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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA21-08218S" target="_blank" >GA21-08218S: Využití multimodální analytiky učení pro studium procesů seberegulovaného učení v systémech pro řízení výuky</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

ICERI2021 Proceedings

ISBN

9788409345496

ISSN

2340-1095

e-ISSN

—

Počet stran výsledku

9

Strana od-do

6798-6806

Název nakladatele

IATED

Místo vydání

Seville, Spain

Místo konání akce

Online Conference

Datum konání akce

1. 1. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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