Learners' Autonomous Learning Behavior in Distance Reading Based on Big Data

Shu-liang, Huan; Yang, Cuibi

doi:10.3991/ijet.v17i09.31373

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…Another one is the completion strategy, which requires making inferences or associations to understand information. Organizational strategy allows the learner to systematically classify, organize, and summarize information (Huam & Yang, 2022). Strategic review helps to consolidate acquired knowledge.…”

Section: Introductionmentioning

confidence: 99%

Flipped Classroom Based on Blum’s Modeling and its Impact on Autonomous Learning Experienced by Mathematics Students from a University in Lima, Peru

Carrillo-Yalán,

Vargas-Trujillo,

García-Cuéllar

et al. 2024

Acad. J. Interdiscip. Stud.

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Introduction. The flipped classroom method represents an avant-garde approach to the learning process, in which students acquire the fundamental concepts of a subject outside the traditional classroom environment. In this work, Blum's modeling was used, based on discovery learning, where students are initially faced with problems or situations that require a solution, encouraging the search for information and the application of various strategies to solve them. This combination enhances learning autonomy by offering greater opportunities for practice and feedback. Objective. The research aims to determine the impact of the flipped classroom method as a tool to promote autonomous learning. Method. A mixed methodology was used, integrating elements of quasi-experimental and phenomenological design, with an explanatory scope. Results. The study revealed a 65% effectiveness in the development of autonomous learning among students who participated in the flipped classroom implementation. In addition, participants reported an increase in motivation, ability to set goals, identification of challenges and strengths, availability of resources and an environment conducive to learning, as well as selection of effective study strategies and increased reflection on the effects of their learning. In conclusion, the findings support the usefulness of flipped classroom as a valuable tool for fostering autonomous learning in university students. Received: 3 February 2024 / Accepted: 26 April 2024 / Published: 5 May 2024

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Section: Introductionmentioning

confidence: 99%

Flipped Classroom Based on Blum’s Modeling and its Impact on Autonomous Learning Experienced by Mathematics Students from a University in Lima, Peru

Carrillo-Yalán,

Vargas-Trujillo,

García-Cuéllar

et al. 2024

Acad. J. Interdiscip. Stud.

View full text Add to dashboard Cite

show abstract

“…These complexities have necessitated the reclassification of these databases as big data [4][5][6]. Faced with this paradigm shift, educational institutions are now seeking advanced analytical tools capable of deciphering both student and institutional performances [7]. Data centers in educational settings often exhibit big data characteristics and apply specific data mining methods to extract hidden insights.…”

Section: Introductionmentioning

confidence: 99%

Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions

Baniata,

Kang,

Alsharaiah

et al. 2024

Applied Sciences

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Educational institutions are increasingly focused on supporting students who may be facing academic challenges, aiming to enhance their educational outcomes through targeted interventions. Within this framework, leveraging advanced deep learning techniques to develop recommendation systems becomes essential. These systems are designed to identify students at risk of underperforming by analyzing patterns in their historical academic data, thereby facilitating personalized support strategies. This research introduces an innovative deep learning model tailored for pinpointing students in need of academic assistance. Utilizing a Gated Recurrent Neural Network (GRU) architecture, the model is rich with features such as a dense layer, max-pooling layer, and the ADAM optimization method used to optimize performance. The effectiveness of this model was tested using a comprehensive dataset containing 15,165 records of student assessments collected across several academic institutions. A comparative analysis with existing educational recommendation models, like Recurrent Neural Network (RNN), AdaBoost, and Artificial Immune Recognition System v2, highlights the superior accuracy of the proposed GRU model, which achieved an impressive overall accuracy of 99.70%. This breakthrough underscores the model’s potential in aiding educational institutions to proactively support students, thereby mitigating the risks of underachievement and dropout.

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“…The analysis of students' learning behavior characteristics is an important means for educators to better understand students and improve the quality and effectiveness of teaching [1][2][3]. Teachers can analyze students' learning characteristics by observing their classroom participation, questioning, group discussions, and other behaviors during classroom teaching.…”

Section: Introductionmentioning

confidence: 99%

Student Online Learning Behavior Characteristics Based on Multidimensional Cognitive Model

Zhang¹

2023

Int. J. Emerg. Technol. Learn.

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Analysis of student learning behavior characteristics is an important means for educators to better understand students and improve the quality and effectiveness of teaching in the field of education. It is necessary to refer to students' cognitive levels for analysis of student learning behavior characteristics. However, existing algorithms only focus on the overall performance and grades of students, ignoring the individual differences in learning cognitive levels among students, which affects the accuracy of the analysis results. Therefore, this paper conducts research on student online learning behavior characteristics based on a multidimensional cognitive model. Firstly, a multidimensional and multilevel model for evaluating students' cognitive levels is constructed, and the process of evaluating students' cognitive levels is sustainable and can be adjusted in real-time as students' cognitive levels change. By considering the differences in evaluation levels and students' cognitive levels, targeted observation and extraction of students' online learning behavior characteristics can be achieved. A new model based on variational autoencoder neural network is proposed to perform decoupled representation of students' implicit preferences. By using a regularization term based on maximum mean difference, the model can learn independent hidden vectors sensitive to dynamic and static factors from students' online learning behavior history data and multidimensional cognitive evaluation history data. The experimental results verify the effectiveness of the constructed model.

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Learners' Autonomous Learning Behavior in Distance Reading Based on Big Data

Cited by 6 publications

References 27 publications

Flipped Classroom Based on Blum’s Modeling and its Impact on Autonomous Learning Experienced by Mathematics Students from a University in Lima, Peru

Flipped Classroom Based on Blum’s Modeling and its Impact on Autonomous Learning Experienced by Mathematics Students from a University in Lima, Peru

Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions

Student Online Learning Behavior Characteristics Based on Multidimensional Cognitive Model

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