Literature DB >> 29861029

Everyday discrimination, negative emotions, and academic achievement in Filipino secondary school students: Cross-sectional and cross-lagged panel investigations.

Jesus Alfonso D Datu1.   

Abstract

Different forms of overt discrimination have been consistently linked to maladaptive psychological, physical health, and educational outcomes. However, limited research has been carried out to assess the link of subtle forms of discrimination like everyday discrimination on academic functioning in the school context. The current study addressed this research gap through examining the association of everyday discrimination with negative emotions and academic achievement among Filipino high school students. A cross-sectional study (Study 1) showed that everyday discrimination was positively associated with negative emotions and negatively linked to perceived academic achievement. Furthermore, everyday discrimination had indirect effects on academic achievement through the intermediate variable negative emotions. Then, a two-wave cross-lagged panel investigation (Study 2) demonstrated that Time 1 everyday discrimination was linked to higher Time 2 negative emotions. Reciprocal associations were also found among the constructs because Time 1 academic achievement was linked to lower levels Time 2 negative emotions and Time 2 everyday discrimination. The theoretical and practical implications of the research are elucidated.
Copyright © 2018 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Keywords:  Academic achievement; Everyday discrimination; Negative emotions

Mesh:

Year:  2018        PMID: 29861029     DOI: 10.1016/j.jsp.2018.04.001

Source DB:  PubMed          Journal:  J Sch Psychol        ISSN: 0022-4405


  2 in total

Review 1.  The Impact of EFL Students' Emotioncy Level on Their Motivation and Academic Achievement: A Theoretical Conceptual Analysis.

Authors:  Xuena Zhang
Journal:  Front Psychol       Date:  2021-12-24

2.  Academic Emotion Classification and Recognition Method for Large-scale Online Learning Environment-Based on A-CNN and LSTM-ATT Deep Learning Pipeline Method.

Authors:  Xiang Feng; Yaojia Wei; Xianglin Pan; Longhui Qiu; Yongmei Ma
Journal:  Int J Environ Res Public Health       Date:  2020-03-16       Impact factor: 3.390

  2 in total

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