Self-Regulated Learning through Self-Concordant Goals, Social Modeling and Sustainability Focus

Research has shown that when students spend time reflecting and autonomously selecting their college goals, they show high intrinsic motivation, low extrinsic motivation and improvement in emotional and social adjustment over the first several months of college. Despite of this, it is estimated that 77% of first- and second-year students are undecided on their major and many remain undecided as they transition into their junior and senior year. Moreover, in education settings the primary emphasis is on academic success, but focusing solely on academic goals is limiting. As learning is inherently social, academic and social domains cannot be separated, while social goals act as motivators for learning behaviors and increase academic and social competence. Therefore, the goal selection process deserves a lot more consideration and attention than is currently given. To this aim, I used Self-Determination Theory (SDT) and its offshoot, The Self-Concordance Model (SCM) as a framework for the contention that academic/career, well-being and social goals should all be relatively aligned towards the betterment of self and the society at large. This case study contributes to the goal selection literature by offering insights on group projects that incorporate SCM and SDT principles under a self-selected common purpose. The interviews conducted at the end of the semester showed the unificatory power of a self-selected common purpose during their group projects, however the leap from group projects centered around grand challenges to similar interdisciplinary majors was not always obvious to the students.

Publication Details

Journal
Digital Scholarship - UNLV (University of Nevada Reno)
Published
2025-02-28
DOI
https://doi.org/10.34917/38330402
Primary Topic
Motivation and Self-Concept in Sports
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Self-Regulated Learning through Self-Concordant Goals, Social Modeling and Sustainability Focus

Digital Scholarship - UNLV (University of Nevada Reno)
Motivation and Self-Concept in Sports
article

Self-Regulated Learning through Self-Concordant Goals, Social Modeling and Sustainability Focus

article en

Abstract

Research has shown that when students spend time reflecting and autonomously selecting their college goals, they show high intrinsic motivation, low extrinsic motivation and improvement in emotional and social adjustment over the first several months of college. Despite of this, it is estimated that 77% of first- and second-year students are undecided on their major and many remain undecided as they transition into their junior and senior year. Moreover, in education settings the primary emphasis is on academic success, but focusing solely on academic goals is limiting. As learning is inherently social, academic and social domains cannot be separated, while social goals act as motivators for learning behaviors and increase academic and social competence. Therefore, the goal selection process deserves a lot more consideration and attention than is currently given. To this aim, I used Self-Determination Theory (SDT) and its offshoot, The Self-Concordance Model (SCM) as a framework for the contention that academic/career, well-being and social goals should all be relatively aligned towards the betterment of self and the society at large. This case study contributes to the goal selection literature by offering insights on group projects that incorporate SCM and SDT principles under a self-selected common purpose. The interviews conducted at the end of the semester showed the unificatory power of a self-selected common purpose during their group projects, however the leap from group projects centered around grand challenges to similar interdisciplinary majors was not always obvious to the students.

Digital Scholarship - UNLV (University of Nevada Reno)
Openalex Percentile: Top 51%
Motivation and Self-Concept in Sports
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.