TY  - JOUR
A1  - Pohle, Lara
A1  - Hosoya, Georg
A1  - Pohle, Jennifer
A1  - Meyer-Jenßen, Lars
T1  - The relationship between early childhood teachers' instructional quality and children's mathematics development
JF  - Learning and instruction : the journal of the European Association for Research on Learning and Instruction
N2  - This study examined how early childhood (EC) teachers' instructional quality predicted children's development in mathematics across two measurement occasions. Therefore, EC teachers' (n = 25) instructional quality was assessed using one standardized observation instrument covering both domain-specific and general aspects of instructional quality. Additionally, data on children's (n = 208) outcome in early number skills was collected applying a standardized test. Multilevel structural equation modeling was used accounting for nested data. Children's age and the average size of preschool groups were controlled for. Results revealed that EC teachers' instructional quality predicted children's development but was not associated with their initial achievement. The findings suggest that instruments covering domain-specific and general aspects might be helpful in order to measure EC teachers' instructional quality in mathematics and predict children's learning growth. Understanding the mechanisms between instructional quality and children's development may help EC teachers to enhance their math teaching in practice.
KW  - Early childhood education
KW  - Early childhood teachers
KW  - Instructional
KW  - quality
KW  - Early mathematics development
Y1  - 2022
U6  - https://doi.org/10.1016/j.learninstruc.2022.101636
SN  - 0959-4752
SN  - 1873-3263
VL  - 82
PB  - Elsevier
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Pohle, Jennifer
A1  - Adam, Timo
A1  - Beumer, Larissa
T1  - Flexible estimation of the state dwell-time distribution in hidden semi-Markov models
JF  - Computational statistics & data analysis
N2  - Hidden semi-Markov models generalise hidden Markov models by explicitly modelling the time spent in a given state, the so-called dwell time, using some distribution defined on the natural numbers. While the (shifted) Poisson and negative binomial distribution provide natural choices for such distributions, in practice, parametric distributions can lack the flexibility to adequately model the dwell times. To overcome this problem, a penalised maximum likelihood approach is proposed that allows for a flexible and data-driven estimation of the dwell-time distributions without the need to make any distributional assumption. This approach is suitable for direct modelling purposes or as an exploratory tool to investigate the latent state dynamics. The feasibility and potential of the suggested approach is illustrated in a simulation study and by modelling muskox movements in northeast Greenland using GPS tracking data. The proposed method is implemented in the R-package PHSMM which is available on CRAN.
KW  - Penalized likelihood
KW  - Smoothing
KW  - Time series
KW  - Animal movement modeling
Y1  - 2022
U6  - https://doi.org/10.1016/j.csda.2022.107479
SN  - 0167-9473
SN  - 1872-7352
VL  - 172
PB  - Elsevier
CY  - Amsterdam
ER  -