Two studies were conducted with distinct samples to investigate how motivational beliefs cohere and function together (i.e., motivational profiles) and predict academic adjustment. Integrating across motivational theories, participants ( = 160 upper elementary students; = 325 college students) reported on multiple types of motivation (achievement goals, task value, perceived competence) for schooling more generally (Study 1) and in science (Study 2). Three profiles characterized by and motivation were identified in both studies. Profiles characterized by motivation (Study 1) and (Study 2) were also present. Across studies, the and profiles were associated with the highest academic engagement and achievement. Findings highlight the benefit of integrating across motivational theories when creating motivational profiles, provide initial evidence regarding similarities and differences in integrative motivational profiles across distinct samples, and identify which motivational combinations are associated with beneficial academic outcomes in two educational contexts.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6220891PMC
http://dx.doi.org/10.1037/edu0000245DOI Listing

Publication Analysis

Top Keywords

motivational profiles
16
motivational
8
integrative motivational
8
upper elementary
8
college students
8
distinct samples
8
integrating motivational
8
motivational theories
8
profiles characterized
8
characterized motivation
8

Similar Publications

Subspecies phylogeny in the human gut revealed by co-evolutionary constraints across the bacterial kingdom.

Cell Syst

January 2025

Duchossois Family Institute, University of Chicago, Chicago, IL 60637, USA; Department of Pathology, University of Chicago, Chicago, IL 60637, USA; Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL 60637, USA. Electronic address:

The human gut microbiome contains many bacterial strains of the same species ("strain-level variants") that shape microbiome function. The tremendous scale and molecular resolution at which microbial communities are being interrogated motivates addressing how to describe strain-level variants. We introduce the "Spectral Tree"-an inferred tree of relatedness built from patterns of co-evolutionary constraint between greater than 7,000 diverse bacteria.

View Article and Find Full Text PDF

Integrating single-cell multimodal epigenomic data using 1D-convolutional neural networks.

Bioinformatics

January 2025

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States.

Motivation: Recent experimental developments enable single-cell multimodal epigenomic profiling, which measures multiple histone modifications and chromatin accessibility within the same cell. Such parallel measurements provide exciting new opportunities to investigate how epigenomic modalities vary together across cell types and states. A pivotal step in using this type of data is integrating the epigenomic modalities to learn a unified representation of each cell, but existing approaches are not designed to model the unique nature of this data type.

View Article and Find Full Text PDF

Background And Aims: The widespread popularity of video games reflects their appeal to meet fundamental needs. This study aims to investigate the psychological factors of gaming use, identifying profiles ranging from healthy to gaming disorder.

Methods: In this cross-sectional study, 5,222 participants were surveyed.

View Article and Find Full Text PDF

Objectives: Grounded in the Health Empowerment Model, which posits that health literacy and patient empowerment are intertwined yet distinct constructs, this study investigates how the interplay of these factors influences attitudes toward seeking professional psychological help in members of online communities for mental health (OCMHs). This while acknowledging the multidimensionality of patient empowerment, encompassing meaningfulness, competence, self-determination, and impact.

Design And Methods: A cluster analysis of data gathered from 269 members of Italian-speaking OCMHs on Facebook has been performed.

View Article and Find Full Text PDF

Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phenotypes at the level of individual cells. The analysis of the resulting data poses significant challenges, and proper statistical methods are required to analyze and extract information from scRNA-Seq datasets. Sample classification based on gene expression data has proven effective and valuable for precision medicine applications.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!