Constructing personal learning environments through ICT-mediated foreign language instruction

Kupchyk, L. Y.; Літвінчук, А. Т.

doi:10.1088/1742-6596/1840/1/012045

Cited by 16 publications

(12 citation statements)

References 18 publications

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“…Tips on how to prepare and conduct a webinar for your students, see [18]. Cloud computing tools allow a student to form a personal learning environment (PLE), a term that appeared several years ago in Western literature related to the practical application of e-learning 2.0 [21,31].…”

Section: Social Servicesmentioning

confidence: 99%

Distance course examination

Kukharenko¹,

Shunevych²,

Kravtsov³

2022

Educ. Technol. Q.

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The article deals with the theoretical foundations of carrying out distance course (DC) examination with the aim of creating the program for expert training. An expert is defined as a specialist who has experience in compiling distance courses, organization of distance learning as well as has and uses content tutor skills.The article also describes the basic skills that an expert should have as well as typical mistakes of the developers of distance courses are presented. On the basis of the research a DC ``Expert examination of distance course'' was compiled for experienced developers of distance courses and administrators of distance learning systems at different institutions. During the training, the learners analyzed the purpose of Bloom's taxonomy and its accessibility, the complexity of the text and its design, the tasks and learning activities, the quality of tests and the evaluation system, etc. The result of the course training is learner's report on the distance course examination and analysis of this work. 47 lecturers of Ukrainian educational institutions were trained and only 12 of them successfully completed the training.

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Section: Social Servicesmentioning

confidence: 99%

Distance course examination

Kukharenko¹,

Shunevych²,

Kravtsov³

2022

Educ. Technol. Q.

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“…The article "Constructing personal learning environments through ICT-mediated foreign language instruction" [22] of Larysa Kupchyk et al deals with the concept of student-centred Personal Learning Environment (PLE) in the context of higher education, which is used as a means of transforming foreign language learning and teaching practices. It aims to reveal the opportunities for creating PLEs through incorporating ICT in the process of foreign language education.…”

Section: Educational Technologymentioning

confidence: 99%

XII International Conference on Mathematics, Science and Technology Education: conference report

2021

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This article represents the proceedings of the XII International Conference on Mathematics, Science and Technology Education (ICon-MaSTEd 2020) held at the Kryvyi Rih State Pedagogical University, Ukraine, 15–17 October 2020. Background information and the organizational structure of the meeting, a summary of the papers, and acknowledgements of the contributions of the many people who made the conference a success are presented.

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“…Moreover, the algorithm continuously adapts and refines its recommendations based on user feedback and performance metrics, ensuring a dynamic and effective learning experience. With its ability to cater to the diverse needs of learners, this algorithm heralds a new era of personalized language education, promising increased fluency and confidence in English listening skills [5]. The Personalized Learning Path Recommendation Algorithm for English Listening Learning is a sophisticated system designed to revolutionize the way individuals engage with and master the intricacies of English listening comprehension [6].…”

Section: Introductionmentioning

confidence: 99%

Personalized Learning Path Recommendation Algorithm for English Listening Learning

Xiaomei Li

2024

jes

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A personalized learning path recommendation algorithm for English listening learning leverages data on users' proficiency levels, learning preferences, and past performance to suggest tailored learning paths. By incorporating natural language processing (NLP) techniques, the algorithm can analyze audio content, transcripts, and user interactions to assess comprehension and identify areas for improvement. It then recommends a sequence of listening exercises, podcasts, audiobooks, or other resources matched to the user's skill level and interests. This paper introduces the Ranked Path Recommendation (RPR) algorithm, designed to facilitate personalized English listening learning. Leveraging data analytics and machine learning techniques, the RPR algorithm aims to provide tailored recommendations of listening materials based on individual learners' preferences, proficiency levels, and learning objectives. Through a series of experiments and analyses, the effectiveness of the algorithm is evaluated, considering factors such as recommendation accuracy, learner satisfaction, and adaptability. Results demonstrate the algorithm's ability to curate diverse and relevant listening materials, enhancing learner engagement and comprehension. However, challenges such as algorithmic biases and the need for ongoing refinement are acknowledged. Ultimately, the RPR algorithm represents a promising approach to adaptive learning in language education, contributing to the advancement of personalized and effective language learning experiences. Results demonstrate that the RPR algorithm achieved recommendation accuracy ranging from 85% to 92% across ten different scenarios, with corresponding learner satisfaction ratings ranging from 6.9 to 8.8 on a scale of 1 to 10. Learner feedback indicates that recommended materials were perceived as relevant, engaging, and diverse, contributing to enhanced comprehension and motivation.

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Constructing personal learning environments through ICT-mediated foreign language instruction

Cited by 16 publications

References 18 publications

Distance course examination

Distance course examination

XII International Conference on Mathematics, Science and Technology Education: conference report

Personalized Learning Path Recommendation Algorithm for English Listening Learning

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