Cognitive processes in university learning: A developmental framework using structural equation modelling

Title
Cognitive processes in university learning: A developmental framework using structural equation modelling
Publication Date
2011
Author(s)
Phan, Huy
( author )
OrcID: https://orcid.org/0000-0002-3066-4647
Email: hphan2@une.edu.au
UNE Id une-id:hphan2
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
John Wiley & Sons Ltd
Place of publication
United Kingdom
DOI
10.1348/2044-8279.002000
UNE publication id
une:10157
Abstract
Background: Both achievement goals and study processing strategies theories have been shown to contribute to the prediction of students' academic performance. Existing research studies (Fenollar, Roman, & Cuestas, 2007; Liem, Lau, & Nie, 2008; Simons, Dewitte, & Lens, 2004) amalgamating these two theoretical orientations in different causal models have reported their associations with other adaptive strategies and motivational constructs - for example, effort expenditure. Despite this recognition, there have been to date very few studies that explored the relations between achievement goals, study processing strategies, effort, and academic performance over time. Aim of study: The primary focus of our study is to explore the relations between the aforementioned theoretical constructs over a 2-year period. Specifically, we tested an empirical model that conceptualized the relations between performance-approach and mastery goals, deep processing strategies, effort, and academic performance across six time points of data collection. Methodology: Two hundred and eighty-one (161 females, 120 males) university students took part in this study. The participants were administered various Likert-scale inventories and the overall course mark and final examination were used as indexes of academic performance. Results: Structural equation modelling indicated a relatively good fit to the a posteriori model and the hypothesized paths were, in part, supported. The major findings included the predictive effects of performance-approach goals at Time 1 on deep processing strategies at Time 2 and mastery goals at Time 3; the predictive effect of mastery goals at Time 3 on effort at Time 4; the predictive effects of deep processing at Time 2 on mastery goals at Time 3 and Time 4. Furthermore, the placement of deep processing and effort in this structural model also accentuated the performance-approach goals - mastery goals - effort - academic performance relation, and the performance-approach goals - deep processing - mastery goals - effort - academic performance relation. Discussion: Our study has important theoretical and practical implications concerning the conceptualization of the performance-approach and mastery goals relationship, and the use of goal structure and adaptive strategies (e.g., deep processing) to enhance academic learning.
Link
Citation
British Journal of Educational Psychology, 81(3), p. 509-530
ISSN
2044-8279
0007-0998
Start page
509
End page
530

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