Effects of digital game-based learning on students’ digital etiquette literacy, learning motivations, and engagement

Zheng, Yunxiang; Zhang, Junyi; Li, Yumeng; Wu, Xiaomin; Ding, Ruofei; Luo, Xianfei; Liu, Panpan; Huang, Jingxiu

doi:10.1016/j.heliyon.2023.e23490

Cited by 11 publications

(1 citation statement)

References 73 publications

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“…However, the findings from research on FCs are mixed, with some reporting positive results ranging from small to moderate effects, while others finding no significant difference. Most of these studies focused on STEM subjects, with business being unspecified [5]. In cases where business courses were included, no discernible impact was found.…”

Section: Introductionmentioning

confidence: 99%

Sequence Modeling with Recurrent Neural Networks (RNNs) for Student Learning Behavior Pattern Recognition in a Flipped Classroom

Tang

2024

jes

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The flipped classroom model has become increasingly popular in education, altering the traditional methods of teaching. It is important to understand and acknowledge how students learn within this framework in order to optimize instructional strategies and promote personalized learning. This study investigates the use of Sequence Modeling with Recurrent Neural Networks (RNNs) to identify patterns in student learning behavior within a flipped classroom setting. The proposed deep learning architecture utilizes RNNs to analyze sequential patterns in students' interactions with the flipped classroom materials, while also incorporating attention mechanisms to better detect important patterns and temporal dynamics in the learning process. Multimodal learning techniques are also employed, combining data from various sources to gain a comprehensive understanding of student behavior. Additionally, clustering techniques using autoencoders are explored to group students with similar learning behaviors. Predictive models, such as RNN or LSTM networks, are developed to forecast future learning behaviors and provide insights into potential challenges or successes for individual students. The effectiveness of this framework is evaluated using real-world data from flipped classroom implementations, with performance metrics like recall, precision, and accuracy used to assess the success of the sequence modeling approach in recognizing and predicting student behavior patterns. Overall, the application of deep learning methods, specifically sequence modeling with RNNs, demonstrates potential for improving personalized learning experiences and facilitating proactive interventions to support diverse student needs.

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Section: Introductionmentioning

confidence: 99%

Sequence Modeling with Recurrent Neural Networks (RNNs) for Student Learning Behavior Pattern Recognition in a Flipped Classroom

Tang

2024

jes

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Adoption of Digital Games as Pedagogical Aids for Teachers and Pupils in Primary Schools to Overcome Learning Problems in Arithmetic

Boussaha,

Rahab,

Khelifi Touhami

et al. 2024

Lecture Notes in Computer Science

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A Conception of an Instrument Supporting the Design of Educational Video Games for Cultural Heritage

Dankov

2024

IFAC-PapersOnLine

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Effects of digital game-based learning on students’ digital etiquette literacy, learning motivations, and engagement

Cited by 11 publications

References 73 publications

Sequence Modeling with Recurrent Neural Networks (RNNs) for Student Learning Behavior Pattern Recognition in a Flipped Classroom

Sequence Modeling with Recurrent Neural Networks (RNNs) for Student Learning Behavior Pattern Recognition in a Flipped Classroom

Adoption of Digital Games as Pedagogical Aids for Teachers and Pupils in Primary Schools to Overcome Learning Problems in Arithmetic

A Conception of an Instrument Supporting the Design of Educational Video Games for Cultural Heritage

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