Enhancing English learning materials with data-driven learning: a mixed-methods study of task motivation

Zare, Javad; Delavar, Khadijeh Aqajani

doi:10.1080/01434632.2022.2134881

Cited by 4 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considerable research underpins the significant influence of task motivation on learner performance, engagement, and learning outcomes [31][32][33][34]. Features related to task content, topic interest, personalized content, and relevance to life experiences have all been found to boost task motivation and, consequently, learner engagement [29,35].…”

Section: Task Motivation and Underlying Theoriesmentioning

confidence: 99%

Higher Education Students’ Task Motivation in the Generative Artificial Intelligence Context: The Case of ChatGPT

Hmoud,

Swaity,

Hamad

et al. 2024

Information

View full text Add to dashboard Cite

Artificial intelligence has been attracting the attention of educational researchers recently, especially ChatGPT as a generative artificial intelligence tool. The context of generative artificial intelligence could impact different aspects of students’ learning, such as the motivational aspect. The present research intended to investigate the characteristics of students’ task motivation in the artificial intelligence context, specifically in the ChatGPT context. The researchers interviewed 15 students about their experiences with ChatGPT to collect data. The researchers used inductive and deductive content analysis to investigate students’ motivation when learning with ChatGPT. To arrive at the categories and sub-categories of students’ motivation, the researchers used the MAXQDA 2022. Five main categories emerged: task enjoyment, reported effort, result assessment, perceived relevance, and interaction. Each category comprised at least two sub-categories, and each sub-category was further organized into codes. The results indicated more positive characteristics of motivation than negative ones. The previous results could be due to the conversational or social aspect of the chatbot, enabling relationships with humans and enabling the maintenance of good quality conversations with them. We conclude that a generative AI could be utilized in educational settings to promote students’ motivation to learn and thus raise their learning achievement.

show abstract

Section: Task Motivation and Underlying Theoriesmentioning

confidence: 99%

Higher Education Students’ Task Motivation in the Generative Artificial Intelligence Context: The Case of ChatGPT

Hmoud,

Swaity,

Hamad

et al. 2024

Information

View full text Add to dashboard Cite

show abstract

“…Data-driven learning is usually utilized in foreign language learning to help students to learn from data, taking the role of the researcher in guided discovery tasks. Learning from authentic data promote self-regulated learning raises awareness skills and promotes learners' agency, autonomy and motivation for learning [86]. Learning environments should provide opportunities for data searching with the support of digital technologies.…”

Section: Data Research and Collectionmentioning

confidence: 99%

Meta-Learning: A Nine-Layer Model Based on Metacognition and Smart Technologies

2023

View full text Add to dashboard Cite

The international organizations of education have already pointed out that the way students learn, what they learn, and the skills needed, will be radically transformed in the coming years. Smart technologies are ready to come into play, changing the conditions of learning, providing opportunities for transformative learning experiences, and promising more conscious, self-directed and self-motivated learning. Meta-learning refers to a set of mental meta-processes by which learners consciously create and manage personal models of learning. Meta-learning entails a cluster of meta-skills that are progressively and hierarchically transformed, ensuring the transition to the highest levels of understanding termed meta-comprehension. The current article aims to investigate the concept of meta-learning and describe the meta-levels of learning through the lens of metacognition. In addition, the potential of smart technologies to provide fertile ground for the implementation of meta-learning training strategies is examined. The results of this article provide a new meta-learning theoretical framework supported by smart devices capable of supporting future meta-learners or, more accurately, meta-thinkers, to transcend the usual states of knowing and move to the next meta-levels of human intelligence.

show abstract

“…In recent years, integrating data-driven approaches, gamification, and problem-based learning gained substantial attention as promising strategies to enhance engineering students' engagement, motivation, and knowledge acquisition [2][3][4]. Gamification is defined as the application of game elements and mechanics outside their primary context [5] and has shown enormous potential in transforming traditional educational approaches.…”

Section: Introductionmentioning

confidence: 99%

Using Problem-Based Learning and Gamification as a Catalyst for Student Engagement in Data-Driven Engineering Education: A Report

Čubela,

Rossner,

Neis

2023

Education Sciences

View full text Add to dashboard Cite

This report explores the integration of problem-based learning, gamification, and data-driven approaches in engineering education. With a focus on the course “GeoGovernment 1”, this framework aimed to engage students actively and foster self-directed learning. By tackling real-world issues like automated teller machine (ATM) burglaries in Rhineland-Palatinate (Germany), students gained experience in data analyses and geoinformatics technology. This approach not only motivated students but also enhanced their prospects in science, technology, engineering, and mathematics (STEM) fields, equipping them with skills necessary for their future careers. The course structure emphasized student-centered learning, with educators playing facilitative roles to provide guidance. In summary, the combination of problem-based learning, gamification, and data-driven approaches offers a promising solution to address the challenges faced by STEM education, providing an engaging and effective learning experience for students, and ultimately preparing them for the demands of the ever-evolving professional landscape.

show abstract

Enhancing English learning materials with data-driven learning: a mixed-methods study of task motivation

Cited by 4 publications

References 47 publications

Higher Education Students’ Task Motivation in the Generative Artificial Intelligence Context: The Case of ChatGPT

Higher Education Students’ Task Motivation in the Generative Artificial Intelligence Context: The Case of ChatGPT

Meta-Learning: A Nine-Layer Model Based on Metacognition and Smart Technologies

Using Problem-Based Learning and Gamification as a Catalyst for Student Engagement in Data-Driven Engineering Education: A Report

Contact Info

Product

Resources

About