Evidence map›Paper›PMID 42082584›Full record

ArticleScientific reports2026

Visual analysis of mathematics education resources by introducing bidirectional long short-term memory networks for smart classroom.

Jia Wei, Miao Li

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jia WeiCollege of Education, Bohai University, Jinzhou, 121013, China. weijia@qymail.bhu.edu.cn.
Miao LiCollege of Education, Bohai University, Jinzhou, 121013, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to address the problems of incomplete temporal feature capture and low high-dimensional data visualization in mathematical education resource analysis within smart classrooms. It constructs a mathematical education resource visualization analysis model integrating the Bidirectional Long Short-Term Memory Network (BiLSTM) and Multi-Head Attention (MHA) mechanism. Taking the 16-week mathematics learning data of 1200 high school students from the smart classroom platform (including more than 150,000 behavior records, teaching resource attributes, and learning performance indicators) as the research object, this study cleans and normalizes the data, adopts Pearson correlation coefficients to screen key features, and constructs a multi-dimensional temporal matrix. Second, it uses a multi-layer BiLSTM to extract bidirectional temporal dependencies of learning behaviors, and combines MHA (number of heads = 4, single-head dimension = 32) to enhance the weights of key behaviors such as wrong-question review and delayed review. Finally, the study employs Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to achieve dimensionality reduction and visualization of high-dimensional features. Experimental comparisons with models such as the single-layer Long Short-Term Memory Network (LSTM) and BiLSTM show that the Multi-BiLSTM + MHA model achieves optimal performance. This model outperforms other models in accuracy, F1-score, and resource utilization rate, with a fast-training convergence speed. In student group clustering, the high-activity group shows significantly higher knowledge mastery levels, with good group differentiation. This model can intuitively present differences in learning patterns and has strong generalization ability. This study provides technical support for personalized mathematics teaching and precise resource allocation in smart classrooms.

Indexed as

MathematicsHumansLearningLong Short Term MemoryPrincipal Component AnalysisSchoolsStudentsAttention mechanismBidirectional long and short-term memory networkSmart classroomVisualization of educational resources

Identifiers

PMID42082584
PMCPMC13328563

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.