“Doing” mindsets in the classroom: A coding scheme for teacher and student mindset-related verbalizations

There is a growing body of research showing the crucial role that students’ growth versus fixed ability-mindsets have in their school achievement, enjoyment, and resilience. The overwhelming majority of this research adopts a variable-oriented approach. As a result, little is known about how teachers and students co-regulate each other’s mindsets within classroom interactions. This manuscript addresses the need for more person-oriented research that examines how teachers and students do mindsets in naturalistic settings, i.e., their mindset-related verbalizations. In this manuscript, we provide a coding scheme to study the moment-to-moment dynamics of mindset-related verbalizations of both teachers and students within Science, Technology, Engineering, and Mathematics (STEM) contexts: The STEAM (Student-TEAcher-Mindset) coding scheme. We demonstrate the utility of the coding system through content and ecological validity, inter-rater reliability, and a case study of STEAM-generated time-series data. We show how these data can be used to chart moment-to-moment dynamics that occur between teacher and student. The coding scheme provides teachers and researchers with a practical tool for analyzing how person-specific mindset-related language can wax and wane in the context of peer and teacher interactions within STEM lessons.

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Manual for Qualitative Dynamical Coding of Individual of Dyadic Coordination Trajectories. Version 2015

10.31219/osf.io/m56zx ◽

2018 ◽

Author(s):

Ana Teixeira Melo ◽

Madalena Alarcão

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Dyadic Data ◽

Coding Scheme ◽

Dynamic Patterns

This manuscript corresponds to a Manual for Qualitative Dynamical Coding of Individual of Dyadic CoordinationTrajectories. This manual reports a coding scheme aimed to produce synthetic qualitative descriptions ofthe overall patterns and dynamics of the trajectories (sequences of states) of one or moreselected dimension of interest pertaining to individuals or dyadic interpersonal system. It proposes a method where simple time series data collected from Likert-type scales can transformed and coded in a way that allows for the inspection of dynamic patterns of change for a given variable or dimension of interest. It presents codes to analyse the dynamics of coordination between individuals for dyadic data.

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Rheology of the Zagros Lithosphere from Post-Seismic Deformation of the 2017 Mw7.3 Kermanshah, Iraq, Earthquake

Remote Sensing ◽

10.3390/rs12122032 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2032 ◽

Cited By ~ 1

Author(s):

Xiaoran Lv ◽

Falk Amelung ◽

Yun Shao ◽

Shu Ye ◽

Ming Liu ◽

...

Keyword(s):

Displacement Field ◽

Time Series Data ◽

Series Data ◽

Uppermost Mantle ◽

Aftershock Sequences ◽

Deformation Processes ◽

The Moment ◽

Seismic Deformation ◽

Lower Crustal ◽

Zagros Region

We use 2018–2020 Sentinel-1 InSAR time series data to study post-seismic deformation processes following the 2017 Mw 7.3 Kermanshah, Iraq earthquake. We remove displacements caused by two large aftershock sequences from the displacement field. We find that for a six month period the response is dominated by afterslip along the up-dip extension of the coseismic rupture zone, producing up to 6 cm of radar line-of-sight displacements. The moment magnitude of afterslip is Mw 5.9 or 12% of the mainshock moment. After that period, the displacement field is best explained by viscoelastic relaxation and a lower crustal viscosity of η l c = 1 − 0.4 + 0.8 × 10 19 Pas . The viscosity of the uppermost mantle is not constrained by the data, except that it is larger than 0.6 × 10 19 Pas . The relatively high lower crustal and uppermost mantle viscosities are consistent with a cold and dry lithosphere of the Zagros region.

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Toward Capturing Scientific Evidence in Elderly Care: Efficient Extraction of Changing Facial Feature Points

Sensors ◽

10.3390/s21206726 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6726

Author(s):

Kosuke Hirayama ◽

Sinan Chen ◽

Sachio Saiki ◽

Masahide Nakamura

Keyword(s):

Time Series ◽

Facial Expression ◽

Time Series Data ◽

Scientific Evidence ◽

Elderly Care ◽

Facial Feature ◽

The Elderly ◽

Series Data ◽

Feature Values ◽

The Moment

To capture scientific evidence in elderly care, a user-defined facial expression sensing service was proposed in our previous study. Since the time-series data of feature values have been growing at a high rate as the measurement time increases, it may be difficult to find points of interest, especially for detecting changes from the elderly facial expression, such as many elderly people can only be shown in a micro facial expression due to facial wrinkles and aging. The purpose of this paper is to implement a method to efficiently find points of interest (PoI) from the facial feature time-series data of the elderly. In the proposed method, the concept of changing point detection into the analysis of feature values is incorporated by us, to automatically detect big fluctuations or changes in the trend in feature values and detect the moment when the subject’s facial expression changed significantly. Our key idea is to introduce the novel concept of composite feature value to achieve higher accuracy and apply change-point detection to it as well as to single feature values. Furthermore, the PoI finding results from the facial feature time-series data of young volunteers and the elderly are analyzed and evaluated. By the experiments, it is found that the proposed method is able to capture the moment of large facial movements even for people with micro facial expressions and obtain information that can be used as a clue to investigate their response to care.

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Spatiotemporal slip distributions associated with the 2018–2019 Bungo Channel long-term slow slip event inverted from GNSS data

Scientific Reports ◽

10.1038/s41598-021-03982-6 ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Yukinari Seshimo ◽

Shoichi Yoshioka

Keyword(s):

Time Series Data ◽

Series Data ◽

Slow Slip ◽

Slow Slip Events ◽

Bungo Channel ◽

Slip Event ◽

The Moment ◽

Gnss Data ◽

Gnss Time Series

AbstractLong-term slow slip events (L-SSEs) have repeatedly occurred beneath the Bungo Channel in southwestern Japan with durations of several months to a couple of years, with a recurrence interval of approximately 6 years. We estimated the spatiotemporal slip distributions of the 2018–2019 Bungo Channel L-SSE by inverting processed GNSS time series data. This event was divided into two subevents, with the first on the southwest side of the Bungo Channel from 2018.3 to 2018.7 and the second beneath the Bungo Channel from 2018.8 to 2019.4. Tectonic tremors became active on the downdip side of the L-SSE occurrence region when large slow slips took place beneath the Bungo Channel. Compared with the previous Bungo Channel L-SSEs, this spatiotemporal slip pattern and amount were similar to those of the 2002–2004 L-SSE. However, the slip expanded in the northeast and southwest directions in the latter half of the second subevent. The maximum amount of slip, the maximum slip velocity, the total released seismic moment, and the moment magnitude of the 2018–2019 L-SSE were estimated to be 28 cm, 54 cm/year, $$4.4 \times 10^{19}$$ 4.4 × 10 19 Nm, and 7.0, respectively, all of which were the largest among the 1996–1998, 2002–2004, 2009–2011, and 2018–2019 L-SSEs.

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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Some statistical and CI models to predict chaotic high-frequency financial data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189107 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6419-6430

Author(s):

Dusan Marcek

Keyword(s):

Time Series Data ◽

Moving Average ◽

Methodological Approach ◽

Back Propagation ◽

Large Data ◽

Series Data ◽

Data Set ◽

Training Time ◽

Optimal Population ◽

Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽

10.15408/etk.v19i2.17206 ◽

2020 ◽

Vol 19 (2) ◽

Author(s):

Budiandru Budiandru ◽

Sari Yuniarti

Keyword(s):

Interest Rates ◽

Stock Price ◽

Time Series Data ◽

Series Data ◽

Islamic Banking ◽

Vector Error Correction ◽

Investment Financing ◽

Vector Error ◽

Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

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