Language e-Learning and Music Appreciation

Ngiam, Leon Chong Wen; See, Swee Lan

doi:10.1007/978-3-319-42070-7_80

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…Compute the coefficient of variation of the j-th index: (10) Determine the weight of the j-th index: (11) After the weights of the indices are determined by entropy weight method, compute the weighted distance between data samples based on their index weights, using the following distance matrix: (12) Let wij be the weighted distance between data samples ai and aj. Sort the elements in each column of the matrix in ascending order, forming the element wli' in the j-th column and l-th row in the new distance matrix W', i.e., the distance from ai to the l-th farthest data sample: (13) Compute the mean of the elements in each row of W', and substitute the mean w'l of the l-th row to the DBSCAN.…”

Section: Dis(aiaj) Is Always Greater Than Zero; (2) Identity: If and ...mentioning

confidence: 99%

“…Online learning platforms and intelligent terminals provide flexible and pertinent learning modes for college students [1][2][3][4][5]. Online teaching and learning fully reflect the concept of student-oriented teaching, and effectively improve the initiative of students, exerting a huge impact on the teaching and evaluation ideas of traditional offline classroom [6][7][8][9][10][11][12][13]. To improve teaching quality, online learning platforms and teachers have been actively integrating Internet information technology into information teaching mode.…”

Section: Introductionmentioning

confidence: 99%

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Formative Evaluation of College Students’ Online English Learning Based on Learning Behavior Analysis

Zhang¹,

Ming-li²,

Song³

et al. 2022

Int. J. Emerg. Technol. Learn.

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To measure the achievements and progress of college students in online learning, online learning platforms and teachers must pay attention to the formative evaluation of the learning process. The relevant data should be fully utilized to analyze the online English learning behavior of college students, such that online learning platforms and teachers can make formative evaluation of the students’ online learning. However, the existing studies on formative evaluation are mostly theoretical. To solve the problem, this paper explores the formative evaluation of college students’ online English learning based on learning behavior analysis. Firstly, the density-based spatial clustering of applications with noise (DBSCAN) was adopted to analyze the data samples of college students’ online English learning behavior, the evaluation indices were selected for the formative evaluation of the said behavior, and the index weighting method was explained in details. Next, the school precaution function of online English learning was realized through the graph structure data prediction of students’ online learning behavior. Based on the proposed graph neural network, the clustering Euclidean distance weight was introduced to measure the similarity between two nodes. In addition, the weight update process was illustrated for the distance weight-based attention mechanism. The proposed formative evaluation approach was proved effective through experiments.

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Section: Dis(aiaj) Is Always Greater Than Zero; (2) Identity: If and ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Formative Evaluation of College Students’ Online English Learning Based on Learning Behavior Analysis

Zhang¹,

Ming-li²,

Song³

et al. 2022

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

“…Online learning is a novel learning pattern developing rapidly based on online education platforms in recent years, and it has been taken as a major development direction in the field of education [1][2][3][4][5]. This new style learning pattern can satisfy college students' personalized learning requirements in diverse directions, help improve their learning efficiency, and solve problems such as the time and space limits, as well as the unbalanced learning resource allocation [6,7]. To urge college students to learn actively and satisfy their needs for modern education and lifelong learning, the online education platforms should formulate reasonable teaching strategies to improve users' learning effect [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Learning Interest Model for a Personalized Learning Requirement Analysis of College Students

Wang¹,

Han²,

Wei³

2022

Int. J. Emerg. Technol. Learn.

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The randomness in college students’ participation of online learning causes common problems such as improper learning resource allocation or unbalanced supply and demand to online education platforms, thus scientifically predicting college students’ learning requirements, triggering their dynamic learning interest, and effectively improving their learning effect are important prerequisites for ensuring these platforms to develop stably, efficiently, and sustainably. Existing prediction models generally ignored the progressing process of learning and failed to comprehensively model or analyze from multiple dimensions such as resource utilization, learning behavior, learning pattern, teaching mode, and teaching management, etc. To fill in this research gap, this paper proposed a Dynamic Learning Interest (DLI) model for analyzing the personalized learning requirements of college students. Based on the college students’ online learning cycles and the learning cycle division results, a time series DLI model was established for studying the prediction of college students’ personalized learning requirements, and short-term prediction and comparative experiment were carried out accordingly. Then, the proposed model was subject to stability test and white noise test to determine the model structure and estimate it parameters. At last, significance test was performed on the proposed model and its parameters, and the results verified the effectiveness of the model.

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Language e-Learning and Music Appreciation

Cited by 2 publications

References 7 publications

Formative Evaluation of College Students’ Online English Learning Based on Learning Behavior Analysis

Formative Evaluation of College Students’ Online English Learning Based on Learning Behavior Analysis

A Dynamic Learning Interest Model for a Personalized Learning Requirement Analysis of College Students

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