Examining high achievement in mathematics and science among post-primary students in Ireland: a multilevel binary logistic regression analysis of PISA data
Abstract In Ireland, while, on average, students have performed well on national and international assessments of mathematics and science, the low proportions of high achievers in these subjects are noteworthy. Given these patterns and the multifaceted benefits in individual and societal terms that...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-09-01
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| Series: | Large-scale Assessments in Education |
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| Online Access: | https://doi.org/10.1186/s40536-022-00131-x |
| _version_ | 1828373248814874624 |
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| author | Vasiliki Pitsia |
| author_facet | Vasiliki Pitsia |
| author_sort | Vasiliki Pitsia |
| collection | DOAJ |
| description | Abstract In Ireland, while, on average, students have performed well on national and international assessments of mathematics and science, the low proportions of high achievers in these subjects are noteworthy. Given these patterns and the multifaceted benefits in individual and societal terms that expertise in mathematics and science has been associated with, policymakers in Ireland have begun to place an increasing emphasis on high achievement in these subjects. This emphasis has coincided with ongoing efforts during the last decade to raise interest and improve academic performance within the realm of science, technology, engineering, and mathematics (STEM) education. Despite this policy attention, research on high achievement in mathematics and science nationally, but also internationally, has been particularly scarce. In an attempt to provide research evidence that could add further impetus to the ongoing efforts, this study examines high achievement in mathematics and science among post-primary students in Ireland using data from the 2012 and 2015 cycles of the Programme for International Student Assessment (PISA). Specifically, the study aimed to evaluate the contribution of various contextual characteristics stemming from students, their families, teachers, and schools in the prediction of high achievement in mathematics and science within a two-stage analysis that included a series of bivariate tests and multilevel binary logistic regression modelling. The results showed that variables related to students’ self-beliefs, engagement, and socioeconomic background were consistently associated with high achievement in mathematics and science. Overall, the significant role of students’ homes and families in predicting students’ chances of being high achievers in the two subjects was highlighted. In turn, this indicated that further efforts to enhance collaboration between teachers, schools, and parents may be warranted if progress in the area of high achievement in mathematics and science is to be made. The implications of these findings for policy and practice within the Irish context, the limitations of the study, and recommendations for future research are discussed. |
| first_indexed | 2024-04-14T07:16:13Z |
| format | Article |
| id | doaj.art-ae999cd1281a4d5abc75928c743520e8 |
| institution | Directory Open Access Journal |
| issn | 2196-0739 |
| language | English |
| last_indexed | 2024-04-14T07:16:13Z |
| publishDate | 2022-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Large-scale Assessments in Education |
| spelling | doaj.art-ae999cd1281a4d5abc75928c743520e82022-12-22T02:06:18ZengSpringerOpenLarge-scale Assessments in Education2196-07392022-09-0110113010.1186/s40536-022-00131-xExamining high achievement in mathematics and science among post-primary students in Ireland: a multilevel binary logistic regression analysis of PISA dataVasiliki Pitsia0Centre for Assessment Research, Policy and Practice in Education (CARPE), Dublin City UniversityAbstract In Ireland, while, on average, students have performed well on national and international assessments of mathematics and science, the low proportions of high achievers in these subjects are noteworthy. Given these patterns and the multifaceted benefits in individual and societal terms that expertise in mathematics and science has been associated with, policymakers in Ireland have begun to place an increasing emphasis on high achievement in these subjects. This emphasis has coincided with ongoing efforts during the last decade to raise interest and improve academic performance within the realm of science, technology, engineering, and mathematics (STEM) education. Despite this policy attention, research on high achievement in mathematics and science nationally, but also internationally, has been particularly scarce. In an attempt to provide research evidence that could add further impetus to the ongoing efforts, this study examines high achievement in mathematics and science among post-primary students in Ireland using data from the 2012 and 2015 cycles of the Programme for International Student Assessment (PISA). Specifically, the study aimed to evaluate the contribution of various contextual characteristics stemming from students, their families, teachers, and schools in the prediction of high achievement in mathematics and science within a two-stage analysis that included a series of bivariate tests and multilevel binary logistic regression modelling. The results showed that variables related to students’ self-beliefs, engagement, and socioeconomic background were consistently associated with high achievement in mathematics and science. Overall, the significant role of students’ homes and families in predicting students’ chances of being high achievers in the two subjects was highlighted. In turn, this indicated that further efforts to enhance collaboration between teachers, schools, and parents may be warranted if progress in the area of high achievement in mathematics and science is to be made. The implications of these findings for policy and practice within the Irish context, the limitations of the study, and recommendations for future research are discussed.https://doi.org/10.1186/s40536-022-00131-xHigh achievementMathematicsSciencePISAMultilevel binary logistic regression modellingIreland |
| spellingShingle | Vasiliki Pitsia Examining high achievement in mathematics and science among post-primary students in Ireland: a multilevel binary logistic regression analysis of PISA data Large-scale Assessments in Education High achievement Mathematics Science PISA Multilevel binary logistic regression modelling Ireland |
| title | Examining high achievement in mathematics and science among post-primary students in Ireland: a multilevel binary logistic regression analysis of PISA data |
| title_full | Examining high achievement in mathematics and science among post-primary students in Ireland: a multilevel binary logistic regression analysis of PISA data |
| title_fullStr | Examining high achievement in mathematics and science among post-primary students in Ireland: a multilevel binary logistic regression analysis of PISA data |
| title_full_unstemmed | Examining high achievement in mathematics and science among post-primary students in Ireland: a multilevel binary logistic regression analysis of PISA data |
| title_short | Examining high achievement in mathematics and science among post-primary students in Ireland: a multilevel binary logistic regression analysis of PISA data |
| title_sort | examining high achievement in mathematics and science among post primary students in ireland a multilevel binary logistic regression analysis of pisa data |
| topic | High achievement Mathematics Science PISA Multilevel binary logistic regression modelling Ireland |
| url | https://doi.org/10.1186/s40536-022-00131-x |
| work_keys_str_mv | AT vasilikipitsia examininghighachievementinmathematicsandscienceamongpostprimarystudentsinirelandamultilevelbinarylogisticregressionanalysisofpisadata |