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on Education |
| By: | Zuzana Smidova |
| Abstract: | This paper reviews recent empirical literature on policy drivers of two educational outcomes - years of schooling and rates of return - that form the OECD’s aggregate measure of human capital. The paper sets the literature findings into the context of current educational polices in place in OECD countries. While much of the empirical results are mixed, depend on country and time coverage as well as estimation methods, the review identifies the following policies most likely to promote better educational outcomes: quality pre-primary education, quality teaching, accountability and autonomy of teaching institutions, comprehensive lower secondary education and availability of individual financing for the pursuit of higher education. |
| Keywords: | early childhood education, education, education attainment, education quality, higher education, OECD, primary school, secondary school, teacher |
| JEL: | E24 I20 I26 I28 I25 J24 |
| Date: | 2019–11–13 |
| URL: | http://d.repec.org/n?u=RePEc:oec:ecoaaa:1577-en&r=all |
| By: | van der Vorst, Tommy; Jelicic, Nick |
| Abstract: | In this study we explore the potential impact of educational AI applications in personalized learning. According to Bloom (1984) students that are tutored one-to-one perform two standard deviations better than students who learn via traditional educational methods. Due to the limited amount of teachers and costs associated, personalized one-to-one learning is not generally feasible from a societal point of view. Breakthroughs in the field of machine learning offer promising avenues to aid in personalized learning. AI may hence be the 'holy grail' in unlocking the potential of one-to-one learning, by enabling applications to offer personalized teaching to each individual student. We assess the potential impact of AI in personalized learning from a socio-technical perspective. Therefore, we investigate the technological possibilities, as well as any aspects that may impact adoption, e.g. legal, societal and ethical. To conclude we formulate policy options that can stimulate the adoption of AI-driven personalized learning applications. |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:itse19:205222&r=all |