Measuring the Effectiveness of Holistic Approach to Teaching and Assessment used in Teaching a Decision Support Systems (DSS) Course

2019 ◽  
Author(s):  
ADIL FATHELRAHMAN
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Teaching Strategy ◽  
Holistic Approach ◽  
Learning Outcome ◽  
Course Assessment ◽  
Self Evaluation ◽  
Approach To Teaching ◽  
Assessment Strategies

<p>This paper investigates application of holistic approach to teaching and assessment and applies selected strategies to measure the effectiveness of such approach using results from teaching <strong>a Decision Support Systems (DSS) Course. Assessment is an integral part of the learning cycle and for any teaching strategy to succeed, teaching should be aligned with assessment strategies.</strong><strong></strong></p> <p><br></p><p>The strategies used in this paper for measurement of <strong>the effectiveness of the holistic approach </strong>are student ratings, self-evaluation, and learning outcome measures.<strong></strong></p>

scholarly journals Measuring the Effectiveness of Holistic Approach to Teaching and Assessment used in Teaching a Decision Support Systems (DSS) Course

2019 ◽  
Author(s):  
ADIL FATHELRAHMAN
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Teaching Strategy ◽  
Holistic Approach ◽  
Learning Outcome ◽  
Course Assessment ◽  
Self Evaluation ◽  
Approach To Teaching ◽  
Assessment Strategies

<p>This paper investigates application of holistic approach to teaching and assessment and applies selected strategies to measure the effectiveness of such approach using results from teaching <strong>a Decision Support Systems (DSS) Course. Assessment is an integral part of the learning cycle and for any teaching strategy to succeed, teaching should be aligned with assessment strategies.</strong><strong></strong></p> <p><br></p><p>The strategies used in this paper for measurement of <strong>the effectiveness of the holistic approach </strong>are student ratings, self-evaluation, and learning outcome measures.<strong></strong></p>

Decision support systems in integrated nematode management: the need for a holistic approach.

2021 ◽  
pp. 428-438
Author(s):  
Leendert P. G. Molendijk ◽  
Richard A. Sikora
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Holistic Approach ◽  
Distribution Patterns ◽  
Management Tool ◽  
Nematode Management ◽  
Effective Decision Support ◽  
Available Information ◽  
Rotation Crop

Abstract Farmers' decisions regarding the best approach to take in managing a nematode problem are often made just before the planting season begins and then at different stages in crop growth. In many cases an integrated nematode management (INM) system with multiple components is not considered due to lack of knowledge of the severity of the nematode problem, or the absence of an acceptable management tool such as a suitable resistant or tolerant cultivar, an appropriate non-host rotation crop or a suitable biocide for the situation. Seldom, if ever, are data on nematode population densities and distribution patterns across a field taken into consideration in the decision making process. This haphazard approach to INM is comparable to what could be called a 'take a chance' or 'shot in the dark' tactic which often results from the absence of effective decision support tools (DST) or decision support systems (DSS). This chapter discusses presently available information on different forms of DST and DSS as well as the developments that are required to streamline and improve INM.

Assigning Value to Clinical Information – A Major Limiting Factor in the Implementation of Decision-Support Systems

1996 ◽  
Vol 35 (01) ◽  
pp. 1-4 ◽  
Author(s):  
F. T. de Dombal
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Clinical Information ◽  
Limiting Factor ◽  
Major Difficulty ◽  
Computer Aided ◽  
Computer Aided Decision

AbstractThis paper deals with a major difficulty and potential limiting factor in present-day decision support - that of assigning precise value to an item (or group of items) of clinical information. Historical determinist descriptive thinking has been challenged by current concepts of uncertainty and probability, but neither view is adequate. Four equations are proposed outlining factors which affect the value of clinical information, which explain some previously puzzling observations concerning decision support. It is suggested that without accommodation of these concepts, computer-aided decision support cannot progress further, but if they can be accommodated in future programs, the implications may be profound.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

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