Deep Collaborative Online Learning Resource Recommendation Based on Attention Mechanism

Experiential Learning (ExL) is an effective way to consolidate theoretical knowledge and deepen understandings, and the recommendation of ExL resources needs to also take the effect of students’ theoretical learning into consideration. However, existing studies generally ignore the stage-by-stage assessment of students’ completion of online learning tasks, and the recommendation performance of existing resource recommendation models for ExL is not satisfactory enough. Therefore, the recommendation method needs to be innovated, and the interpretability of recommendation results is facing challenges. To respond to these issues, this paper studied a new resource recommendation method for ExL based on the completion degree of online learning tasks. At first, the paper gave the principle of recommending ExL resources based on the completion degree of online learning tasks, and built an online learning task completion degree prediction model. Then, this paper adopted a bi-directional GRU network model based on attention mechanism to analyze the recent online learning behavior sequence of students and attain the completion degree of students’ short term learning tasks. After that, a knowledge map representing ExL resources and the correlation of knowledge attributes was drawn; by combining the completion degree of both the short-term and long-term learning tasks, the ExL resources suitable for students were recommended to them. At last, experimental results verified the effectiveness of the constructed model.

Section: Introductionmentioning

confidence: 99%

A New Resource Recommendation Method for Experiential Leaching Based on the Completion Degree of Online Learning Tasks

Jia¹

2023

“…Aiming at the lack of hierarchy and systematicness of resource recommendation caused by abundant online learning resources and numerous learning platforms, Hao and Yang [23] proposed an attention-based ADCF online learning resource recommendation model by introducing attention mechanism into deep collaboration DCF model. Experimental results show that compared with the DCF model before improvement, the proposed ADCF model achieves accurate recommendation of online learning resources.…”

Section: Introductionmentioning

confidence: 99%

A New Learning Resource Recommendation Method for Improving the Efficiency of Students' Online Independent Learning

Shi¹,

Fan²

2023

If the learning resource recommendation method fully considers the efficiency improvement of college students' online independent learning, it can save college students' learning energy and improve their learning quality, but also can promote students to develop such learning attitudes as active participation, willingness to explore and frequent practices. Therefore, this article studies the learning resource recommendation method for improving the efficiency of college students' online independent learning. This article quantitatively evaluates the learning efficiency of college students in the process of online independent learning, and takes the evaluation results as a reference for learning resource recommendation methods. The capsule network combined with self-attention routing algorithm is used to represent the various demands of college students' online independent learning with multiple vectors, and the sequence information layer based on Transformer model is set up to construct the learning resource recommendation model. Experimental results verify the effectiveness of the model.

“…Stimulating students' interest in learning is to make students have the enthusiasm to learn English, which usually comes from both needs and interests [8][9][10][11][12][13][14]. Personalized recommendation technology, which is widely used in Internet platform, can also be applied to the field of higher education, so as to achieve accurate matching between learning resources and college students, meet students' personalized learning preference needs, reduce students' learning resource selection cost, and provide students with more diversified and rational learning resource supply [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Personalized Learning Resources Recommendation for Interest-Oriented Teaching

Qiu-cheng¹

2023

An interest-oriented teaching method can stimulate students' interest in learning, thus generating great internal drive. The personalized learning resource recommendation method for interest-oriented teaching meets students' personalized learning preference needs, reduces students' learning resource selection cost, and provides students with more diversified and rational learning resource supply. Different from traditional recommendation algorithms, the recommendation algorithm constructed in this article essentially adjusts the recommendation results in real time based on college students' adoption behavior of historical recommendation information. This article describes the problem of personalized learning resource recommendation for interest-oriented teaching, and constructs a personalized learning resource recommendation model based on the communication power of high-scoring learning resources. Experimental results verify the effectiveness of the model.