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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/157154


    Title: 大型資料之表徵學習：心理計量學如何從深度學習獲得助益？
    Representation Learning with Large Data Sets: How Can Psychometrics Benefit from Deep Learning?
    Authors: 黃柏僩
    Contributors: 心理系
    Keywords: 表徵學習;試題反應理論;測驗理論;自編碼器;潛在狄利克雷分配;變分法;機器學習;深度學習;人工智慧;對抗生成模型;自動試題生成
    representation learning;item response theory;test theory;autoencoder;latent Dirichlet allocation;variational inference;machine learning;deep learning;artificial intelligence;generative adversarial nets;automated item generation
    Date: 2023-10
    Issue Date: 2025-05-28 14:07:32 (UTC+8)
    Abstract: 近年來，深度學習已在電腦視覺、語音辨識等科學領域獲得空前的成功。本計畫試圖從近期深度學習的研究成果，思考心理計量可如何從中獲得助益。透過對深度學習文獻之回顧，我們提出了幾種應用深度學習於心理計量的研究方向，包括使用GPU計算與變分法（variational inference）加速複雜心理計量模型於大型資料之訓練、建立可整合結構與非結構資料之測量模型、以及使用生成對抗網路（generative adversarial network）協助自動試題的產生。申請人認為前述的這些研究方向，都是在建立智慧測驗評量系統時所需解決的關鍵步驟，並能協助古典心理計量學面對當代巨量資料之挑戰。
    Relation: 科技部, MOST109-2410-H004-197-MY3, 109.08-112.07
    Data Type: report
    Appears in Collections:[心理學系] 國科會研究計畫

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