Effectiveness of a numeracy intelligent tutoring system in kindergarten: A conceptual replication

Grimes, Ka Rene; Park, Soyoung; McClelland, Amanda M.; Park, Jiyeon; Lee, Young Ri; Nozari, Maryam; Umer, Zainab; Zaparolli, Brenda; Bryant, Diane Pedrotty

doi:10.5964/jnc.6931

Cited by 3 publications

(2 citation statements)

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“…Likewise, of the three studies that were identified in the highly effective intervention set (Grimes et al, 2020; Kosko & Ferdig, 2016; Wu, 2020), two used a researcher developed assessment of mathematical attainment as the primary outcome measure. Within this QCA, these issues cannot be statistically controlled for.…”

Section: Discussionmentioning

confidence: 99%

“…Eight apps were suitable for this analysis (see Figure S1). The associated included studies focused on typically developing children aged between 4 and 7 years old with an experimental design (randomised control trial or quasi‐experimental design) (Berkowitz et al, 2015; Cary et al, 2020; Grimes et al, 2020; Kosko & Ferdig, 2016; Outhwaite et al, 2018; Schacter & Jo, 2017; Spencer, 2013; Wu, 2020). Two studies reported subgroup analyses on children identified as underachieving in mathematics (Cary et al, 2020; Wu, 2020).…”

Section: Methodsmentioning

confidence: 99%

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Understanding how educational maths apps can enhance learning: A content analysis and qualitative comparative analysis

Outhwaite¹,

Early

Herodotou

et al. 2023

Brit J Educational Tech

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Educational applications (apps) are ubiquitous within children's learning environments and emerging evidence has demonstrated their efficacy. However, it remains unclear what the active ingredients (ie, mechanisms), or combination of ingredients, of successful maths apps are. The current study developed a new, open‐access, three‐step framework for assessing the educational value of maths apps, comprised of type of app, mathematical content and app design features. When applied to a selection of available maths apps previously evaluated with children in the first 3 years of school (the final sample included 23 apps), results showed that practice‐based apps were the most common app type tested (n = 15). Basic number skills, such as number representation and relationships, were the most common area of mathematics targeted by apps (n = 21). A follow‐up qualitative comparative analysis showed observed learning outcomes with maths apps were enhanced when apps combined the following: a scaffolded and personalised learning journey (programmatic levelling) and explanations of why answers were right or wrong (explanatory feedback), as well as praise, such as ‘Great job!’ (motivational feedback). This novel evidence stresses the significance of feedback and levelling design features that teaching practitioners and other stakeholders should consider when deciding which apps to use with young children. Directions for future research are discussed. Practitioner notesWhat is already known about this topic Educational apps have been shown to support maths attainment in the first 3 years of school. Several existing frameworks have attempted to assess the educational value of some of these maths apps. Emerging experimental evidence also demonstrates the benefits of specific app design features, including feedback and levelling. What this paper adds Practice‐based maths apps are the most common type of app previously evaluated with young children. These evaluated maths apps have mostly focused on basic number skills. The combination of explanatory and motivational feedback, with programmatic levelling (either dynamic or static), was a necessary condition for enhancing learning outcomes with maths apps. Implications for practice and policy The inclusion of feedback and levelling in maths apps should be considered by app developers when designing apps, and by educational practitioners and parents when deciding which apps to use with their children. Further consideration is also needed for the development of educational apps that include a broad range of maths skills.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Understanding how educational maths apps can enhance learning: A content analysis and qualitative comparative analysis

Outhwaite¹,

Early

Herodotou

et al. 2023

Brit J Educational Tech

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The Application Method of Big Data of Data Mining Algorithm in College Basketball Teaching

OuYang

Zhang

2022

Scientific Programming

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The teaching quality monitoring and evaluation system in colleges and universities gives the scientific and objective evaluation of school teaching work and effectively optimizes and adjusts the important system of teaching work under the condition of deep excavation of the problems. However, the data on the teaching quality of colleges and universities in the state of static storage has seriously affected the value of data existence and the mining of laws. This paper mainly analyzes the application types of data mining technology in the college basketball teaching quality monitoring and evaluation system and discusses the data mining implementation method in the college basketball teaching quality and evaluation system, hoping to have a certain reference for relevant personnel.

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Direct and conceptual replication in numerical cognition

Cipora

Soltanlou

2021

J. Numer. Cogn.

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When we proposed this Special Issue to John Towse, Journal of Numerical Cognition (JNC) Editor-in-Chief back in May 2019, and first spread the news via Twitter and during the second Mathematical Cognition and Learning Society (MCLS) meeting in Ottawa, the world was different. It was a very long time before we had heard of COVID-19. The MCLS was enjoying a fantastic in-person meeting and we were all looking forward to MCLS 2020.As guest editors we had multiple ideas on how to make the best out of the Special Issue when it comes to adoption of Open Science practices, methodological rigor, and using Open Science infrastructure to streamline the process. We were delighted to see so much enthusiasm, encouragement, and support from colleagues, when they heard about the initiative. It was also great to see that we received multiple pre-submission abstracts. Even though the world has changed a lot since then, it has been challenging to everyone involved, and our own roads towards this moment have sometimes been bumpy, we are here, extremely happy, and proud to present the Special Issue on Direct and Conceptual Replication in Numerical Cognition. It comprises 12 replication papers and three commentaries by authors of replicated papers. The contributions are covering most of areas of numerical and mathematical cognition spanning from hard-core basic cognitive effects through developmental studies and interventions (cf. Figure 1 and Table 1). We are even more delighted that finally all our ideas (e.g., data and material sharing, Open Review Reports) on how to get the most out of the Special Issue found their way. At the same time, we are glad to see that since we proposed the special issue, the JNC evolved. For instance, by adopting the Open Science badges it gives even more visible credit for following these practices than it did before. It seems that Open Science including replication attempts is going to be more established and valued in our field.

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Effectiveness of a numeracy intelligent tutoring system in kindergarten: A conceptual replication

Cited by 3 publications

References 87 publications

Understanding how educational maths apps can enhance learning: A content analysis and qualitative comparative analysis

Understanding how educational maths apps can enhance learning: A content analysis and qualitative comparative analysis

The Application Method of Big Data of Data Mining Algorithm in College Basketball Teaching

Direct and conceptual replication in numerical cognition

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