Cognitive Returns to Having Better Educated Teachers: Evidence from the China Education Panel Survey

Teachers’ own level of human capital development is commonly believed to be deterministic for the quality and effectiveness of their instruction and management in the classroom. Yet, there still exists an international debate on whether better educated teachers contribute to students’ cognitive development. Leveraging a random class-assignment subsample (N = 3436) from a nationally representative teacher-student linked dataset in China, this study reassesses the ongoing contention regarding the value of teacher education. By linking differences in teachers’ own educational attainment levels across different subjects of instruction to variation in seventh grade students’ Chinese, Math and English test scores using student fixed-effect models, this study quantifies the cognitive returns attributable to better educated teachers, in student learning terms. Findings show that teachers with at least a bachelor’s degree contribute substantially to student learning compared to those who are less qualified, by as much as 0.069 SDs or about two additional months of learning over a typical academic year. Additional sensitivity analyses suggest that this observed effect is robust to model specifications, and is consistent for students from different backgrounds.

Between adverbs and particles: A corpus study of selected intensifiers

In this paper, we present a preliminary study of three intensifiers (absolutně, naprosto, úplně) based on data from three different corpora, a written corpus SYN2020, a web corpus ONLINE-ARCHIVE, and a spoken corpus ORTOFON 1. Providing a parallel annotation of a random sample of each intensifier, we focus on their functions and meanings in context. We analyse their properties in order to define those features which are relevant to their word class assignment, and to prepare grounds for the future disambiguation tasks.

The Effect of Pre-Class Assignment in Enhancing the Chinese National-Type School Pupils in Mastery Chinese Language

The KSSR curriculum system introduced by Malaysia's ministry of education is demanding teachers to build pupils who will have the ability to carry out the learning process independently. To build pupils with the ability to learn independently, the pre-class assignment is an implementation needed to give the learning autonomy back to pupils. However, some of the teachers do not like students to do any pre-class assignment because they are afraid that students who have already obtained a certain level of understanding towards the learning materials would feel boring and show the signs of being not interested to listen during the in-class learning process. Thus, this study aims to study the effect of pre-class assignments in enhancing the Chinese language reading comprehension mastery level of pupils from Chinese national-type schools in order to convince more educators to start practicing the use of pre-class assignments. A group of 260 pupils from eight different schools in Kuala Lumpur was involved in this study. A quantitative approach was used in this study and tests were developed and used in this study. The result of this study showed that the implementation of pre-class assignments was effective in helping pupils to master Chinese language reading comprehension. The finding of this research showed that the pupils' performance in Chinese language reading comprehension was not affected by the language used at home and prior exposure of pupils towards pre-class assignments.

The impact of developing a blended learning sub-system on students online learning engagement

In this research, we show a development process of engagement sub-systems in a blended-learning management system and evaluate the impact of student interaction in the whole system. We develop special sub-systems for engagement purposes via forum, course rating, and class assignment modules. During the system development process, we employ continuous improvement methodology which helps to shorten the software delivery time without disturbing the overall operation. We evaluate the impact of engagement processes in terms of behavioral, emotional and cognitive aspects. Our evaluation results show that by employing the engagement sub-systems we have increased a 0.30 satisfaction point on average (1-5 Likert scale) for 11 evaluation survey questions distributed to 305 students during 2 times evaluation period. Another interesting finding from the surveys is that behavioral (discussion forum and attendance list sub-system) and cognitive (course rating sub-system) aspects have great influences for the students’ activities (class assignment sub-system) which finally has a great impact on their cognitive performances.

Self-reported alcohol topography, past alcohol use history, and current alcohol cravings

Introduction: Drinking topography is the idea that how a person drinks, in addition to how much they drink, can influence the immediate and long-term effects of alcohol consumption. Measures are often crude, and there has been little effort to associate such measures with disease status. Methods: To address these gaps, an online survey was completed by 734 U.S. participants. They described how they usually drank using a series of 20 words (e.g., chug, nip, nurse, shots). A latent class analysis was performed to identify clusters of descriptors. Past alcohol use history, as measured by the Alcohol Use Disorders Identification Test (AUDIT), and current alcohol cravings, as measured by the Desires for Alcohol Questionnaire (DAQ), were then regression on class assignment. The analysis was adjusted for age, sex, race, ethnicity, marital status, income, and education level. Results: Three LCA classes were accepted, with the class characterized by the words shot, swig, gulp, chug, and guzzle having significantly higher AUDIT (b[95% CI] = 1.22 [0.98, 1.46], p < 0.001) and DAQ (b[95% CI] = 12.9 [10.5, 15.3], p < 0.001) scores relative to the class characterized by sample, sip, and nurse. The third class predominantly used drink and sip, and was not significantly different from the sample class (p's = 0.290-0.471). Conclusions: Measuring drinking topography offers a unique way to identify individuals at risk of an alcohol use disorder or those who may be resistant to treatment. It may also be useful in determining drinking motives. However, additional research is needed.

Can Students Learn to Use Textbooks Effectively?

Can a class assignment covering the reading help students do better in an introductory chemistry class for nonmajors? Two sections of the same course have been compared where one section had a reading assignment that "forced" the students to interact with the text while the other class had the typical homework, quiz assignments, and discussion posts. Both groups took 3 exams and the final exam. A t-test compared the means for each exam, the final exam, and the overall class average. The mean was higher for the group that had the reading assignment in each case. The mean for the class was 71.3% versus 66.7% (p value = 0.050). The mean for exam 1 was 73.0% versus 60.5% (p = 0.052). The mean for exam 2 was 73.3% versus 67.5% (p = 0.225). The mean for exam 3 was 76.9% versus 62.4% (p = 0.044). The final exam was 56.6% versus 55.3% (p = 0.445). The implication may be that students do not understand how to study or what is involved in effective studying. When reading the text is incentivized as an assignment, more students seemed to interact with the text, and more were likely to succeed in the class. This paper examines how interacting with the text for a class supports comprehension.

Student Preferences Impact Outcome of Flipped Classroom in Dental Education: Students Favoring Flipped Classroom Benefited More

Many reports in dental education showed that student learning improved with the flipped classroom method. However, there are few reports that describe how different subsets of students may benefit from the flipped classroom. In this study, we investigated how students’ preference for the flipped classroom impacted their learning outcome. We used a flipped classroom module on the physiology of the autonomic nervous system taught to year one Doctor of Dental Surgery students to test the hypothesis that students who favored the flipped classroom performed better on assessment quizzes. The module was composed of pre-class activity, out-of-class assignment, in-class discussion, and two in-class quizzes. Quiz 1 was given after students self-studied the foundational content online through the pre-class activity, and Quiz 2 was at the end of the module. Students filled out a survey to report learning experiences and preferences. Fewer students scored below 75% on Quiz 2 than on Quiz 1. Students’ self-evaluated understanding of content significantly improved after finishing the assignment and discussion compared to finishing the pre-class activity alone. Moreover, students who preferred to learn through the flipped classroom scored higher in Quiz 2. Students with higher overall grades in the course preferred the flipped classroom more than low performers. Our results indicated that students favoring the flipped classroom method spent more time on the assignment, understood the content better, and performed better on assessments than students who prefer traditional lectures.

Recognition of Cosmic Ray Images Obtained from CMOS Sensors Used in Mobile Phones by Approximation of Uncertain Class Assignment with Deep Convolutional Neural Network

In this paper, we describe the convolutional neural network (CNN)-based approach to the problems of categorization and artefact reduction of cosmic ray images obtained from CMOS sensors used in mobile phones. As artefacts, we understand all images that cannot be attributed to particles’ passage through sensor but rather result from the deficiencies of the registration procedure. The proposed deep neural network is composed of a pretrained CNN and neural-network-based approximator, which models the uncertainty of image class assignment. The network was trained using a transfer learning approach with a mean squared error loss function. We evaluated our approach on a data set containing 2350 images labelled by five judges. The most accurate results were obtained using the VGG16 CNN architecture; the recognition rate (RR) was 85.79% ± 2.24% with a mean squared error (MSE) of 0.03 ± 0.00. After applying the proposed threshold scheme to eliminate less probable class assignments, we obtained a RR of 96.95% ± 1.38% for a threshold of 0.9, which left about 62.60% ± 2.88% of the overall data. Importantly, the research and results presented in this paper are part of the pioneering field of the application of citizen science in the recognition of cosmic rays and, to the best of our knowledge, this analysis is performed on the largest freely available cosmic ray hit dataset.