gynoid fat. Furthermore, android fat amount was significantly associated with clustering of MS components after adjustment for multiple parameters including age, gender, adiponectin, hsCRP, a surrogate marker of insulin resistance, whole body fat mass and VAT area. CONCLUSIONS: Our findings are consistent with the hypothesized role of android fat as a pathogenic fat depot in the MS. Measurement of android fat may provide a more complete understanding of metabolic risk associated with variations in fat distribution

Dispersion retrieval from multi-level ultra-deep reactive-ion-etched microstructures for terahertz slow-wave circuits

A multi-level microstructure is proposed for terahertz slow-wave circuits, with dispersion relation retrieved by scattering parameter measurements. The measured return loss shows strong resonances above the cutoff with negligible phase shifts compared with finite element analysis. Splitting the circuit into multi levels enables a low aspect ratio configuration that alleviates the loading effect of deep-reactive-ion etching on silicon wafers. This makes it easier to achieve flat-etched bottom and smooth sidewall profiles. The dispersion retrieved from the measurement, therefore, corresponds well to the theoretical estimation. The result provides a straightforward way to the precise determination of dispersions in terahertz vacuum electronics.</jats:p

A Case of Acute Myocardial Infarction Caused by Distal Embolization of a Left Main Coronary Artery Thrombus

Coronary embolism is an uncommon cause of myocardial infarction. A 48-year-old male presented with typical chest pain of an MI. There was no definite ST segment change on electrocardiogram (ECG) and no elevation of myocardial enzymes. Coronary angiography (CAG) revealed occlusion of the distal left anterior descending coronary artery (dLAD), the distal left circumflex coronary artery (dLCX), the diagonal branch (D) and the obtuse marginal branch (OM), with a large filling defect in the left main coronary artery (LMA) that caused the myocardial infarction. We considered the possibility that coronary embolization was caused by the migration of a thrombus in the LMA during CAG. We did balloon angioplasty in the dLAD, dLCX, OM and D and treated the patient with glycoprotein IIb/IIIa receptor antagonist. However, thrombi remained in the dLAD, OM, and dLCX. After 3 days of anti-thrombotic treatment, follow-up CAG revealed only slight resolution of thrombi in the LAD. After triple antiplatelet agent medication for 1 year, a follow-up CAG showed a resolution of the thrombi in all coronary arteries