Modeling as Constrained Problem Solving: An Empirical Study of the Data Modeling Process

1995 ◽  
Vol 41 (3) ◽  
pp. 419-434 ◽  
Author(s):  
Ananth Srinivasan ◽  
Dov Te’eni
Keyword(s):  
Problem Solving ◽  
Empirical Study ◽  
Data Modeling ◽  
Modeling Process ◽  
Constrained Problem

scholarly journals Strategies in public service interpreting

Interpreting ◽  
2017 ◽  
Vol 19 (1) ◽  
pp. 118-141 ◽  
Author(s):  
Marta Arumi Ribas ◽  
Mireia Vargas-Urpi
Keyword(s):  
Problem Solving ◽  
Qualitative Study ◽  
Empirical Study ◽  
Present Article ◽  
Public Service ◽  
Video Recordings

Strategies have been far more widely researched in conference interpreting than in the interactional setting of public service interpreting (PSI), although studies of the latter by Wadensjö and other authors suggest a strategic rationale for certain types of rendition (especially non-renditions). The present article describes an exploratory, qualitative study, based on roleplay, to identify strategies in PSI: the roleplays were designed to incorporate a variety of ‘rich points’, coinciding with peak demands on the interpreter’s problem-solving capacities and therefore particularly relevant to empirical study of interpreting strategies. Five interpreter-mediators with the Chinese–Spanish/Catalan language combination were each asked to interpret three different dialogues, in which the primary participants’ input was a re-enactment of real situations. Analysis of the transcribed video recordings was complemented by a preliminary questionnaire and by retrospective interviews with the interpreters. Their strategies, classified according to whether the problems concerned were essentially linguistic or involved the dynamics of interaction, in some cases reflect priorities typically associated with intercultural mediation. The advantages and limitations of using ‘rich points’ and roleplays in the study of interpreting strategies are briefly discussed

scholarly journals Adaptabilidade de Objetos de Aprendizagem usando Calibragem e Sequenciamento Adaptativo de Exercícios

2018 ◽  
Vol 26 (1) ◽  
pp. 70
Author(s):  
Rômulo César Silva ◽  
Alexandre Ibrahim Direne ◽  
Diego Marczal ◽  
Ana Carla Borille ◽  
Paulo Ricardo Bittencourt Guimarães ◽  
...  
Keyword(s):  
Problem Solving ◽  
Empirical Study ◽  
Cognitive Skills ◽  
Learning Objects ◽  
Skill Level ◽  
Problem Solution ◽  
Algebraic Expressions ◽  
Student Skill ◽  
Definition Of ◽  
Two Parameters

The work approaches theoretical and implementation issues of a framework for creating and executing Learning Objects (LOs) where problem-solving tasks are ordered according to the matching of two parameters, both calculated automatically: (1) student skill level and (2) problem solution difficulty. They are formally defined as algebraic expressions. The definition of skill level is achieved through a rating-based measure that resembles the ones of game mastery scales, while the solution difficulty is based on mistakes and successes of learners to deal with the problem. An empirical study based on existing students data demonstrated the suitability of the formulas. Besides, the motivational aspects of learning are considered in depth. In this sense, it is important to propose activities according to the student’s level of expertise, which is achieved through presenting students with exercises that are compatible with the difficulty degree of their cognitive skills. Also, the results of an experiment conducted with four highschool classes using the framework for the domain of logarithmic properties are presented.

scholarly journals The Cognitive Underpinnings of Multiply-Constrained Problem Solving

2019 ◽  
Author(s):  
Derek Ellis ◽  
Gene Arnold Brewer ◽  
Matthew Kyle Robison
Keyword(s):  
Working Memory ◽  
Problem Solving ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Attention Control ◽  
Crystallized Intelligence ◽  
Constrained Problems ◽  
Constrained Problem ◽  
Analytical Strategies ◽  
Fluid And Crystallized Intelligence

An individual encounters problem of varying difficulty every day. Each problem may include a different number of constraints. Multiply-constrained problems, such as the compound remote associates, are commonly used to study problem solving. Since their development, multiply-constrained problems have been related to creativity and insight. Moreover, research has investigated the cognitive abilities underlying problem solving abilities. In the present study we sought to fully evaluate a range of cognitive abilities (i.e., working memory, attention control, episodic and semantic memory, and fluid and crystallized intelligence) previously associated with multiply-constrained problem solving. Additionally, we sought to determine whether problem solving ability and strategies (analytical or insightful) were task specific or domain general through the use of novel problem solving tasks (TriBond and Location Bond). Multiply-constrained problem solving abilities were shown to be domain general, solutions derived through insightful strategies were more often correct than those derived through analytical strategies, and crystallized intelligence was the only cognitive ability that provided unique predictive value after accounting for all other abilities.

Why does problem-oriented mathematics education not succeed in an eighth grade? An insight in an empirical study

2019 ◽  
pp. 111-119
Author(s):  
Heike Hagelgans
Keyword(s):  
Problem Solving ◽  
Qualitative Study ◽  
Empirical Study ◽  
Mathematics Teaching ◽  
Research Method ◽  
Eighth Grade ◽  
Specific Class ◽  
School Class ◽  
Teaching Concept ◽  
Research Findings

Based on current research findings on the possibilities of integration of problem solving into mathematics teaching, the difficulties of pupils with problem solving tasks and of teachers to get started in problem solving, this article would like to show which concrete difficulties delayed the start of the implementation of a generally problem-oriented mathematics lesson in an eighth grade of a grammar school. The article briefly describes the research method of this qualitative study and identifies and discusses the difficulties of problem solving in the examined school class. In a next step, the results of this study are used to conceive a precise teaching concept for this specific class for the introduction into problem-oriented mathematics teaching.

Perceptions of F/OSS Community

2007 ◽  
pp. 68-94
Author(s):  
Andrew Schofield ◽  
Grahame S. Cooper
Keyword(s):  
Social Interaction ◽  
Problem Solving ◽  
Empirical Study ◽  
Relevant Literature ◽  
Common Interest ◽  
Development Model ◽  
Future Research ◽  
Community Members ◽  
Support Provision

The role of online communities is a key element in free and open source software (F/OSS) and a primary factor in the success of the F/OSS development model. F/OSS communities are inter-networked groups of people who are united by a common interest in F/OSS software. This chapter addresses holistic issues pertaining to member participation in F/OSS communities, specifically considering their reasons and motivation for participating. It collates the relevant literature on F/OSS community participation and presents the results of an empirical study into members’ perceptions of their own participation. We identify primary reasons for participation such as problem solving, support provision, and social interaction and rank their importance by the participants’ preferences. We then separate development and support activities and compare the community members’ perceptions of the two. Finally, we draw conclusions and discuss the potential for future research in this area.

Data Modeling

2011 ◽  
pp. 234-248
Author(s):  
Clare Atkins
Keyword(s):  
Problem Solving ◽  
Undergraduate Students ◽  
Data Structures ◽  
Information Needs ◽  
Data Modeling ◽  
Problem Solving Skills ◽  
Complex Process ◽  
Essential Components ◽  
Critical Skills ◽  
Tools And Techniques

Despite extensive changes in technology and methodology, anecdotal and empirical evidence (e.g., Davis et al., 1997) consistently suggests that communication and problem-solving skills are fundamental to the success of an IT professional. As two of the most valued skills in an IT graduate, they should be essential components of an effective education program, regardless of changes in student population or delivery mechanisms. While most educators would concur with this view, significantly more emphasis is generally placed on teaching the tools and techniques that students will require in their future careers, and a corresponding amount of energy is expended in attempting to identify what those tools and techniques might be. In contrast, successful problem solving is often seen either as an inherent capability that some students already possess or as a skill that some will magically acquire during the course of their studies. Data modeling as an activity, by which we mean the gathering and analysis of users’ information needs and their representation in an implementable design, is largely one of communication and problem solving and, consequently, provides an excellent opportunity for explicitly teaching these skills. Data modeling is generally considered to be one of the more difficult skills to teach (e.g., Hitchman, 1995; Pletch, 1989), particularly if the student has no previous understanding of physical data structures (de Carteret & Vidgen, 1995). The essential constructs, such as entities, attributes or objects, may be elegant in their powerful simplicity, but their combination into a useful design is a complex process of categorization in which there is “considerable room for choice and creativity in selecting the most useful classification” (Simsion, 1994 p.82). Data modeling requires not only the ability to communicate about and to solve a problem, but also to create possible solutions and then choose between them. Herein lies the difficulty. It is not enough to learn what the different constructs are, or even to study simple textbook examples of how to put them together. The student must really understand the problem, be able to create and recognize a number of possible ways in which the problem can be solved, and then exercise considerable critical skills in choosing between them. This chapter examines these issues and describes various ways in which final-year undergraduate students, taking a specialist module in data modeling, have been encouraged to develop, and have confidence in, their creative and critical ability to solve problems in a disciplined and systematic way.

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