Uncovering a Connection between the Teachers’ Professional Development Program and Students’ Learning

Most research suggests professional development improves teachers’ knowledge and pedagogy and enhances teachers’ confidence to facilitate a positive attitude about student learning. This study attempted to investigate the connection between teacher professional development program and students’ Learning. This study took Readers’ Theater Teaching Program (RTTP) for professional development as an example to inquiry how participants applied their new knowledge and skills learned from RTTP to their teaching practice and how the impact influenced students’ reading fluency. This study was a two-year project. In the first year, this study focused on designing and implementing RTTP and evaluating participants’ satisfaction of RTTP, what they learned and how they applied it to design their English reading curriculum. In the second year, the study adopted quasi-experimental design approach and evaluated how participants RT instruction influenced their students’ reading fluency. The participants in this study composed two junior high school English teachers and their students. Data was collected from a number of different sources including teaching observation, semi-structured interviews, teaching diary, teachers’ professional development portfolio, pre/post RT content knowledge tests, teacher survey, and students’ reading fluency tests. The results indicated that teachers learned more RT script writing than other specific contents and hold a positive attitude toward RT instruction and considered it as a very wonderful strategy to meet a variety of needs. All of the experimental group students had a big progress in reading fluency after RT instruction.  The evidences from this study indicated that RT English instruction significantly influenced students’ reading fluency and classroom climate. Keywords: Teacher’s Professional Development, Program Evaluation, Readers’ Theater, English Reading Instruction, Reading fluenc

Effects of Convolutional Autoencoder Bottleneck Width on StarGAN-based Singing Technique Conversion

Singing technique conversion (STC) refers to the task of converting from one voice technique to another while leaving the original singer identity, melody, and linguistic components intact. Previous STC studies, as well as singing voice conversion research in general, have utilized convolutional autoencoders (CAEs) for conversion, but how the bottleneck width of the CAE affects the synthesis quality has not been thoroughly evaluated. To this end, we constructed a GAN-based multi-domain STC system which took advantage of the WORLD vocoder representation and the CAE architecture. We varied the bottleneck width of the CAE, and evaluated the conversion results subjectively. The model was trained on a Mandarin dataset which features four singers and four singing techniques: the chest voice, the falsetto, the raspy voice, and the whistle voice. The results show that a wider bottleneck corresponds to better articulation clarity but does not necessarily lead to higher likeness to the target technique. Among the four techniques, we also found that the whistle voice is the easiest target for conversion, while the other three techniques as a source produce more convincing conversion results than the whistle.Comment: The original edition of this paper will be published in the CMMR 2023 Proceedings. This ArXiv publication is a cop

OMARS: The Framework of an Online Multi-Dimensional Association Rules Mining System

Recently, the integration of data warehouses and data mining has been recognized as the primary platform for facilitating knowledge discovery. Effective data mining from data warehouses, however, needs exploratory data analysis. The users often need to investigate the warehousing data from various perspectives and analyze them at different levels of abstraction. To this end, comprehensive information processing and data analysis have to be systematically constructed surrounding data warehouses, and an on-line mining environment should be provided. In this paper, we propose a system framework to facilitate on-line association rules mining, called OMARS, which is based on the idea of integrating OLAP service and our proposed OLAM cubes and auxiliary cubes. According to the concept of OLAM cubes, we define the OLAM lattice framework that exploit arbitrary hierarchies of dimensions to model all possible OLAM data cubes

Investigating Berezinskii-Kosterlitz-Thouless phase transitions in Kagome spin ice by quantifying Monte Carlo process: Distribution of Hamming distances

We reinvestigate the phase transitions of the Ising model on the Kagome lattice with antiferromagnetic nearest-neighbor and ferromagnetic next-nearest-neighbor interactions, which has a six-state-clock spin ice ground state and two consecutive Berezinskii-Kosterlitz-Thouless (BKT) phase transitions. Employing the classical Monte Carlo (MC) simulations, the phases are characterized by the magnetic order parameter, and the critical temperatures are obtained by the finite-size scaling of related physical quantities. Moreover, we attempt to gain general information on the phase transitions from the MC process instead of MC results and successfully extract the correct transition points with surprisingly high accuracy. Specifically, we focus on the selected data set of uncorrelated MC configurations and quantify the MC process using the distribution of two-configuration Hamming distances in this small data collection. This distribution is more than a quantity that features different behaviors in different phases but also nicely supports the same BKT scaling form as the order parameter, from which we successfully determine the two BKT transition points with surprisingly high accuracy. We also discuss the connection between the phase transitions and the intrinsic dimension extracted from the Hamming distances, which is widely used in the growing field of machine learning and is reported to be able to detect critical points. Our findings provide a new understanding of the spin ice transitions in the Kagome lattice and can hopefully be used similarly to identify transitions in the quantum system on the same lattice with strong frustrations.Comment: 12 figure

Data based reconstruction of complex geospatial networks, nodal positioning, and detection of hidden node

Funding This work was supported by ARO under grant no. W911NF-14-1-0504.Peer reviewedPublisher PD

The Differential Role of Human Cationic Trypsinogen (PRSS1) p.R122H Mutation in Hereditary and Nonhereditary Chronic Pancreatitis: A Systematic Review and Meta-Analysis.

Background:Environmental factors and genetic mutations have been increasingly recognized as risk factors for chronic pancreatitis (CP). The PRSS1 p.R122H mutation was the first discovered to affect hereditary CP, with 80% penetrance. We performed here a systematic review and meta-analysis to evaluate the associations of PRSS1 p.R122H mutation with CP of diverse etiology. Methods:The PubMed, EMBASE, and MEDLINE database were reviewed. The pooled odds ratio (OR) with 95% confidence intervals was used to evaluate the association of p.R122H mutation with CP. Initial analysis was conducted with all etiologies of CP, followed by a subgroup analysis for hereditary and nonhereditary CP, including alcoholic or idiopathic CP. Results:A total of eight case-control studies (1733 cases and 2415 controls) were identified and included. Overall, PRSS1 p.R122H mutation was significantly associated with an increased risk of CP (OR = 4.78[1.13-20.20]). Further analysis showed p.R122H mutation strongly associated with the increased risk of hereditary CP (OR = 65.52[9.09-472.48]) but not with nonhereditary CP, both alcoholic and idiopathic CP. Conclusions:Our study showing the differential role of p.R122H mutation in various etiologies of CP indicates that this complex disorder is likely influenced by multiple genetic factors as well as environmental factors