{"title":"哪些一年级学生在STEM科目上取得了最大的学习成果?","authors":"Jekaterina Rogaten, B. Rienties","doi":"10.1080/23752696.2018.1484671","DOIUrl":null,"url":null,"abstract":"ABSTRACT With the introduction of the Teaching Excellence Framework a lot of attention is focussed on measuring learning gains. A vast body of research has found that individual student characteristics influence academic progression over time. This case-study aims to explore how advanced statistical techniques in combination with Big Data can be used to provide potentially new insights into how students are progressing over time, and in particular how students’ socio-demographics (i.e. gender, ethnicity, Social Economic Status, prior educational qualifications) influence students’ learning trajectories. Longitudinal academic performance data were sampled from 4222 first-year STEM students across nine modules and analysed using multi-level growth-curve modelling. There were significant differences between white and non-White students, and students with different prior educational qualifications. However, student-level characteristics accounted only for a small portion of variance. The majority of variance was explained by module-level characteristics and assessment level characteristics.","PeriodicalId":43390,"journal":{"name":"Higher Education Pedagogies","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23752696.2018.1484671","citationCount":"10","resultStr":"{\"title\":\"Which first-year students are making most learning gains in STEM subjects?\",\"authors\":\"Jekaterina Rogaten, B. Rienties\",\"doi\":\"10.1080/23752696.2018.1484671\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT With the introduction of the Teaching Excellence Framework a lot of attention is focussed on measuring learning gains. A vast body of research has found that individual student characteristics influence academic progression over time. This case-study aims to explore how advanced statistical techniques in combination with Big Data can be used to provide potentially new insights into how students are progressing over time, and in particular how students’ socio-demographics (i.e. gender, ethnicity, Social Economic Status, prior educational qualifications) influence students’ learning trajectories. Longitudinal academic performance data were sampled from 4222 first-year STEM students across nine modules and analysed using multi-level growth-curve modelling. There were significant differences between white and non-White students, and students with different prior educational qualifications. However, student-level characteristics accounted only for a small portion of variance. The majority of variance was explained by module-level characteristics and assessment level characteristics.\",\"PeriodicalId\":43390,\"journal\":{\"name\":\"Higher Education Pedagogies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2018-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/23752696.2018.1484671\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Higher Education Pedagogies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/23752696.2018.1484671\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Higher Education Pedagogies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23752696.2018.1484671","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
Which first-year students are making most learning gains in STEM subjects?
ABSTRACT With the introduction of the Teaching Excellence Framework a lot of attention is focussed on measuring learning gains. A vast body of research has found that individual student characteristics influence academic progression over time. This case-study aims to explore how advanced statistical techniques in combination with Big Data can be used to provide potentially new insights into how students are progressing over time, and in particular how students’ socio-demographics (i.e. gender, ethnicity, Social Economic Status, prior educational qualifications) influence students’ learning trajectories. Longitudinal academic performance data were sampled from 4222 first-year STEM students across nine modules and analysed using multi-level growth-curve modelling. There were significant differences between white and non-White students, and students with different prior educational qualifications. However, student-level characteristics accounted only for a small portion of variance. The majority of variance was explained by module-level characteristics and assessment level characteristics.
期刊介绍:
The aim of Higher Education Pedagogies is to identify, promote and publish excellence and innovations in the practice and theory of teaching and learning in and across all disciplines in higher education. The journal will provide an international forum for the sharing, dissemination and discussion of research, experience and perspectives across a wide range of teaching and learning issues. The journal will prove a valuable resource for individuals in the development and enhancement of their own practice, and for institutions in the promotion of the scholarship of teaching and learning. Higher Education Pedagogies will focus on disciplinary pedagogies and learning experiences; the higher education curriculum, i.e. what is taught and how it is developed and enhanced including both skills and knowledge; the delivery of the higher education curriculum; how it is taught and how students learn, and academic development; the role of teaching and learning in the development of academic careers and its place within the profession. Higher Education Pedagogies welcomes papers which are accessible to both specialist and generalist readers and are theoretically and empirically rigorous. Through advancing knowledge of, and practice in, teaching and learning, Higher Education Pedagogies will prove essential reading for all those who wish to stay informed of state-of-the-art teaching and learning developments in higher education. Higher Education Pedagogies is sponsored by the Higher Education Academy.