Using a case study aimed at streamlining exam scheduling and distribution in a distance learning DL unit, we outline a sequential and non-linear four-step framework designed to reengineer processes. Our early involvement of all stakeholders, and our in-depth analysis and documentation of the existing process, allowed us to avoid the traditional pitfalls associated with business process reengineering BPR. Consequently, the outcome of our case study indicates a streamlined and efficient process with a higher faculty satisfaction at substantial cost reduction. This interest in rethinking processes and procedures is driven mainly by budget shortfalls, information technology infusion, and external pressures for greater accountability and responsiveness.
The value of q is within [0, 1], 0 indicates no spatial stratified heterogeneity, 1 indicates perfect spatial stratified heterogeneity.
The value of q indicates the percent of the variance of an attribute explained by the stratification. The q follows a noncentral F probability density function.
A hand map with different spatial patterns. Spatial interpolation[ edit ] Spatial interpolation methods estimate the variables at unobserved locations in geographic space based on the values at observed locations.
Basic methods include inverse distance weighting: Kriging is a more sophisticated method that interpolates across space according to a spatial lag relationship that has both systematic and random components. This can accommodate a wide range of spatial relationships for the hidden values between observed locations.
Kriging provides optimal estimates given the hypothesized lag relationship, and error estimates can be mapped to determine if spatial patterns exist.
Local regression and Regression-Kriging Spatial regression methods capture spatial dependency in regression analysisavoiding statistical problems such as unstable parameters and unreliable significance tests, as well as providing information on spatial relationships among the variables involved.
The estimated spatial relationships can be used on spatial and spatio-temporal predictions. Geographically weighted regression GWR is a local version of spatial regression that generates parameters disaggregated by the spatial units of analysis.
Spatial stochastic processes, such as Gaussian processes are also increasingly being deployed in spatial regression analysis. Model-based versions of GWR, known as spatially varying coefficient models have been applied to conduct Bayesian inference.
Factors can include origin propulsive variables such as the number of commuters in residential areas, destination attractiveness variables such as the amount of office space in employment areas, and proximity relationships between the locations measured in terms such as driving distance or travel time.
In addition, the topological, or connectiverelationships between areas must be identified, particularly considering the often conflicting relationship between distance and topology; for example, two spatially close neighborhoods may not display any significant interaction if they are separated by a highway.
After specifying the functional forms of these relationships, the analyst can estimate model parameters using observed flow data and standard estimation techniques such as ordinary least squares or maximum likelihood.
Competing destinations versions of spatial interaction models include the proximity among the destinations or origins in addition to the origin-destination proximity; this captures the effects of destination origin clustering on flows.
Computational methods such as artificial neural networks can also estimate spatial interaction relationships among locations and can handle noisy and qualitative data. This characteristic is also shared by urban models such as those based on mathematical programming, flows among economic sectors, or bid-rent theory.
An alternative modeling perspective is to represent the system at the highest possible level of disaggregation and study the bottom-up emergence of complex patterns and relationships from behavior and interactions at the individual level.
Two fundamentally spatial simulation methods are cellular automata and agent-based modeling. Cellular automata modeling imposes a fixed spatial framework such as grid cells and specifies rules that dictate the state of a cell based on the states of its neighboring cells.Data analysis is used in nearly every sector.
This includes the use of data analysis in hospitals to track the recovery of patients. This includes the use of data analysis in . The key to effective distance education is focusing on the needs of the learners, the requirements of the content and the constraints faced by the teacher, before selecting a delivery system.
However, its success depends on the integrated efforts of students, faculty, facilitators, support staff and administrators. October - Differences Between Traditional and Distance Education Academic Performances: A meta-analytic approach.
Mickey Shachar and Yoram Neumann Touro University International. Institutions: select this option to submit any materials related to Candidacy or Initial Accreditation. Select Self Study Report as the document type for the self study report and any related appendices or supplemental materials for the Biennial Evaluation visit, Candidacy visit, or the Initial Accreditation visit.
John Hattie developed a way of synthesizing various influences in different meta-analyses according to their effect size (Cohen’s d). In his ground-breaking study “Visible Learning” he ranked influences that are related to learning outcomes from very positive effects to very negative effects.
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Hattie found that the average effect size of all the interventions he studied was "How to" Guideline series is coordinated by Helen Mongan-Rallis of the Education Department at the University of Minnesota Duluth.