Influence of Online Learning Environment and Student Engagement on International Students’ Sustainable Chinese Learning

Fan, Jingwei; Tian, Ming

doi:10.3390/su141711106

Cited by 7 publications

(3 citation statements)

References 70 publications

(125 reference statements)

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“…The purpose of this course is to enable international students to overcome language barriers and smoothly transition into university study after completing their preparatory studies. It aims to facilitate the transition from language studying to professional studies for these students [ 21 ]. Science and Technology Chinese is a specialized Chinese language teaching that is built upon the foundation of basic Chinese language teaching and is categorized as the second stage of Chinese language teaching [ 22 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysing the influencing factors of on-line studying engagement of preparatory international students: A case study of the science and technology Chinese course

Yang,

Tang,

et al. 2024

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Analysing the influencing factors of on-line studying engagement of preparatory international students: A case study of the science and technology Chinese course

Yang,

Tang,

et al. 2024

Heliyon

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“…This is because the online learning environment gives learners independence, which reduces the impact on engagement. Furthermore, Fan and Tian (2022) emphasised that instructors' dedication and the design of the course both influence participants' impressions of their online learning experience. Adequate technical support, web-based materials, and well-organized online courses are critical in assisting students' learning.…”

Section: Online Learning Environment and How It Influences Learnersmentioning

confidence: 99%

The Influence of Online Environment on Learners’ Behaviour and Learning Environment

Iliyas¹,

Jenal²,

Taib³

et al. 2023

IJARBSS

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Online learning has become prevalent especially during this post COVID-19 pandemic. Therefore, it is important to understand the online learning motivation in order to help online students learn better. This study aims to investigate how learners' behaviour influences learners' online motivation, how learners' environment influences learners' online motivation, how social engagement influences changes in learners' behaviour, how instructor support influences changes in learners' behaviour, and the relationship between instructor support and learners' behaviour, learners' environment, and social engagement. A purposive sample of 100 participants responded to the survey. The instrument used is a 5 Likert-scale survey and is rooted from Thorndike (1905) to merge with Pintrich & De Groot's (1990) motivational construct to reveal the variables in table 1 below. The survey has 5 sections. Section A has 5 items on demographic profile. Section B has 12 items on Behaviour. Section C has 14 items on Environment. Section D has 5 items on Changes in Behaviour while section E has 7 items on Changes in Environment. This paper has presented and highlighted the crucial elements of online motivation from the perspective of the law of effect by Thorndike (1905). The present findings also provide important pedagogical implications for enhancing students' online learning motivation.

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“…The widespread tools to measure SE are divided into three categories: (1) self-reports, (2) observational checklists, and (3) automated measurements. Self-reports are questionnaires that report the level of activeness, excitement, or boredom of the students during learning sessions, but they can be costly or biased tools to detect SE [5][6][7]. The observational checklist measures SE while relying on the questionnaires filled in by external instructors or observers.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Student Engagement State in a Classroom Environment Using Deep and Efficient Transfer Learning Algorithm

et al. 2023

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A student’s engagement in a real classroom environment usually varies with respect to time. Moreover, both genders may also engage differently during lecture procession. Previous research measures students’ engagement either from the assessment outcome or by observing their gestures in online or real but controlled classroom environments with limited students. However, most works either manually assess the engagement level in online class environments or use limited features for automatic computation. Moreover, the demographic impact on students’ engagement in the real classroom environment is limited and needs further exploration. This work is intended to compute student engagement in a real but least controlled classroom environment with 45 students. More precisely, the main contributions of this work are twofold. First, we proposed an efficient transfer-learning-based VGG16 model with extended layer, and fine-tuned hyperparameters to compute the students’ engagement level in a real classroom environment. Overall, 90% accuracy and 0.5 N seconds computational time were achieved in terms of computation for engaged and non-engaged students. Subsequently, we incorporated inferential statistics to measure the impact of time while performing 14 experiments. We performed six experiments for gender impact on students’ engagement. Overall, inferential analysis reveals the positive impact of time and gender on students’ engagement levels in a real classroom environment. The comparisons were also performed by various transfer learning algorithms. The proposed work may help to improve the quality of educational content delivery and decision making for educational institutions.

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Influence of Online Learning Environment and Student Engagement on International Students’ Sustainable Chinese Learning

Cited by 7 publications

References 70 publications

Analysing the influencing factors of on-line studying engagement of preparatory international students: A case study of the science and technology Chinese course

Analysing the influencing factors of on-line studying engagement of preparatory international students: A case study of the science and technology Chinese course

The Influence of Online Environment on Learners’ Behaviour and Learning Environment

Recognition of Student Engagement State in a Classroom Environment Using Deep and Efficient Transfer Learning Algorithm

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