Brief Experimental Analysis of Math Interventions: A Synthesis of Evidence

2019 ◽  
pp. 153450841988393
Author(s):  
Nicole M. McKevett ◽  
Robin S. Codding
Keyword(s):  
Experimental Analysis ◽  
Reading Interventions ◽  
Brief Experimental Analysis ◽  
Quick Method ◽  
Math Intervention ◽  
Math Interventions ◽  
Computational Fluency ◽  
Effective Interventions ◽  
And Performance ◽  
Performance Interventions

Brief experimental analysis (BEA) is a quick method used to identify the function of student learning difficulties and match effective interventions to students’ needs. Extensive work has been done to explore the use of this methodology to determine effective reading interventions; however, a smaller number of published studies have examined the use of BEAs in math. The purpose of the current review was to identify all studies that have used BEA methodology in math. Fifteen studies that included 63 participants and used BEA methodology to identify the most effective math intervention for students were located. Results of the synthesis indicate that the majority of BEAs compared skill and performance interventions on computational fluency; however, the methodology across the included studies varied. Strengths and limitations of the research, in addition to implications for research and practice, are discussed.

Brief experimental analysis of early reading interventions

2009 ◽  
Vol 47 (4) ◽  
pp. 215-243 ◽  
Author(s):  
Anna-Lind Petursdottir ◽  
Kristen McMaster ◽  
Jennifer J. McComas ◽  
Tracy Bradfield ◽  
Viveca Braganza ◽  
...  
Keyword(s):  
Experimental Analysis ◽  
Reading Interventions ◽  
Early Reading ◽  
Brief Experimental Analysis

scholarly journals Exploring the relations between teachers’ cue-utilization, monitoring and regulation of students’ text learning

2021 ◽  
Author(s):  
Janneke van de Pol ◽  
Selia N. van den Boom-Muilenburg ◽  
Tamara van Gog
Keyword(s):  
Direct Effect ◽  
Text Learning ◽  
Cue Utilization ◽  
Effective Interventions ◽  
Monitoring Accuracy ◽  
Effect Monitoring ◽  
Within Subjects ◽  
Learning From Texts ◽  
And Performance ◽  
Cue Availability

AbstractThis study investigated teachers’ monitoring and regulation of students’ learning from texts. According to the cue-utilization framework (Koriat, in Journal of Experimental Psychology, 126, 349–370, 1997), monitoring accuracy depends on how predictive the information (or cues) that teachers use to make monitoring judgments actually is for students’ performance. Accurate monitoring of students’ comprehension is considered a precondition for adaptive regulation of students’ learning. However, these assumptions have not yet been directly investigated. We therefore examined teachers’ cue-utilization and how it affects their monitoring and regulation accuracy. In a within-subjects design, 21 secondary education teachers made monitoring judgments and regulation decisions for fifteen students under three cue-availability conditions: 1) only student cues (i.e., student’s name), 2) only performance cues (i.e., diagrams students completed about texts they had read), and 3) both student and performance cues (i.e., student’s name and completed diagram). Teachers’ absolute and relative monitoring accuracy was higher when having student cues available in addition to diagram cues. Teachers’ relative regulation accuracy was higher when having only performance cues available instead of only student cues (as indicated by a direct effect). Monitoring accuracy predicted regulation accuracy and in addition to a direct effect, we also found and indirect effect of cue-availability on regulation accuracy (via monitoring accuracy). These results suggest that accurate regulation can be brought about both indirectly by having accurate monitoring judgments and directly by cue-utilization. The findings of this study can help to refine models of teacher monitoring and regulation and can be useful in designing effective interventions to promote teachers’ monitoring and regulation.

scholarly journals An Experimental Analysis of Model Compression Techniques for Object Detection

2020 ◽  
Author(s):  
Andrey De Aguiar Salvi ◽  
Rodrigo Coelho Barros
Keyword(s):  
Object Detection ◽  
Experimental Analysis ◽  
State Of The Art ◽  
Neural Architecture ◽  
Model Compression ◽  
Processing Power ◽  
Benchmark Datasets ◽  
The Difference ◽  
And Performance ◽  
Consumption Constraints

Recent research on Convolutional Neural Networks focuses on how to create models with a reduced number of parameters and a smaller storage size while keeping the model’s ability to perform its task, allowing the use of the best CNN for automating tasks in limited devices, with reduced processing power, memory, or energy consumption constraints. There are many different approaches in the literature: removing parameters, reduction of the floating-point precision, creating smaller models that mimic larger models, neural architecture search (NAS), etc. With all those possibilities, it is challenging to say which approach provides a better trade-off between model reduction and performance, due to the difference between the approaches, their respective models, the benchmark datasets, or variations in training details. Therefore, this article contributes to the literature by comparing three state-of-the-art model compression approaches to reduce a well-known convolutional approach for object detection, namely YOLOv3. Our experimental analysis shows that it is possible to create a reduced version of YOLOv3 with 90% fewer parameters and still outperform the original model by pruning parameters. We also create models that require only 0.43% of the original model’s inference effort.

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