The Role of Executive Functions for Dyadic Literacy Learning in Kindergarten

Sande, E. van de; Segers, Eliane

doi:10.1080/10409289.2017.1393739

Cited by 8 publications

(5 citation statements)

References 47 publications

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“…This could improve alignment between the instructional material given and the students' needs which optimizes the functioning of system regulation. Moreover, this would facilitate the targeting of direct interventions on students' regulation with, for example, stop and think interventions -an intervention that has proven effective in other studies (Sande, Segers, & Verhoeven, 2018). Another possible future question is to explore how moment-by-moment learning curves could be used as an assessment tool for SRL.…”

Section: Discussionmentioning

confidence: 99%

What can moment-by-moment learning curves tell about students’ self-regulated learning?

Molenaar¹,

Horvers²,

Baker

2021

Learning and Instruction

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Many students in primary education learn mathematics using adaptive learning technologies (ALTs) on tablets every day. Driven by developments in the emerging field of learning analytics, these technologies adjust problems based on learners' performance. Yet, until now it is largely unclear how students regulate their learning with ALTs. Hence, we explored how learners regulate their effort, accuracy and learning with an ALT using moment-by-moment learning curves. The results indicated that moment-by-moment learning curves did reflect students' accuracy and learning, but no associations with effort were found. Immediate drops were associated with high prior knowledge and suboptimal learning. Immediate peaks were associated with robust learning and pointed to effective student regulation. Close multiple spikes showed moderate learning and lower initial levels of accuracy but, with system support, these students seemed able to regulate their learning. Separated multiple spikes indicated reduced learning and accuracy and potentially signal the inability of students to regulate their learning. In this light, moment-by-moment learning curves seem to be valuable indicators of accuracy regulation during learning with ALTs and could potentially be used in interventions to support SRL with personalized visualizations.

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Section: Discussionmentioning

confidence: 99%

What can moment-by-moment learning curves tell about students’ self-regulated learning?

Molenaar¹,

Horvers²,

Baker

2021

Learning and Instruction

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“…However, there is no consensus on this point (see e.g., Melby-Lervag, Redick & Hulme, 2016 for a meta-analysis on lack of transfer effects of working memory training). What does seem relevant is to make sure that kindergartners with weaker executive functions receive support while doing a taxing task (see e.g., Van de Sande, Segers, & 2017).…”

Section: Discussionmentioning

confidence: 99%

Individual differences in the development of scientific thinking in kindergarten

Graaf

Segers

2018

Learning and Instruction

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“…For example, Nystrand et al [36] employed absolute duration measures to document an overall low level of in-depth discussion in the classes they observed (average times of between 15 and 50 s per class period) and highlighted the relatively longer duration of in-depth discussion in high-track versus low-track classes (almost twice as much time spent on discussion in high-track classes). Sande et al [50] showed that children with reduced attention control spend less time play a serious game compared to children with high attention control. Kovanović et al [27] emphasize the importance of careful decision making in determinations of how to calculate time-on-task in online environments from clickstream data in which estimates must be made to account for task abandonment and the lack of formal log-out procedures.…”

Section: Passage Of Time: Considering Time As a Continuous Flow Of Ev...mentioning

confidence: 99%

Temporal Aspects of Learning Analytics - Grounding Analyses in Concepts of Time

Molenaar¹,

Wise²

2022

The Handbook of Learning Analytics

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This chapter represents an effort to lay out a common framework for the concepts of time to (a) support diverse researchers working on temporal aspects of learning analytics to communicate better, (b) facilitate an understanding of how different approaches to studying time in learning articulate and (c) map out the space of temporal analysis to reduce redundancy of efforts. We distinguish two concepts of time, namely the passage of time and order in time. Passage of time considers time as a continuous flow of events and order in time focuses on the organization among events. Within the passage of time we distinguish four metrics: position, duration, frequency and rate. Within order in time we discriminate between consistency, recurrent and non-recurrent change and irregular change. Metrics extracted to index passage of time can be used in many different statistical methods, whereas analysis of order in time commonly requires the usage of advanced analysis methods. For either, decisions about the level of granularity at which time is considered and segmentation of time into “windows” have important effects on analysis results. We argue that understanding the value of temporal concepts and implications for the related analysis, is foundational for closing the loop and advancing learning analytics design with temporal insights.

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The Role of Executive Functions for Dyadic Literacy Learning in Kindergarten

Cited by 8 publications

References 47 publications

What can moment-by-moment learning curves tell about students’ self-regulated learning?

What can moment-by-moment learning curves tell about students’ self-regulated learning?

Individual differences in the development of scientific thinking in kindergarten

Temporal Aspects of Learning Analytics - Grounding Analyses in Concepts of Time

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