A study on the development of English reading skills in the MOOC model of English language teaching

被引：0

作者：

Ling L. ^{[1
]}

机构：

[1] Department of Foreign Languages, Southwest Jiaotong University Hope College, Chengdu

来源：

International Journal of Networking and Virtual Organisations | 2023年 / 28卷 / 2-4期

关键词：

DBN; deep belief network; English teaching; MOOC; personalised recommendation; reading ability;

D O I：

10.1504/IJNVO.2023.133861

中图分类号：

学科分类号：

摘要：

This study proposes a personalised intelligent reading resource recommendation method based on MOOC mode. This method uses a deep belief network (DBN) model to extract students’ reading interests and other related data features, and uses the K-means algorithm to classify users’ interests. The model is applied to a personalised recommendation system in the MOOC environment. When the training set accounts for 100%, 75%, 50%, and 25% of the total dataset, the root mean square errors of the recommendation results of the DBN algorithm are 78%, 83%, 88%, and 96%, respectively. During the training process, the convergence speed of the DBN algorithm is significantly faster, with a minimum root mean square error value of 0.805. In the evaluation of recommendation effectiveness under different indicators, DBN performs the best, indicating that the model can adapt to various situations and has great practical application value. Copyright © 2023 Inderscience Enterprises Ltd.

引用

页码：318 / 336

页数：18

共 20 条

[1]

Absatova M., Aituarova A., Ospanbekova M., Azhgaliyev M., Uanbayev E., Doshybekov A., Experimental research of motivational and reflexive components of students’ reading culture, Linguistics and Culture Review, 5, S1, pp. 670-683, (2021)

[2]

Ambarita R., Kristiana V., Dewi R.S., The effect of metacognitive strategy to improve reading ability of the students in stie alwashliyah sibolga, Project (Professional Journal of English Education), 5, 1, pp. 195-199, (2022)

[3]

Atmowardoyo H., Sakkir G., Effects of best-practice based materials in receptive language learning behaviours in improving receptive language skills, Linguistics and Culture Review, 5, S1, pp. 1313-1334, (2021)

[4]

Chen Y., Dai Y., Han X., Ge Y., Yin H., Li P., Dig users’ intentions via attention flow network for personalized recommendation, Information Sciences, 547, 8, pp. 1122-1135, (2021)

[5]

Erya W.I., Pustika R., Students’ perception towards the use of webtoon to improve reading comprehension skill, Journal of English Language Teaching and Learning, 2, 1, pp. 51-56, (2021)

[6]

Fu Z., Lian T., Yao Y., Zheng W., MulSimNet: a multi-branch sub-interest matching network for personalized recommendation, Neurocomputing, 495, 21, pp. 37-50, (2022)

[7]

Gu H., Yao J., Bai P., Zhou L., Cheung A., Abrami P., Does Abracadabra help improve the English reading ability of Chinese elementary school students? A quasi-natural experimental study, Science Insights Education Frontiers, 9, 2, pp. 1221-1240, (2021)

[8]

Hartono H., Suparto S., Hassan A., Language: a ‘mirror’ of the culture and its application English language teaching, Linguistics and Culture Review, 5, 1, pp. 93-103, (2021)

[9]

Jeong H.Y., Multi criteria based personalized recommendation service using analytical hierarchy process for Airbnb, The Journal of Supercomputing, 77, 11, pp. 13224-13242, (2021)

[10]

Katsaris I., Vidakis N., Adaptive e-learning systems through learning styles: a review of the literature, Advances in Mobile Learning Educational Research, 1, 2, pp. 124-145, (2021)

← 1 2 →