Exploring the Relationship Between Online Discourse and Commitment in Twitter Professional Learning Communities

Educators show great interest in participating in social-media communities, such as Twitter, to support their professional development and learning. The majority of the research into Twitter-based professional learning communities has investigated why educators choose to use Twitter for professional development and learning and what they actually do in these communities. However, few studies have examined why certain community members remain committed and others gradually drop out. To fill this gap in the research, this study investigated how some key features of online discourse influenced the continued participation of the members of a Twitter-based professional learning community. More than 600,000 tweets generated over six years under the hashtag #edchat were gathered. Online discourse was deconstructed to the cognitive dimension, the interactive dimension, and the social dimension. Text-mining methods were then used to automatically identify these dimensions in the tweets. Finally, survival analysis was used to quantify the influences of these dimensions on users’ commitment time to the Twitter community. The implications of the results and findings are then discussed

Pump Depletion in Parametric Amplification

We derive analytic solutions for Heisenberg evolution under the trilinear parametric Hamiltonian which are correct to second order in the interaction strength but are valid for all pump amplitudes. The solutions allow pump depletion effects to be incorporated in the description of parametric amplification in experimentally relevant scenarios and the resulting new phenomena to be rigorously described

Student Assessment in Small Groups: A Spectral Clustering Model

Enabling the formative assessment of students while limiting demands on teachers’ time is a significant concern for technology mediated learning in small groups. Previous approaches have either required extensive time commitments on the part of teachers or relied on the development of special computational models of behavior. Oftentimes, these models overlook the way in which traces of student interaction in a learning system also constitute traces of human behavior, and, act only as “blunt instruments” relying only on the automated accounting of student activities. We employ activity theory to categorize traces of student behavior captured from a Virtual Math Teams (VMT) geometry class in an online, synchronous environment. From this, six semantically-grounded measures are generated for each student. Using these, a recently-developed clustering algorithm – spectral clustering – is coded to identify students who have similar behavior patterns. Structured in such a fashion, the theoretical and computational approach taken allows for an automated and meaningfully-grounded assessment of student performance, enabling teachers to offer concrete and personalized help in a timely format.ye

Numerical Built-In Method for the Nonlinear JRC/JCS Model in Rock Joint

The joint surface is widely distributed in the rock, thus leading to the nonlinear characteristics of rock mass strength and limiting the effectiveness of the linear model in reflecting characteristics. The JRC/JCS model is the nonlinear failure criterion and generally believed to describe the characteristics of joints better than other models. In order to develop the numerical program for JRC/JCS model, this paper established the relationship between the parameters of the JRC/JCS and Mohr-Coulomb models. Thereafter, the numerical implement method and implementation process of the JRC/JCS model were discussed and the reliability of the numerical method was verified by the shear tests of jointed rock mass. Finally, the effect of the JRC/JCS model parameters on the shear strength of the joint was analyzed

Google Analytics based Temporal-Geospatial Analysis for Web Management: A Case Study of a K-12 Online Resource Website

As Google Analytics becomes increasingly popular, more detailed records of users’ behaviors can be captured and analyzed to better understand the performance of websites. However, current Google Analytics related research usually draws conclusions from rough estimation based on the observation of the dashboard or other basic statistical processing of the data. This study aims to provide a more accurate and informative analysis from both temporal and geospatial perspectives via clustering and GIS application. The results obtained from a resource website case study demonstrate that the proposed method is able to help web managers better examine the temporal effect on users’ visiting patterns based on accurate mathematical computation as well as provides more geographical insight into website performance through the constructed density measure and 3D graphic presentation. By offering in-depth quantitative information relying on mining data from web logs, such a study can help web stakeholders make better decisions on how to maintain and improve the websites, especially adjusting resources by considering temporal fluctuations and inequity in geographical distribution

Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference

For middle-school math students, interactive question-answering (QA) with tutors is an effective way to learn. The flexibility and emergent capabilities of generative large language models (LLMs) has led to a surge of interest in automating portions of the tutoring process - including interactive QA to support conceptual discussion of mathematical concepts. However, LLM responses to math questions can be incorrect or mismatched to the educational context - such as being misaligned with a school's curriculum. One potential solution is retrieval-augmented generation (RAG), which involves incorporating a vetted external knowledge source in the LLM prompt to increase response quality. In this paper, we designed prompts that retrieve and use content from a high-quality open-source math textbook to generate responses to real student questions. We evaluate the efficacy of this RAG system for middle-school algebra and geometry QA by administering a multi-condition survey, finding that humans prefer responses generated using RAG, but not when responses are too grounded in the textbook content. We argue that while RAG is able to improve response quality, designers of math QA systems must consider trade-offs between generating responses preferred by students and responses closely matched to specific educational resources.Comment: 6 pages, presented at NeurIPS'23 Workshop on Generative AI for Education (GAIED

Rapid and Efficient Extraction and HPLC Analysis of Sesquiterpene Lactones from Aucklandia lappa Root.

The root of Aucklandia lappa Decne, family Asteraceae, is widely used in Asian traditional medicine due to its sesquiterpene lactones. The aim of this study was the development and optimization of the extraction and analysis of these sesquiterpene lactones. The current Chinese Pharmacopoeia reports a monograph for "Aucklandiae Radix", but the extraction method is very long and tedious including maceration overnight and ultrasonication. Different extraction protocols were evaluated with the aim of optimizing the maceration period, solvent, and shaking and sonication times. The optimized method consists of only one hour of shaking plus 30 minutes of sonication using 100% MeOH as solvent. 1H NMR spectroscopy was used as a complementary analytical tool to monitor the residual presence of sesquitepene lactones in the herbal material. A suitable LC-DAD method was set up to quantify the sesquiterpene lactones. Recovery was ca. 97%, but a very high instability of constituents was found after powdering the herbal drug. A loss of about 20% of total sesquiterpenes was found after 15–20 days; as a consequence, it is strongly endorsed to use fresh powdered herbal material to avoid errors in the quantification

Determination of reference intervals of serum levels of human epididymis protein 4 (HE4) in Chinese women

The detailed numbers of subjects from different centers. (DOCX 17.3 kb

Screening of deafness-causing DNA variants that are common in patients of European ancestry using a microarray-based approach

The unparalleled heterogeneity in genetic causes of hearing loss along with remarkable differences in prevalence of causative variants among ethnic groups makes single gene tests technically inefficient. Although hundreds of genes have been reported to be associated with nonsyndromic hearing loss (NSHL), GJB2, GJB6, SLC26A4, and mitochondrial (mt) MT-RNR1 and MTTS are the major contributors. In order to provide a faster, more comprehensive and cost effective assay, we constructed a DNA fluidic array, CapitalBioMiamiOtoArray, for the detection of sequence variants in five genes that are common in most populations of European descent. They consist of c.35delG, p.W44C, p.L90P, c.167delT (GJB2); 309kb deletion (GJB6); p.L236P, p.T416P (SLC26A4); and m.1555A>G, m.7444G>A (mtDNA). We have validated our hearing loss array by analyzing a total of 160 DNAs samples. Our results show 100% concordance between the fluidic array biochip-based approach and the established Sanger sequencing method, thus proving its robustness and reliability at a relatively low cost