English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cognitive diagnosis is an essential task in many educational applications.Many solutions have been designed in the literature. The deterministic input,noisy "and" gate (DINA) model is a classical cognitive diagnosis model and canprovide interpretable cognitive parameters, e.g., student vectors. However, theassumption of the probabilistic part of DINA is too strong, because it assumesthat the slip and guess rates of questions are student-independent. Besides,the question matrix (i.e., Q-matrix) recording the skill distribution of thequestions in the cognitive diagnosis domain often requires precise labels givenby domain experts. Thus, we propose an explicit student vector estimation(ESVE) method to estimate the student vectors of DINA with a localself-consistent test, which does not rely on any assumptions for theprobabilistic part of DINA. Then, based on the estimated student vectors, theprobabilistic part of DINA can be modified to a student dependent model thatthe slip and guess rates are related to student vectors. Furthermore, wepropose an unsupervised method called heuristic bidirectional calibrationalgorithm (HBCA) to label the Q-matrix automatically, which connects thequestion difficulty relation and the answer results for initialization and usesthe fault tolerance of ESVE-DINA for calibration. The experimental results ontwo real-world datasets show that ESVE-DINA outperforms the DINA model onaccuracy and that the Q-matrix labeled automatically by HBCA can achieveperformance comparable to that obtained with the manually labeled Q-matrix whenusing the same model structure.

Cognitive Diagnosis with Explicit Student Vector Estimation and Unsupervised Question Matrix Learning