Information Overload and Interaction Overload as Two Separate Attributes of Information Overload Syndrome

He, Tingting

doi:10.1142/s0218495821500114

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…They are with limitation to learn the complex interaction between students and exercise questions. At present, considerable progress in personalized learning has been achieved through methods based on cognitive psychology and related theories, however, personalized learning in online scenarios still faces the issue of information overload [3]. In A R T I C L E I N F O Personalized exercise question recommendation is a crucial aspect of smart education used to customize educational exercises and questions to individual students' distinct abilities and learning progress.…”

Section: Introductionmentioning

confidence: 99%

Enhanced personalized learning exercise question recommendation model based on knowledge tracing

Pei,

Raga Jr.,

Abisado

2023

Int. J. Adv. Intell. Informatics

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Personalized exercise question recommendation is a crucial aspect of smart education used to customize educational exercises and questions to individual students' distinct abilities and learning progress. Integrating cognitive diagnosis with deep learning has shown promising results in personalized exercise recommendations. However, the black-box nature of the deep learning model hinders their interpretability. This makes it challenging for educators and students to understand the reasons behind the model's predictions for the next problem, and this limits their opportunity to take an active role in improving the learning process. To address this limitation, this article presents a novel personalized exercise question recommendation model based on knowledge tracing. The approach incorporates graph convolutional neural networks to model the student's abilities, thus enhancing the interpretability of the model. By employing Bidirectional gate recurrent unit (Bi-GRU), the model effectively traces fluctuations in students' abilities over time and predicts their responses to exercise questions. Experimental results demonstrate the effectiveness of this model, achieving an accuracy of 90.8% and 92.6% on ASSISTment 2009 and ASSISTment 2017 datasets, containing 4218 and 1709 student records, respectively. Moreover, the experiment was also conducted to validate the model's exercise difficulty setting. Results indicate an acceptable level of effectiveness in generating appropriate difficulty-level recommendations for individual students. The proposed model contributes to advancing personalized exercise recommendations by offering valuable insights that can lead to more efficient and effective student learning experiences.

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Section: Introductionmentioning

confidence: 99%

Enhanced personalized learning exercise question recommendation model based on knowledge tracing

Pei,

Raga Jr.,

Abisado

2023

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

show abstract

“…In recent years, with the rapid development and popularization of the World Wide Web and smart devices, all kinds of information emerge one after another. Faced with surging information, it is difficult for people to efficiently and accurately obtain the information they want from the vast sea of Internet information, which is the problem of information overload [ 1 – 5 ]. Internet giants such as Alibaba, Baidu, and Google all process and generate petabytes of data every day, and the Internet has also moved from the IT (Information Technology) era to the DT (Data Technology) era [ 6 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Filtering Recommendation of Music MOOC Resources Based on Spark Architecture

Wang

2022

Computational Intelligence and Neuroscience

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With the rapid development of MOOC platforms, MOOC resources have grown substantially, causing the problem of information overload. It is difficult for users to select the courses they need from a large number of MOOC resources. It is necessary to help users select the right music courses and at the same time make the outstanding music courses stand out. Recommendation systems are considered a more efficient way to solve the information overload problem. To improve the accuracy of the recommendation results of music MOOC resources, a mixed collaborative filtering recommendation algorithm based on Spark architecture is proposed. First, the user data and item data are modeled and scored by the collaborative filtering algorithm, then the tree structure of the XGBoost model and the features of regular learning are combined to predict the scores, and then the two algorithms are mixed to solve the optimal objective function to obtain the set of candidate recommendation data. Then, the frog-jumping algorithm is used to train the weighting factors, and the optimal combination of weighting factors is used as the training result of the samples to realize the data analysis of the mixed collaborative filtering recommendation algorithm. The experimental results in the music MOOC resource show that the average absolute error and root mean square error of the proposed method are 0.406 and 1.117, respectively, when the sparsity is 30%, which are lower than those of other existing collaborative filtering recommendation methods, with higher accuracy and execution efficiency.

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Information Overload and Interaction Overload as Two Separate Attributes of Information Overload Syndrome

Cited by 2 publications

References 37 publications

Enhanced personalized learning exercise question recommendation model based on knowledge tracing

Enhanced personalized learning exercise question recommendation model based on knowledge tracing

Collaborative Filtering Recommendation of Music MOOC Resources Based on Spark Architecture

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