Teachers' expertise in feedback application adapted to the phases of the learning process

Christophel, Eva; Gaschler, Robert; Schnotz, Wolfgang

doi:10.3389/fpsyg.2014.00858

“…Ericsson et al, 1993 ) might require that young players develop skills to competently use of their motivational resources, by, for instance, scheduling work on skill acquisition such that as many of the activities as possible are intrinsically motivating (cf. Rheinberg and Engeser, 2010 as well as Christophel et al, 2014 , for training of motivational competence). Underlining this challenge, Coughlan et al, 2014 reported that participants in the expert group of their study rated their practice as more effortful and less enjoyable compared to other participants.…”

Section: Discussionmentioning

confidence: 99%

Playing off the curve - testing quantitative predictions of skill acquisition theories in development of chess performance

Gaschler

¹

,

Progscha

²

,

Smallbone

³

et al. 2014

Self Cite

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Learning curves have been proposed as an adequate description of learning processes, no matter whether the processes manifest within minutes or across years. Different mechanisms underlying skill acquisition can lead to differences in the shape of learning curves. In the current study, we analyze the tournament performance data of 1383 chess players who begin competing at young age and play tournaments for at least 10 years. We analyze the performance development with the goal to test the adequacy of learning curves, and the skill acquisition theories they are based on, for describing and predicting expertise acquisition. On the one hand, we show that the skill acquisition theories implying a negative exponential learning curve do a better job in both describing early performance gains and predicting later trajectories of chess performance than those theories implying a power function learning curve. On the other hand, the learning curves of a large proportion of players show systematic qualitative deviations from the predictions of either type of skill acquisition theory. While skill acquisition theories predict larger performance gains in early years and smaller gains in later years, a substantial number of players begin to show substantial improvements with a delay of several years (and no improvement in the first years), deviations not fully accounted for by quantity of practice. The current work adds to the debate on how learning processes on a small time scale combine to large-scale changes.

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“…Their function is to initiate the learning process and to monitor the progress, such as to instruct the learner to progress from simple problems to more complex ones (Bimba et al, 2017). Several findings show that teachers themselves have difficulties selecting appropriate tasks for their students (cf., Christophel et al, 2014). However, because task selec-tion is a prerequisite to have students advance in their learning process, ML could help to solve this problem (Naidu et al, 2020).…”

Section: Adaptive Learningmentioning

confidence: 99%

Machine Learning in Educational Sciences

2024

0

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Classical statistical methods are limited in the analysis of highdimensional datasets. Machine learning (ML) provides a powerful framework for prediction by using complex relationships, often encountered in modern data with a large number of variables, cases and potentially non-linear effects. ML has turned into one of the most influential analytical approaches of this millennium and has recently become popular in the behavioral and social sciences. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows as larger and more complex datasets become available through massive open online courses (MOOCs) and large scale investigations.The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. Here, we review the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. In this review, we (1) provide an overview of the types of data suitable for ML, (2) give practical advice for the application of ML methods, and (3) show how ML-based tools and applications can be used to enhance the quality of education. Additionally we provide practical R code with exemplary analyses, available at https: //osf.io/ntre9/?view only=d29ae7cf59d34e8293f4c6bbde3e4ab2.

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“…Their function is to initiate the learning process and to monitor the progress, such as to instruct the learner to progress from simple problems to more complex ones (Bimba et al, 2017). Several findings show that teachers themselves have difficulties selecting appropriate tasks for their students (cf., Christophel et al, 2014). However, because task selec-tion is a prerequisite to have students advance in their learning process, ML could help to solve this problem (Naidu et al, 2020).…”

Section: Adaptive Learningmentioning

confidence: 99%

Machine Learning for the Educational Sciences

Hilbert¹,

Coors²,

Kraus³

et al. 2021

Preprint

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Classical statistical methods are limited in the analysis of highdimensional datasets. Machine learning (ML) provides a powerful framework for prediction by using complex relationships, often encountered in modern data with a large number of variables, cases and potentially non-linear effects. ML has turned into one of the most influential analytical approaches of this millennium and has recently become popular in the behavioral and social sciences. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows as larger and more complex datasets become available through massive open online courses (MOOCs) and large scale investigations.The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. Here, we review the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. In this review, we (1) provide an overview of the types of data suitable for ML, (2) give practical advice for the application of ML methods, and (3) show how ML-based tools and applications can be used to enhance the quality of education. Additionally we provide practical R code with exemplary analyses, available at https: //osf.io/ntre9/?view only=d29ae7cf59d34e8293f4c6bbde3e4ab2.

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Teachers' expertise in feedback application adapted to the phases of the learning process

Cited by 6 publications

References 28 publications

Playing off the curve - testing quantitative predictions of skill acquisition theories in development of chess performance

Playing off the curve - testing quantitative predictions of skill acquisition theories in development of chess performance

Machine Learning in Educational Sciences

Machine Learning for the Educational Sciences

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