Revisiting the Self-Determination Theory-Motivating the Unmotivated

Luria, Ela

doi:10.7459/ept/44.2.02

Cited by 2 publications

(2 citation statements)

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“…From the figure, it could also be seen that all motivation types existed when the students were working with the AI apps. This is in line with Luria (2022) who explains that students could experience more than one type of motivation when they are engaged in their learning activities.…”

Section: Evidence Of Amotivationsupporting

confidence: 87%

Amotivation in AI injected EFL classrooms: Implications for teachers

Sumakul¹,

Hamied²

2023

Indonesian J. Appl. Linguist.

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Motivation is an essential aspect of students' success in their learning, and an investigation into the factors that could deteriorate their motivation could shed light on that issue. This study investigates amotivation during the application of artificial intelligence technology in EFL classrooms or AI-injected learning. As artificial intelligence is still a relatively new technology, but its application is becoming increasingly more prevalent in language classrooms, this study aims to explore factors that could negatively affect EFL students’ motivation to use technology in their learning. This study included questionnaires and interviews to collect data from 133 EFL students in an Indonesian higher education institution. The students had experience working with AI applications in their learning. The statistical analysis of the questionnaire data suggested that, although not dominant, amotivation was evident among the students. More than 25% of the students experienced amotivation while learning using the AI apps. The qualitative analysis of the interview data revealed three factors that could give rise to amotivation among the students when working with the AI apps: intelligence, user interface, and lesson design. Intelligence and user interface were internal to the AI apps, while lesson design was associated with the teachers' pedagogical competence in preparing the lessons for their students. This study suggests that app design and lesson design are two motivational factors that could affect students’ motivation in AI-injected learning.

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Section: Evidence Of Amotivationsupporting

confidence: 87%

Amotivation in AI injected EFL classrooms: Implications for teachers

Sumakul¹,

Hamied²

2023

Indonesian J. Appl. Linguist.

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“…Another way is through competence, that refers to the efficacy sensation and the ability on task solving. This process occurs when students experience a tangible progress and achieve their academic goals, their self-confidence and motivation increase (Conner et al, 2022;Frikha et al, 2022;Luria, 2022;Qingyan & Ahmad, 2023). The academic engagement is reinforced when learners feel competent in their ability to face challenges and overcome obstacles.…”

Section: Literature Reviewmentioning

confidence: 99%

Academic Engagement: A Bibliometric Analysis in Scientific Production

Flores,

Ramos,

Mendoza

et al. 2023

J. Professional Business Review

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Purpose: To conduct a bibliometric analysis of the existing literature on academic engagement in order to quantify academic output, identify influential authors and journals, and assess the temporal evolution of research, providing a comprehensive bibliometric perspective with guidance for future studies in the academic field. Theoretical framework: Through a systematic search in academic databases, relevant studies about student commitment and motivation in educational contexts were identified. Design/methodology/approach: The methodology has a descriptive and exploratory approach, 816 articles were reviewed. By analyzing bibliometric indicators, it was revealed a constant growth in research in this field over the last decades, proving a growing interest in understanding and enhancing student participation in their learning process. Findings: The study identified an evolution on research themes, going from individual factors such as motivation to broader contextual issues, such as the impact of technology on the engagement and educational quality. The influence of key authors and institutions in shaping the field was highlighted, as well as a multidisciplinary approach encompassing psychology, education, medicine, and educational technology. Research, Practical & Social implications: This bibliometric study offers a panoramic and updated vision of academic engagement research. Originality/value: Its findings offer a solid foundation to future investigations and enrich the comprehension of how student participation and motivation contributes to a more meaningful learning and the improvement of educational systems.

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Revisiting the Self-Determination Theory-Motivating the Unmotivated

Cited by 2 publications

References 0 publications

Amotivation in AI injected EFL classrooms: Implications for teachers

Amotivation in AI injected EFL classrooms: Implications for teachers

Academic Engagement: A Bibliometric Analysis in Scientific Production

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