Evidence map›Paper›PMID 41003253›Full record

ArticleJournal of Intelligence2025

Development of a Computerized Adaptive Assessment and Learning System for Mathematical Ability Based on Cognitive Diagnosis.

Yi Zhang, Liping Zhang, Heyang Zhang, Xiaopeng Wu

Abstract read
In one paragraph

Article in Journal of Intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Yi ZhangSchool of Education, Guangzhou University, Guangzhou 510006, China.
Liping ZhangFaculty of Education, Northeast Normal University, Changchun 130024, China.
Heyang ZhangSchool of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China.ORCID 0009-0003-8546-9587
Xiaopeng WuSchool of Education, Guangzhou University, Guangzhou 510006, China.ORCID 0000-0003-0147-8228

Funding

Guangdong Provincial Philosophy and Social Science Foundation: A Study on Cognitive Diagnostic Measurement of Academic Achievement of Primary and Secondary School Students in the Greater Bay Area in the Context of New Quality Education GD25YJY18
6 · The paper itself

Abstract

With the rapid evolution of technology and the continuous deepening of digital transformation in education, personalized and adaptive learning have emerged as inevitable trends in the educational landscape. This study focuses on a Computerized Adaptive Learning System Based on Cognitive Diagnosis (CAL-CDS)-an integrated platform that incorporates multiple technologies for assessment and learning. The study is organized around two dimensions: (1) constructing a foundational cognitive diagnostic assessment framework, and (2) investigating the operational mechanisms of the cognitive diagnosis-based computerized adaptive system. It comprehensively incorporates core components including cognitive modeling, Q-matrix generation, and diagnostic test development. On this basis, this study dissects the system's operational logic from four aspects: the adaptive testing system, diagnostic system, recommendation system, and empirical case studies. This study effectively addresses two core questions: how to construct a cognitive diagnostic assessment framework that alignes with China's mathematics knowledge structure, and how to facilitate personalized student learning via cognitive diagnosis. Overall, this study offers a systematic solution for developing mathematics-specific cognitive diagnosis-driven adaptive learning systems.

Indexed as

adaptive learning systemcognitive diagnosis assessmentonline learning

Identifiers

PMID41003253
PMCPMC12470637

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.