復(fù)旦大學(xué)經(jīng)濟(jì)學(xué)院宋弘教授學(xué)術(shù)報(bào)告 | CCAP成立30周年系列活動(dòng)

報(bào)告題目:Educational Informatization and Students Human Capital: Heterogeneity and Long-term Evidence

報(bào)  告  人:宋弘 教授(復(fù)旦大學(xué))

持 人:劉承芳 教授

報(bào)告時(shí)間:2025年3月5日 星期三 13:30-14:30

報(bào)告地點(diǎn):北京大學(xué)王克楨樓107會(huì)議室


報(bào)告人簡(jiǎn)介

宋弘,復(fù)旦大學(xué)經(jīng)濟(jì)學(xué)院教授、博導(dǎo),新加坡國(guó)立大學(xué)經(jīng)濟(jì)學(xué)博士,入選國(guó)家級(jí)青年人才計(jì)劃(2021)。研究領(lǐng)域?yàn)閯趧?dòng)經(jīng)濟(jì)學(xué)、發(fā)展經(jīng)濟(jì)學(xué),論文發(fā)表于American Economic Journal: Economic Policy, Journal of Labor EconomicsJournal of Development Economics, Journal of International Economics, 《經(jīng)濟(jì)研究》、《管理世界》、《經(jīng)濟(jì)學(xué)季刊》、《世界經(jīng)濟(jì)》、《金融研究》等國(guó)內(nèi)外權(quán)威期刊。曾獲張培剛發(fā)展經(jīng)濟(jì)學(xué)青年學(xué)者獎(jiǎng)、洪銀興經(jīng)濟(jì)學(xué)獎(jiǎng)、上海市曙光學(xué)者、上海社科新人上海市哲學(xué)社會(huì)科學(xué)學(xué)術(shù)新銳獎(jiǎng)、優(yōu)秀成果獎(jiǎng)等。多份資政專報(bào)被國(guó)家領(lǐng)導(dǎo)人批示,并被國(guó)家和地方政府部門(mén)采納,擔(dān)任Journal of Asian Economics, China & World Economy, 《世界經(jīng)濟(jì)文匯》等期刊副主編或編輯,曾獲復(fù)旦大學(xué)教學(xué)創(chuàng)新大賽一等獎(jiǎng)等。


報(bào)告內(nèi)容

We conducted a randomized experiment on educational informatization in impoverished regions of western China, leveraging information technology to deliver high-quality instructional videos to rural primary school students. The study aimed to evaluate the impact of this intervention on student development, with a focus on both short-term dynamic effects and long-term outcomes over a three-year period. The experiment was designed with three distinct treatment groups: one receiving online education, another using recorded video lessons, and a third relying on traditional teacher-led instruction. We specifically examined how the effects of educational informatization varied across different student populations, subjects, and grades, as well as how it interacted with traditional teachers and teaching methods. The analysis establishes substantial heterogeneity in the effects of educational informatization. Additionally, we employed machine learning techniques to explore heterogeneity, seeking to investigate context-specific and personalized strategies for optimizing the effectiveness of educational informatization.


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