Understanding, Investigating, and promoting deep learning in language education: A survey on chinese college students' deep learning in the online EFL teaching context

Jiang, Ruihong

doi:10.3389/fpsyg.2022.955565

Cited by 9 publications

(6 citation statements)

References 51 publications

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“…Some modifications to evaluation methods have also been commonly shown to maximize deep learning; this includes incorporating a mix of higher-and lower-order questions and using formative rather than summative assessments (Al-Kadri et al, 2012;Agarwal, 2019). Further, students have shown greater interest in interactive learning environments and classes where interest-to-effort ratios are balanced (Jiang, 2022;Smarandache et al, 2022). However, to be proven effective, the instructor's modifications should overpower any effect that the students' personal Overall, the findings of this study are especially important in the wake of the coronavirus disease 2019 (COVID-19) pandemic, which spurred rapid evolution to the educational environments in anatomy that continue to impact teaching today.…”

Section: Discussionmentioning

confidence: 99%

Visuospatial ability and student approach to learning as predictors of academic performance on written versus laboratory‐based assessments in human anatomy

Wang

McWatt

2023

Anatomical Sciences Ed

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As hours devoted to human anatomy curricula fall under threat and curricular delivery methods remain in flux, many new teaching innovations are emerging, which require comprehensive evaluation to ensure evidence‐based teaching is maintained. Although grades are the predominant measure of ‘learning’, alternative metrics can assess more nuanced and meaningful outcomes. Two common predictors of students' three‐dimensional understanding of the body and depth of learning are visuospatial abilities and approaches to learning, respectively. This study evaluated and compared the relative predictive power of these metrics on written and laboratory‐based assessments in a human anatomy course. Deep approaches to learning and visuospatial abilities were expected to positively correlate with overall performance, with visuospatial abilities being the more salient predictor, especially on laboratory‐based assessments. Additionally, visuospatial abilities were expected to positively correlate with deep learning approaches and negatively correlate with surface learning approaches. Multiple linear regression models controlling for covariates found that both visuospatial abilities (p = 0.049; p = 0.014) and deep learning approaches (p = 0.001; p = 0.001) were independent significant predictors of final and laboratory‐based grades, while only deep learning approaches were significantly predictive of written grades (p = 0.007). There was no significant relationship between visuospatial abilities and approaches to learning. Given these findings and the increased reliance on visuospatially demanding digital learning activities in anatomy, both metrics should be considered when evaluating the impact of teaching innovations. Further, educators should design learning resources and environments that train visuospatial abilities and promote deeper learning approaches to maximize students' success.

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Section: Discussionmentioning

confidence: 99%

Visuospatial ability and student approach to learning as predictors of academic performance on written versus laboratory‐based assessments in human anatomy

Wang

McWatt

2023

Anatomical Sciences Ed

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“…Numerous analyses have been undertaken to examine the disparate effects of globalization in higher education between developed and developing nations. Typically, these studies examine the impact of diverse infrastructure, resource, and policy support levels on the extent to which these nations can engage in and derive advantages from global educational networks (Dol, 2022;Jiang, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Analysing the impact of Globalisation in Higher Education on Economic Growth: A comparative analysis of Developed and Developing Countries

Chaudhary,

Kler,

Priyanshu

et al. 2024

Preprint

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The study carries out analysis of higher education sector, in which globalization and economic growth are considered, taking both developing and developed nations as the main comparison object. Dynamics in higher education sector have been altered persistently by the recent pandemic, which has sharply confined students' mobility globally and on the other hand has stimulated the development of different options, which consists of massive open online courses (MOOCs), program mobility, and institutional mobility. In this paper we will try to evaluate effects of such modifications on the economic development of several developing and developed countries. To this end, the existence of a cross-sectional panel data set featured by 30 participating countries taking the period from 2010 to 2022 allows us to explore the centrality of inbound and outbound mobility rates measuring the level of globalization with respect to higher education. Data analysis is conducted by the study using feasible generalized least squares regression model, which is applicable to Cross-section time series data. This minimizes potential problems like heteroskedasticity, autocorrelation, and independence of cross-sectional data. Our findings indicate that the existence of globalization in higher education is a root of the complex impact on economic growth. The research underline the dichotomy of developed and developing countries, which implied the differentiation in economic advantages. While everybody expected that globalization in higher education would help the disadvantageous countries, recent research argued that globalization in high education sector rather benefits to the developed countries. The outcomes of the study indeed highlight that the connection between higher education and economic advancement takes an important place within the roles that higher education play in globalization. Moreover, the policy makers and educational institutions will have a chance of getting the findings at their disposal.

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“…• Sec. I reviews other related works on real datasets, data manipulation [72,79,87], deep models [20,39,80], transfer learning [37,58,94], domain adaptation [89], and OOD generalization [87].…”

Section: Appendix For a New Benchmark: On The Utility Of Synthetic Da...mentioning

confidence: 99%

“…Transfer Learning. There has been a huge literature in the field of transfer learning [37,58,94], where the paradigm of pre-training and then fine-tuning has made outstanding achievements in many deep learning applications. Extensive studies have been done for supervised pre-training [22, 41-43, 88, 90].…”

Section: G More Details Onmentioning

confidence: 99%

A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation

Tang¹,

Jia²

2023

Preprint

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Deep learning in computer vision has achieved great success with the price of large-scale labeled training data. However, exhaustive data annotation is impracticable for each task of all domains of interest, due to high labor costs and unguaranteed labeling accuracy. Besides, the uncontrollable data collection process produces non-IID training and test data, where undesired duplication may exist. All these nuisances may hinder the verification of typical theories and exposure to new findings. To circumvent them, an alternative is to generate synthetic data via 3D rendering with domain randomization. We in this work push forward along this line by doing profound and extensive research on bare supervised learning and downstream domain adaptation. Specifically, under the well-controlled, IID data setting enabled by 3D rendering, we systematically verify the typical, important learning insights, e.g., shortcut learning, and discover the new laws of various data regimes and network architectures in generalization. We further investigate the effect of image formation factors on generalization, e.g., object scale, material texture, illumination, camera viewpoint, and background in a 3D scene. Moreover, we use the simulation-to-reality adaptation as a downstream task for comparing the transferability between synthetic and real data when used for pre-training, which demonstrates that synthetic data pre-training is also promising to improve real test results. Lastly, to promote future research, we develop a new large-scale synthetic-to-real benchmark for image classification, termed S2RDA, which provides more significant challenges for transfer from simulation to reality.

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Understanding, Investigating, and promoting deep learning in language education: A survey on chinese college students' deep learning in the online EFL teaching context

Cited by 9 publications

References 51 publications

Visuospatial ability and student approach to learning as predictors of academic performance on written versus laboratory‐based assessments in human anatomy

Visuospatial ability and student approach to learning as predictors of academic performance on written versus laboratory‐based assessments in human anatomy

Analysing the impact of Globalisation in Higher Education on Economic Growth: A comparative analysis of Developed and Developing Countries

A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation

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