scholarly journals How to Apply Problem-Based Learning in Medical Education? A Critical Review

2019 ◽  
Vol 2 (1) ◽  
pp. 14-18
Author(s):  
Said Said Elshama ◽  
◽  
Keyword(s):  
Medical Education ◽  
Differential Diagnosis ◽  
Prior Knowledge ◽  
Problem Based Learning ◽  
History Taking ◽  
Construction Of Knowledge ◽  
Knowledge Activation ◽  
Prior Knowledge Activation ◽  
Apply Problem ◽  
Oriented Approach

Problem-based learning (PBL) is a cornerstone of modern medical education. Principles of PBL are the construction of knowledge, prior knowledge activation, organization of knowledge, elaboration of knowledge, stepwise transfer across contexts and cooperation with other learners. It provides the ability to identify the knowledge, generate and analyze hypotheses that lead to the differential diagnosis of the case according to the complaint of the patient by using history taking, physical exam, and investigations. Application of any innovation such as PBL faces many challenges and obstacles that are related to the students, tutors, learning environment and other stakeholders. We can overcome these obstacles by more training sessions for tutors and students. In addition, the construction of PBL curriculum should be based on a community-oriented approach because it depends on the priorization of common health problems in the surrounding community.

scholarly journals The influence of prior knowledge on the retrieval-directed function of note taking in prior knowledge activation

2011 ◽  
Vol 81 (2) ◽  
pp. 274-291 ◽  
Author(s):  
Sandra A. J. Wetzels ◽  
Liesbeth Kester ◽  
Jeroen J. G. van Merriënboer ◽  
Nick J. Broers
Keyword(s):  
Prior Knowledge ◽  
Note Taking ◽  
Knowledge Activation ◽  
Prior Knowledge Activation

Prior Knowledge Activation and the Comprehension of Compatible and Incompatible Text

1985 ◽  
Vol 20 (4) ◽  
pp. 420 ◽  
Author(s):  
Donna E. Alvermann ◽  
Lynn C. Smith ◽  
John E. Readence
Keyword(s):  
Prior Knowledge ◽  
Knowledge Activation ◽  
Prior Knowledge Activation

Prior Knowledge Activation in Design and in Practice

2015 ◽  
Vol 64 (1) ◽  
pp. 478-497 ◽  
Author(s):  
Courtney Hattan ◽  
Lauren M. Singer ◽  
Sandra Loughlin ◽  
Patricia Ann Alexander
Keyword(s):  
Prior Knowledge ◽  
Knowledge Activation ◽  
Prior Knowledge Activation

Computational Aspects of the Intelligent Tutoring System MetaTutor

2012 ◽  
pp. 247-260 ◽  
Author(s):  
Mihai Lintean ◽  
Vasile Rus ◽  
Zhiqiang Cai ◽  
Amy Witherspoon-Johnson ◽  
Arthur C. Graesser ◽  
...  
Keyword(s):  
Natural Language ◽  
Prior Knowledge ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Metacognitive Strategy ◽  
Tutoring System ◽  
Knowledge Activation ◽  
Prior Knowledge Activation ◽  
Goal Assessment ◽  
Dialogue Mechanism

We present in this chapter the architecture of the intelligent tutoring system MetaTutor that trains students to use metacognitive strategies while learning about complex science topics. The emphasis of this chapter is on the natural language components. In particular, we present in detail the natural language input assessment component used to detect students’ mental models during prior knowledge activation, a metacognitive strategy, and the micro-dialogue component used during sub-goal generation, another metacognitive strategy in MetaTutor. Sub-goal generation involves sub-goal assessment and feedback provided by the system. For mental model detection from prior knowledge activation paragraphs, we have experimented with three benchmark methods and six machine learning algorithms. Bayes Nets, in combination with a word-weighting method, provided the best accuracy (76.31%) and best human-computer agreement scores (kappa=0.63). For sub-goal assessment and feedback, a taxonomy-driven micro-dialogue mechanism yields very good to excellent human-computer agreement scores for sub-goal assessment (average kappa=0.77).

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