scholarly journals Recurrence Quantification Analysis as a Methodological Innovation for School Improvement Research

Author(s):  
Arnoud Oude Groote Beverborg ◽  
Maarten Wijnants ◽  
Peter J. C. Sleegers ◽  
Tobias Feldhoff

AbstractSchool improvement and educational change can be facilitated by learning through reflection, as this allows teachers to discover ways to develop and adapt to change. Higher levels of engagement in reflection have been found to be beneficial, but it is unclear from which everyday routine in engagement in reflection higher levels arise, and thus whether occasions to make knowledge explicit should be organized with a certain constancy. In this study, we therefore used a conceptualization of teacher learning through reflection as a situated and dynamic process in which available environmental information, learning activities, and professional practices are interconnected, and co-develop. Seventeen Dutch Vocational Education and Training teachers participated over a period of 5 months. We explored the use of daily and monthly logs as measurement instruments and Recurrence Quantification Analysis (RQA) as the analysis technique applied to the time-series generated from the daily logs. The findings indicated that teachers who make information from their working environment explicit more are also able to make new insights explicit more. The routine with which teachers make information explicit was found to be mostly unrelated to making new insights explicit. To reach their levels of engagement in reflection, some teachers organized opportunities to reflect with determined intervals, others seemed to recognize those opportunities as the working environment provided them, and some used a combination thereof. Moreover, the use of daily and monthly logs seemed to fit better to some participants than others. Only sometimes does organizing constancy in engagement in reflection seem to relate to the levels thereof. This study provides an example of how logs and RQA can be adopted to tap into professional learning as a dynamic and situated process in support of school improvement and educational change.

Author(s):  
João A. Bastos

Recurrence quantification analysis is a nonlinear time series analysis technique that detects deterministic dependencies in time series. This technique is particularly appropriate for modeling financial time series since it requires no assumptions on stationarity, statistical distribution, and minimum number of observations. This chapter illustrates two applications of recurrence quantification analysis to financial data: a set of international stock indices, and zero-coupon yields of US government bonds.


2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
Antonio Novellino ◽  
José-Manuel Zaldívar

The combination of a nonlinear time series analysis technique, Recurrence Quantification Analysis (RQA) based on Recurrence Plots (RPs), and traditional statistical analysis for neuronal electrophysiology is proposed in this paper as an innovative paradigm for studying the variation of spontaneous electrophysiological activity of in vitro Neuronal Networks (NNs) coupled to Multielectrode Array (MEA) chips. Recurrence, determinism, entropy, distance of activity patterns, and correlation in correspondence to spike and burst parameters (e.g., mean spiking rate, mean bursting rate, burst duration, spike in burst, etc.) have been computed to characterize and assess the daily changes of the neuronal electrophysiology during neuronal network development and maturation. The results show the similarities/differences between several channels and time periods as well as the evolution of the spontaneous activity in the MEA chip. RPs could be used for graphically exploring possible neuronal dynamic breaking/changing points, whereas RQA parameters are suited for locating them. The combination of RQA with traditional approaches improves the identification, description, and prediction of electrophysiological changes and it will be used to allow intercomparison between results obtained from different MEA chips. Results suggest the proposed processing paradigm as a valuable tool to analyze neuronal activity for screening purposes (e.g., toxicology, neurodevelopmental toxicology).


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