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Display questions are more directive than authentic questions, and they promote greater ability in thinking by spurring students to have to back up their contribution. Utilising display questions that build on previous statements made by the students in a rephrased or simplified form facilitates the production of a more elaborate dialogue. [10]
Its systematic procedure is used to examine a text through questions and answers founded on the beliefs that all new knowledge is connected to prior knowledge, that all thinking comes from asking questions, and that asking one question should lead to asking further questions. [12] A Socratic seminar is not a debate.
Kinetic exchange models are multi-agent dynamic models inspired by the statistical physics of energy distribution, which try to explain the robust and universal features of income/wealth distributions. Understanding the distributions of income and wealth in an economy has been a classic problem in economics for more than a hundred years.
Assigning a question type to the question is a crucial task; the entire answer extraction process relies on finding the correct question type and hence the correct answer type. Keyword extraction is the first step in identifying the input question type. [14] In some cases, words clearly indicate the question type, e.g.,
Microsoft's ActiveX, Component Object Model (COM), Microsoft Transaction Server , Distributed Component Object Model (DCOM), Dynamic Data Exchange (DDE), Object Linking and Embedding (OLE), anonymous pipes, named pipes, Local Procedure Call, MailSlots, Message loop, MSRPC, .NET Remoting, and Windows Communication Foundation (WCF) Novell's SPX
Socratic questioning (or Socratic maieutics) [1] is an educational method named after Socrates that focuses on discovering answers by asking questions of students. According to Plato, Socrates believed that "the disciplined practice of thoughtful questioning enables the scholar/student to examine ideas and be able to determine the validity of those ideas". [2]
In artificial intelligence, a procedural reasoning system (PRS) is a framework for constructing real-time reasoning systems that can perform complex tasks in dynamic environments. It is based on the notion of a rational agent or intelligent agent using the belief–desire–intention software model .
Procedural knowledge (i.e., knowledge-how) is different from descriptive knowledge (i.e., knowledge-that) in that it can be directly applied to a task. [2] [4] For instance, the procedural knowledge one uses to solve problems differs from the declarative knowledge one possesses about problem solving because this knowledge is formed by doing. [5]