This study reports relationships between general academic self-concept and achievement in grade 3 and grade 5. Gender-specific effects were investigated using a longitudinal, two-cycle, 3-year autoregressive cross-lagged panel design in a large, representative sample of Polish primary school pupils (N = 4226). Analysis revealed (a) reciprocal relations between general academic self-concept and achievement over time but the influence of prior achievement on self-concept was stronger; (b) on average, levels of both constructs declined over time; (c) gender differences were not observed in longitudinal relations (i.e. cross-lagged, autoregressive and intra-wave correlations); (d) girls demonstrated higher mean levels of academic achievement at both grades; and (e) average level of general academic selfconcept was not gender differentiated in grade 3 but decreased more for girls. These results are discussed in the light of the theoretical and practical implications.
BackgroundProgressive methods offer efficient and reasonably good solutions to the multiple sequence alignment problem. However, resulting alignments are biased by guide-trees, especially for relatively distant sequences.ResultsWe propose MSARC, a new graph-clustering based algorithm that aligns sequence sets without guide-trees. Experiments on the BAliBASE dataset show that MSARC achieves alignment quality similar to the best progressive methods.Furthermore, MSARC outperforms them on sequence sets whose evolutionary distances are difficult to represent by a phylogenetic tree. These datasets are most exposed to the guide-tree bias of alignments.AvailabilityMSARC is available at http://bioputer.mimuw.edu.pl/msarc
Dotychczasowe badania sugerują, że w okresie wczesnoszkolnym przy tym samym poziomie umiejętności z zakresu języka ojczystego nauczyciele wyżej oceniają osiągnięcia szkolne dziewczynek niż chłopców. Celem artykułu jest weryfikacja tej hipotezy. Wykorzystano (a) oceny osiągnięć uczniów wystawione przez nauczycieli poza procesem nauczania (dla celów badawczych) i (b) wyniki standaryzowanych testów osiągnięć z języka polskiego. Analizy uwzględniające potencjalną stronniczość pozycji testowych ze względu na płeć ucznia przeprowadzono metodą modelowania wielu wskaźników i wielu przyczyn (MIMIC). Wykorzystano dane pochodzące z ogólnopolskiego badania 4144 uczniów trzeciej klasy szkoły podstawowej. Zgodnie z przewidywaniami, nauczyciele wyżej oceniali osiągnięcia dziewczynek niż chłopców, ale ta różnica zanikła, gdy do modelu analizy włączono wyniki standaryzowanych testów. Okazuje się więc, że nauczyciele potrafią bezstronnie ocenić osiągnięcia szkolne z języka polskiego dziewczynek i chłopców.Słowa kluczowe: język polski; model wielu wskaźników i wielu przyczyn; oceny szkolne; płeć; stronniczość; zróżnicowane funkcjonowanie pozycji testowej.
Background: Progressive methods offer efficient and reasonably good solutions to the multiple sequence alignment problem. However, resulting alignments are biased by guide-trees, especially for relatively distant sequences. Results: We propose MSARC, a new graph-clustering based algorithm that aligns sequence sets without guide-trees. Experiments on the BAliBASE dataset show that MSARC achieves alignment quality similar to the best progressive methods. Furthermore, MSARC outperforms them on sequence sets whose evolutionary distances are difficult to represent by a phylogenetic tree. These datasets are most exposed to the guide-tree bias of alignments. Availability: MSARC is available at
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.