ChatGPT and learning outcomes in tourism education: The role of digital literacy and individualized learning

Dalgıç, Ali; Yaşar, Emre; Demir, Mahmut

doi:10.1016/j.jhlste.2024.100481

Cited by 21 publications

(1 citation statement)

References 72 publications

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“…The result shows that motivation (intrinsic goal orientation, extrinsic goal orientation, task value, control of learning beliefs) and learning strategies (elaboration, organization, meta-cognitive self-regulation, time and study environment management, effort regulation, peer learning, critical thinking, and help-seeking) jointly predicts postgraduate students' academic achievement significantly. This finding aligns with previous research (Ahmed et al, 2016;Crede & Philip 2012;Dalgıç et al, 2024;Demir & Demir, 2014;Demir et al, 2023;Lynch 2010;Mirzaei-Alavijeh et al, 2019;Muwonge et al, 2019;Nabizadeh et al, 2019;Neroni et al, 2019;Pokay & Blumenfeld, 1990;Obrent 2012;Schneider & Preckel 2017;Ulstad et al, 2016;Wild & Neef,2023;Yusof et al, 2021) which showed that motivation and learning strategies significantly relates and predicts academic achievement. This is also somewhat at variance with the study of Creede & Philip (2011), where their results showed that strong relationships were found between individual grades (academic achievements) and effort regulation, time and study environment management, and metacognitive self-regulation while the remaining learning strategies proved to be unrelated to academic performance.…”

Section: Discussionsupporting

confidence: 91%

Predictive analysis of motivation and learning strategies on academic achievement of postgraduate students

Eteng-uket,

Effiom

2024

International Journal of Social Sciences and Education Research

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In today's rapidly evolving educational landscape, attaining excellent academic achievement and outcome is often characterized by a multifaceted interaction, such as the intricate prediction and relationship between motivation, learning strategies, and academic outcomes. This thus prompted the study, which sought to carry out a predictive analysis of motivation and learning strategies on the academic achievement of postgraduate students. It adopted the correlational research design; a sample of 333 was drawn using a multi-stage sampling procedure. The Motivated Strategies for Learning Questionnaire was the instrument used. Validity and reliability measures were established. The data were analyzed using simple and multiple regression. The results revealed that motivational and learning strategies significantly predict the academic achievement of postgraduate students, both independently and jointly. Considering the findings, it was suggested, among others, that educators should promote effective cognitive strategies to improve the performance of postgraduate students.

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