Developmental profiles of arithmetic fluency skills from grades 1 to 9 and their early identification.

Psyridou, Maria; Torppa, Minna; Tolvanen, Asko; Poikkeus, Anna-Maija; Lerkkanen, Marja-Kristiina; Koponen, Tuire

doi:10.1037/dev0001622

Cited by 3 publications

(16 citation statements)

References 90 publications

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“…The top-ranked (with average normalized importance higher than 50%) features for the prediction of MD were RAN (71.16%), spatial relations (61.49%), arithmetic skills in kindergarten (56.23%), number concepts (53.89%), attention-orientation (50.94%), and counting (spring) (50.00%; Figure 3). This is in line with previous studies showing the association of these skills with later math skills and MD (e.g., Bernabini et al, 2021; Cirino et al, 2018; Geary et al, 2018; Koponen et al, 2019; Kroesbergen et al, 2023; Nelson & Powell, 2018; Psyridou, Torppa, Tolvanen, et al, 2023; Zhang et al, 2020). Arithmetic skills in kindergarten were among the most important predictors of MD.…”

Section: Discussionsupporting

confidence: 93%

“…For instance, a recent study on the developmental profiles of arithmetic skills in the same sample as the current study found that a group with persistent arithmetic difficulties and a group with delayed onset but average performance later on shared similar risk factors in kindergarten. The distinguishing factors between them were limited to four cognitive skills (letter knowledge, counting, number concept, and RAN) and task-avoidant behavior (Psyridou, Torppa, Tolvanen, et al, 2023). Furthermore, learning is a dynamic phenomenon, and there are other factors that can influence the development of math skills during primary school which are not considered in the current model.…”

Section: Discussionmentioning

confidence: 99%

“…The rationale for including reading-related cognitive predictors in our prediction model was that MD and reading difficulties often co-occur (e.g., Joyner & Wagner, 2020; Koponen et al, 2018; Willcutt et al, 2013) and have shared predictors (e.g., Peng et al, 2020). Moreover, rapid automatized naming (RAN) has been associated with both literacy and arithmetic skills and MD (e.g., Donker et al, 2016; Georgiou et al, 2013; Koponen et al, 2017; Kroesbergen et al, 2023; Landerl et al, 2009; Psyridou, Torppa, Tolvanen, et al, 2023; Pulkkinen et al, 2022), while studies have shown contradictory results for the association between phonological awareness and MD (e.g., Amland et al, 2021; De Smedt & Boets, 2010; Liu et al, 2022; Vanbinst et al, 2014; see also Yang et al, 2022 for a meta-analysis).…”

Section: Prediction Modelsmentioning

confidence: 99%

“…In addition, higher task values are associated with higher levels of academic performance and achievement (Eccles et al, 1998; Murphy & Alexander, 2000). Finally, the self-concept of ability in math has been found to be associated with math performance (Cai et al, 2018; Psyridou, Torppa, Tolvanen, et al, 2023). In the present study, we included task-avoidant behavior, interest in reading and math, task values, and self-concept for reading and math as predictors of MD.…”

Section: Prediction Modelsmentioning

confidence: 99%

“…In the present study, we have included a variety of behavioral measures, namely attention, hyperactivity, impulsiveness, attention-orientation, planning, and disruptive behavior, as predictors of MD. Finally, gender was included in the study, although previous research has shown inconsistent findings to date (e.g., Hutchison et al, 2019; Moll et al, 2014; Psyridou, Torppa, Tolvanen, et al, 2023; Wei et al, 2015).…”

Section: Prediction Modelsmentioning

confidence: 99%

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Early prediction of math difficulties with the use of a neural networks model.

Psyridou,

Koponen,

Tolvanen

et al. 2024

Journal of Educational Psychology

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The early prediction of math difficulties (MD) is important as it facilitates timely support. MD are multifaceted, and several factors are involved in their manifestation. This makes the accurate early prediction of MD particularly challenging. In the present study, we aim to predict MD in Grade 6 with kindergarten-age (age 6) measures by applying a neural networks model. We use a set of 49 variables assessed during kindergarten from the domains of early arithmetic skills, cognitive skills, the home learning environment, parental measures, motivation, behavioral problems, and gender, which have been shown to have associations with mathematical development and/or MD. A two-step approach was used: First, we examined whether the neural networks approach can provide a solution for the effective early identification of MD based on all 49 variables and, then, by using the most important predictors as identified by the initial model. The initial model achieved an area under the curve (AUC) of .818, demonstrating excellent performance. The most important predictors of Grade 6 MD came from the domains of arithmetic and cognitive skills (arithmetic skills, rapid automatized naming, number concepts, spatial skills, counting) and behavioral problems (attention-orientation). The model with only the most important predictors achieved an AUC of .776, indicating good performance. Our results provided proof of concept for using neural networks in MD prediction in Grade 6 using information already available in kindergarten. In schools, these results could be used to identify children at potential risk of developing MD and to provide access to early support. Educational Impact and Implications StatementApproximately 4%-15% of children suffer from math difficulties (MD), and many more struggle with them without a formal diagnosis. MD have been shown to put children at increased risk of lower academic achievement, lower motivation, anxiety, depression, and even higher unemployment. Predicting MD accurately and early facilitates timely support. The current study demonstrates the potential of neural networks models to facilitate the early identification of those at risk of developing MD. The performance of our model provided proof of concept for using neural networks for the prediction of MD in Grade 6 using information already available in kindergarten. In a school setting, such prediction knowledge could be used to identify children at potential risk of developing MD and to provide access to early support.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Prediction Modelsmentioning

confidence: 99%

Section: Prediction Modelsmentioning

confidence: 99%

Section: Prediction Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Early prediction of math difficulties with the use of a neural networks model.

Psyridou,

Koponen,

Tolvanen

et al. 2024

Journal of Educational Psychology

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Machine learning predicts upper secondary education dropout as early as the end of primary school

Psyridou,

Prezja,

Torppa

et al. 2024

Sci Rep

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Education plays a pivotal role in alleviating poverty, driving economic growth, and empowering individuals, thereby significantly influencing societal and personal development. However, the persistent issue of school dropout poses a significant challenge, with its effects extending beyond the individual. While previous research has employed machine learning for dropout classification, these studies often suffer from a short-term focus, relying on data collected only a few years into the study period. This study expanded the modeling horizon by utilizing a 13-year longitudinal dataset, encompassing data from kindergarten to Grade 9. Our methodology incorporated a comprehensive range of parameters, including students’ academic and cognitive skills, motivation, behavior, well-being, and officially recorded dropout data. The machine learning models developed in this study demonstrated notable classification ability, achieving a mean area under the curve (AUC) of 0.61 with data up to Grade 6 and an improved AUC of 0.65 with data up to Grade 9. Further data collection and independent correlational and causal analyses are crucial. In future iterations, such models may have the potential to proactively support educators’ processes and existing protocols for identifying at-risk students, thereby potentially aiding in the reinvention of student retention and success strategies and ultimately contributing to improved educational outcomes.

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Context matters: the importance of extra-mathematical knowledge in solving mathematical problems

Sigus,

Mädamürk

2024

Front. Educ.

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Extra-mathematical knowledge is often overlooked when investigating mathematical skills. This study explores profiles of mathematical skills and associations with extra-mathematical knowledge and the understanding of complex sentences. The study involved 1,288 sixth-grade students (52.1% male) from 95 classes in 58 schools in Estonia. Students completed a math test as part of their regular lessons. The profiles of mathematical skills included students’ calculation skills, standard problems, and complex problems. Three distinct profiles of students emerged: students with high skill levels, students with average skill levels, and students with low skill levels. Students with high mathematical skills also had high extra-mathematical knowledge showing the crucial role of understanding the context of the math tasks in addition to having good mathematical skills.

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Developmental profiles of arithmetic fluency skills from grades 1 to 9 and their early identification.

Cited by 3 publications

References 90 publications

Early prediction of math difficulties with the use of a neural networks model.

Early prediction of math difficulties with the use of a neural networks model.

Machine learning predicts upper secondary education dropout as early as the end of primary school

Context matters: the importance of extra-mathematical knowledge in solving mathematical problems

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