Preparing preservice teachers to use progress monitoring data for decision making

2008 ◽  
Author(s):  
Jean A. Boyer ◽  
Tiffany Andrews
Keyword(s):  
Decision Making ◽  
Preservice Teachers ◽  
Progress Monitoring ◽  
Monitoring Data

Decision-Making Accuracy of CBM Progress-Monitoring Data

2017 ◽  
Vol 36 (1) ◽  
pp. 74-81 ◽  
Author(s):  
John M. Hintze ◽  
Craig S. Wells ◽  
Amanda M. Marcotte ◽  
Benjamin G. Solomon
Keyword(s):  
Decision Making ◽  
Progress Monitoring ◽  
Monitoring Data ◽  
Standard Errors ◽  
Data Sets ◽  
Curriculum Based Measurement ◽  
Reading Growth ◽  
Intervention Model ◽  
Student Reading ◽  
Decision Making Rule

This study examined the diagnostic accuracy associated with decision making as is typically conducted with curriculum-based measurement (CBM) approaches to progress monitoring. Using previously published estimates of the standard errors of estimate associated with CBM, 20,000 progress-monitoring data sets were simulated to model student reading growth of two-word increase per week across 15 consecutive weeks. Results indicated that an unacceptably high proportion of cases were falsely identified as nonresponsive to intervention when a common 4-point decision rule was applied, under the context of typical levels of probe reliability. As reliability and stringency of the decision-making rule increased, such errors decreased. Findings are particularly relevant to those who use a multi-tiered response-to-intervention model for evaluating formative changes associated with instructional intervention and evaluating responsiveness to intervention across multiple tiers of intervention.

Decision-making accuracy of CBM progress-monitoring data

2008 ◽  
Author(s):  
John M. Hintze ◽  
Craig S. Wells ◽  
Amanda M. Marcotte
Keyword(s):  
Decision Making ◽  
Progress Monitoring ◽  
Monitoring Data

Estimating Trend in Progress Monitoring Data: A Comparison of Simple Line-Fitting Methods

1992 ◽  
Vol 21 (2) ◽  
pp. 300-312
Author(s):  
Richard Parker ◽  
Gerald Tindal ◽  
Stephanie Stein
Keyword(s):  
Progress Monitoring ◽  
Monitoring Data ◽  
Line Fitting

Multi-Zone Proportional Hazard Model for a Multi-Stage Degradation Process

2013 ◽  
Author(s):  
Lin Li ◽  
Zeyi Sun ◽  
Xinwei Xu ◽  
Kaifu Zhang
Keyword(s):  
Decision Making ◽  
Condition Monitoring ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Monitoring Data ◽  
Status Change ◽  
Multi Stage ◽  
And Performance

Conditional-based maintenance (CBM) decision-making is of high interests in recent years due to its better performance on cost efficiency compared to other traditional policies. One of the most respected methods based on condition-monitoring data for maintenance decision-making is Proportional Hazards Model (PHM). It utilizes condition-monitoring data as covariates and identifies their effects on the lifetime of a component. Conventional modeling process of PHM only treats the degradation process as a whole lifecycle. In this paper, the PHM is advanced to describe a multi-zone degradation system considering the fact that the lifecycle of a machine can be divided into several different degradation stages. The methods to estimate reliability and performance prognostics are developed based on the proposed multi-zone PHM to predict the remaining time that the machine stays at the current stage before transferring into the next stage and the remaining useful life (RUL). The results illustrate that the multi-zone PHM effectively monitors the equipment status change and leads to a more accurate RUL prediction compared with traditional PHM.

scholarly journals Pre-service Teachers’ Interpretation of CBM Progress Monitoring Data

2017 ◽  
Vol 32 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Dana L. Wagner ◽  
Stephanie M. Hammerschmidt-Snidarich ◽  
Christine A. Espin ◽  
Kathleen Seifert ◽  
Kristen L. McMaster
Keyword(s):  
Progress Monitoring ◽  
Monitoring Data

Framing School Mental Health Services Within a Dual-Factor Model of Mental Health

2020 ◽  
pp. 40-60
Author(s):  
Beth Doll ◽  
Evan H. Dart ◽  
Prerna G. Arora ◽  
Tai A. Collins
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Mental Disorders ◽  
Field Test ◽  
Universal Screening ◽  
Progress Monitoring ◽  
Factor Model ◽  
School Mental Health ◽  
Well Being ◽  
Complete Mental Health

This chapter proposes a reimagined dual-factor, multitiered system of support (MTSS) that targets students’ complete mental health by simultaneously diminishing symptoms of mental disorders and enhancing attributes of well-being. Examples of assessments and interventions are cited to show that our existing knowledge base includes examples of universal screening, progress monitoring, and interventions that address complete mental health. An argument is made for more research to build a broader base of assessments of well-being for progress monitoring and universal screening and to develop and field test decision-making protocols to identify students’ complete mental health needs and align services with these needs. The chapter concludes that important first steps toward dual-factor MTSS have already been taken.

Response to Intervention

Education ◽  
2013 ◽  
Author(s):  
Amy Eppolito ◽  
Kathryn White ◽  
Janette Klingner
Keyword(s):  
Decision Making ◽  
Special Education ◽  
Learning Disabilities ◽  
Response To Intervention ◽  
General Education ◽  
Progress Monitoring ◽  
The United States ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Rti Model

Response to intervention (RTI) is a comprehensive, systematic approach to teaching and learning designed to monitor academic and behavioral progress for all students, provide early interventions of increasing intensity to struggling learners, and potentially identify learners with more significant learning disabilities. The model is implemented with multitiered instruction, intervention, and assessment. The key components of the RTI model include (1) high-quality instruction matched to the needs of students, (2) evidence-based interventions of increasing intensity, (3) ongoing progress monitoring, and (4) data-driven decision making. Components of the model, such as data-driven decision making and multitiered instruction, have been studied for the past few decades, but the model as an integrated whole has been developed more recently. One catalyst for increased research and interest in RTI has been a change in federal legislation in the United States. The most recent reauthorization of the Individuals with Disabilities Education Improvement Act (IDEA) in 2004 permits the RTI model to be implemented as an alternative means to identify students with learning disabilities (LDs). These amendments to IDEA stipulate that the RTI process may be used to determine if a child is responding to research-based instruction and intervention as part of the special education evaluation process. Although driven by special education policy, RTI has been lauded as an instructional model that can improve general education overall and for special populations. However, critiques of the model argue that it has been implemented with limited research, resources, and funding and may not be valid for identifying LDs. Some experts question the psychometric validity of the model and promote using multiple forms of assessment, including more traditional standardized psycho-educational tests, in combination with RTI when evaluating students for possible LDs.

scholarly journals Protection of Service-Oriented Environments Serving Critical Infrastructures

Inventions ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 62
Author(s):  
Dimosthenis Kyriazis
Keyword(s):  
Decision Making ◽  
High Performance ◽  
Performance Monitoring ◽  
Critical Infrastructure ◽  
Failure Detection ◽  
False Positives ◽  
Edge Computing ◽  
Monitoring Data ◽  
Critical Infrastructures ◽  
Service Oriented

The emergence of service-oriented architectures has driven the shift towards a service-oriented paradigm, which has been adopted in several application domains. The advent of cloud computing facilities and recently of edge computing environments has increased the aforementioned paradigm shift towards service provisioning. In this context, various “traditional” critical infrastructure components have turned to services, being deployed and managed on top of cloud and edge computing infrastructures. However, the latter poses a specific challenge: the services of the critical infrastructures within and across application verticals/domains (e.g., transportation, health, industrial venues, etc.) need to be continuously available with near-zero downtime. In this context, this paper presents an approach for high-performance monitoring and failure detection of critical infrastructure services that are deployed in virtualized environments. The failure detection framework consists of distributed agents (i.e., monitoring services) to ensure timely collection of monitoring data, while it is enhanced with a voting algorithm to minimize the case of false positives. The goal of the proposed approach is to detect failures in datacenters that support critical infrastructures by targeting both the acquisition of monitoring data in a performant way and the minimization of false positives in terms of potential failure detection. The specific approach is the baseline towards decision making and triggering of actions in runtime to ensure service high availability, given that it provides the required data for decision making on time with high accuracy.

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