scholarly journals A Case Based Methodology for Problem Solving Aiming at Knee Osteoarthritis Detection

2016 ◽  
pp. 274-284
Author(s):  
Marisa Esteves ◽  
Henrique Vicente ◽  
José Machado ◽  
Victor Alves ◽  
José Neves
Keyword(s):  
Problem Solving ◽  
Knee Osteoarthritis ◽  
Case Based

scholarly journals Asynchronous Online Discussions During Case-Based Learning: A Problem-Solving Process

2020 ◽  
Vol 24 (4) ◽  
Author(s):  
Adrie A Koehler ◽  
Zui Cheng ◽  
Holly Fiock ◽  
Shamila Janakiraman ◽  
Huanhuan Wang
Keyword(s):  
Problem Solving ◽  
Online Discussions ◽  
Future Research ◽  
Case Based Learning ◽  
Multiple Case ◽  
Asynchronous Online Discussions ◽  
Expected Benefits ◽  
Problem Solving Process ◽  
Case Based

Asynchronous discussions are typically considered an essential aspect of online case-based learning. While instructors implement discussions to support a variety of instructional purposes during case-based learning (e.g., facilitate students’ sense making, prompt the consideration of diverse perspectives, debate complex topics), whether students receive the expected benefits is unclear, and little research has considered how students intentionally participate in discussions to support their learning during case-based learning. At the same time, students’ participation in asynchronous online discussions represents a complex endeavor. That is, students must make several decisions regarding how to effectively participate, while simultaneously experiencing several challenges. The purpose of this exploratory multiple-case study was to consider the experiences of six graduate students participating in asynchronous online discussions as a part of a case-based course. By analyzing these experiences, we were able to conceptualize students’ navigation of an asynchronous online discussion as a problem-solving process and consider individual problem-solving approaches. Results indicate that students relied primarily on instructors to determine the purpose of their discussion participation, expressed differing levels of value for participating in discussions, adopted a variety of strategies to meet discussion participation goals, and assessed their participation efforts mainly based on guidelines set by the instructor. We offer suggestions for effectively designing and facilitating asynchronous online discussions and discuss areas needing future research.

Developing Preservice Teachers’ Instructional Design Skills Through Case-Based Instruction: Examining the Impact of Discussion Format

2018 ◽  
Vol 70 (4) ◽  
pp. 319-334 ◽  
Author(s):  
Adrie A. Koehler ◽  
Peggy A. Ertmer ◽  
Timothy J. Newby
Keyword(s):  
Preservice Teachers ◽  
Problem Solving ◽  
Class Discussion ◽  
Problem Solving Skills ◽  
Cognitive Levels ◽  
Design Skills ◽  
Based Instruction ◽  
The Impact ◽  
Active Facilitator ◽  
Case Based

For more than 100 years, case-based instruction (CBI) has been an effective instructional method for building problem-solving skills in learners. While class discussion is often included as part of the CBI learning process, the impact on learning is unclear. Furthermore, little research has focused on how specific facilitation strategies influence the development of learners’ problem-solving skills. This study examined the impact of case discussion facilitation strategies on the development of preservice teachers’ problem-solving skills. Specifically, two discussion formats were compared: instructor-facilitated (class discussions guided by instructor-crafted prompts and an active facilitator) and instructor-supported (discussions guided by instructor-crafted prompts only). Results indicated that while preservice teachers’ problem-solving skills improved in both sections of the course, individuals in the instructor-facilitated section demonstrated significantly higher scores on course activities and designed instructional activities at higher cognitive levels compared with preservice teachers who participated in the instructor-supported discussions. Results underscore the importance of an active facilitator in CBI.

A Blackboard Architecture to Support Case-Based Reasoning in Mechanical Design

1992 ◽  
Author(s):  
Theodore Bardsz ◽  
Ibrahim Zeid
Keyword(s):  
Problem Solving ◽  
Engineering Design ◽  
Mechanical Design ◽  
Maximum Amount ◽  
Design Solution ◽  
Case Based Reasoning ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Design Plan ◽  
Case Based

Abstract One of the most significant issues in applying case-based reasoning (CBR) to mechanical design is to integrate previously unrelated design plans towards the solution of a new design problem. The total design solution (the design plan structure) can be composed of both retrieved and dynamically generated design plans. The retrieved design plans must be mapped to fit the new design context, and the entire design plan structure must be evaluated. An architecture utilizing opportunistic problem solving in a blackboard environment is used to map and evaluate the design plan structure effectively and successfuly. The architecture has several assets when integrated into a CBR environment. First, the maximum amount of information related to the design is generated before any of the mapping problems are addressed. Second, mapping is preformed as just another action toward the evaluation of the design plan. Lastly, the architecture supports the inclusion of memory elements from the knowledge base in the design plan structure. The architecture is implemented using the GBB system. The architecture is part of a newly developed CBR System called DEJAVU. The paper describes DEJAVU and the architecture. An example is also included to illustrate the use of DEJAVU to solve engineering design problems.

Case-Based Reasoning and Some Typical Applications

2014 ◽  
pp. 229-267 ◽  
Author(s):  
Durga Prasad Roy ◽  
Baisakhi Chakraborty
Keyword(s):  
Problem Solving ◽  
Machine Intelligence ◽  
Case Based Reasoning ◽  
Engineering System ◽  
Yale University ◽  
Control Pattern ◽  
Clustering And Classification ◽  
How People Learn ◽  
Case Based

Case-Based Reasoning (CBR) arose out of research into cognitive science, most prominently that of Roger Schank and his students at Yale University, during the period 1977–1993. CBR may be defined as a model of reasoning that incorporates problem solving, understanding, and learning, and integrates all of them with memory processes. It focuses on the human problem solving approach such as how people learn new skills and generates solutions about new situations based on their past experience. Similar mechanisms to humans who intelligently adapt their experience for learning, CBR replicates the processes by considering experiences as a set of old cases and problems to be solved as new cases. To arrive at the conclusions, it uses four types of processes, which are retrieve, reuse, revise, and retain. These processes involve some basic tasks such as clustering and classification of cases, case selection and generation, case indexing and learning, measuring case similarity, case retrieval and inference, reasoning, rule adaptation, and mining to generate the solutions. This chapter provides the basic idea of case-based reasoning and a few typical applications. The chapter, which is unique in character, will be useful to researchers in computer science, electrical engineering, system science, and information technology. Researchers and practitioners in industry and R&D laboratories working in such fields as system design, control, pattern recognition, data mining, vision, and machine intelligence will benefit.

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