Patterns of academic success : data-driven typology of university students' approaches to learning, motivation, and academic achievement

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14210%2F21%3A00119478" target="_blank" >RIV/00216224:14210/21:00119478 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.21125/iceri.2021.1538" target="_blank" >http://dx.doi.org/10.21125/iceri.2021.1538</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21125/iceri.2021.1538" target="_blank" >10.21125/iceri.2021.1538</a>

Result language

angličtina

Original language name

Patterns of academic success : data-driven typology of university students' approaches to learning, motivation, and academic achievement

Original language description

The paper aims to answer the question of how different combinations of approaches to learning can serve as an explanation of university students' academic achievement. We will answer the following questions: What meaningful clusters of students can be identified based on approaches to learning? Do identified clusters differ in the levels of academic motivation and academic achievement? We appointed multimodal data analysis combining data from an online survey and a student information system to answer our questions. We focused on first-year students (n=581) from Masaryk University (Brno, Czech Republic) in the online survey. To measure academic motivation, we used the Academic Motivation Scale (AMS). This scale measures extrinsic motivation (EM), intrinsic motivation (IM), and amotivation (AM). To measure approaches to learning, we used the ASSIST scale, which differentiates between three approaches to learning: deep approach, surface approach, and strategic approach. At the same time, we extracted several indicators of academic achievement from the student information system of Masaryk University (entrance exam score, grade point average, average number of credits per semester). During data analysis, we employed confirmatory factor analysis followed by cluster analysis. Based on the performed analysis, we identified five clusters of university students: unmotivated passers, surface fulfillers, focused workers, unfocused thinkers and stimulated learners. Despite their high entrance exam results, unmotivated passers have relatively low GPA, the highest numbers of unaccomplished credits, and the lowest numbers of credits obtained per semester. They appoint mainly surface approach to learning, and they show a high level of amotivation. Unmotivated passers are probably unsatisfied with their studies and are most likely to drop out. Surface fulfillers also have relatively low GPA, but they differ in all other aspects. They have low results in the entrance exam, they have an ambivalent (so-called dissonant) approach to studying, and they have higher levels of external motivation. Forced fulfillers might be passing due to their ability to use the right approach to learning for a particular exam. Another cluster, the so-called focused workers, has many similarities to the previous group. Still, their dominant approach to studying is strategic, which relates to second-best results, despite low entrance exam results and high extrinsic motivation. On the other hand, the "unfocused thinkers" cluster consists of students with the best entrance exam results and a deep approach to learning. Despite these characteristics, they have low achievement indicators, which can be explained by the fact that they are studying for the learning itself and do not think much about external study incentives. The best-achieving cluster is the "stimulated learners" cluster. These students have high levels of deep and strategic approaches to learning, and they show the lowest levels of amotivation. These might be the antecedents of their academic success despite their average entrance exam results. In the paper, we will further discuss the potential of the presented typology for university student support.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

50301 - Education, general; including training, pedagogy, didactics [and education systems]

Project

<a href="/en/project/GA21-08218S" target="_blank" >GA21-08218S: Multimodal learning analytics to study self-regulated learning processes within learning management systems</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

ICERI2021 Proceedings

ISBN

9788409345496

ISSN

2340-1095

e-ISSN

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Number of pages

9

Pages from-to

6798-6806

Publisher name

IATED

Place of publication

Seville, Spain

Event location

Online Conference

Event date

Jan 1, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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