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These proceedings consist of 19 papers, which have been
peer-reviewed by international program committee and selected for
the 5th International Conference on Computer Science, Applied
Mathematics and Applications (ICCSAMA 2017), which was held on June
30-July 1, 2017 in Berlin, Germany. The respective chapters discuss
both theoretical and practical issues in connection with
computational methods and optimization methods for knowledge
engineering. The broad range of application areas discussed
includes network computing, simulation, intelligent and adaptive
e-learning, information retrieval, sentiment analysis, autonomous
underwater vehicles, social media analysis, natural language
processing, biomimetics in organizations, and cash management. In
addition to pure content, the book offers many inspiring ideas and
suggests new research directions, making it a valuable resource for
graduate students, Ph.D. students, and researchers in Computer
Science and Applied Mathematics alike.
Programming problems constitute a significant challenge for the
development of tutoring systems, because they can be solved in many
different ways. To help the student solve a programming problem
effectively, the tutoring system must be able to cover a large
space of possible solutions. If a student solution has
shortcomings, the system must be able to identify the reason why
that solution is not correct. In the state of the art, one of the
most promising approaches to modeling knowledge for tutoring
systems is the constraint-based technique. This approach uses
constraints to model a space of correct solutions, rather than
enumerating them. Nguyen-Thinh Le investigated the applicability of
this approach to develop tutoring systems for programming with the
focus on logic programming. In this book, he proposed the Weighted
Constraint-based Model (WCBM) for building Intelligent Tutoring
Systems. Constraint weights serve three purposes: they are used to
control the process of error diagnosis, to hypothesize the
student's intention, and to prioritize feedback messages according
to the severity of diagnosed errors.
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