scholarly journals Predictive Machine Learning Approach for Complex Problem Solving Process Data Mining

2021 ◽  
Vol 18 (1) ◽  
pp. 45-63
Author(s):  
Aleksandar Pejić ◽  
Piroska Stanić Molcer
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Problem Solving ◽  
Complex Problem ◽  
Learning Approach ◽  
Complex Problem Solving ◽  
Process Data ◽  
Machine Learning Approach ◽  
Problem Solving Process

Game Design as a Complex Problem Solving Process

2017 ◽  
pp. 217-233 ◽  
Author(s):  
Mete Akcaoglu ◽  
Antonio P. Gutierrez ◽  
Charles B. Hodges ◽  
Philipp Sonnleitner
Keyword(s):  
Problem Solving ◽  
Middle School Students ◽  
Game Design ◽  
Formal Education ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
School Students ◽  
Daily Lives ◽  
Problem Solving Process ◽  
System Knowledge

Problem solving is one of the most essential skills for individuals to be successful at their daily lives and careers. When problems become complex, solving them involves identifying relationships among a multitude of interrelated variables, to achieve multiple different possible solutions. Teaching Complex Problem Solving (CPS) skills in formal education contexts is challenging. In this research, we examined if through an innovative game-design course middle school students improved in their CPS skills. Our results showed that students showed significant improvements in their CPS skills, especially in terms of system exploration, t(10) = 2.787, p = .019; system knowledge, t(10) = 2.437, p = .35; system application, t(10) = 2.472, p = .033. In addition, there was a statistically significant change in students' interest for CPS after attending the GDL program, t(6) = 3.890, p = .008. We discuss implications regarding use of game-design tasks as contexts to teach CPS skills in formal and informal educational contexts.

Sarcasm Detection Algorithms

2018 ◽  
Vol 12 (03) ◽  
pp. 457-478 ◽  
Author(s):  
Uraz Yavanoglu ◽  
Taha Yasin Ibisoglu ◽  
Setra Genyang Wıcana
Keyword(s):  
Machine Learning ◽  
Opinion Mining ◽  
Learning Approach ◽  
Semantic Features ◽  
Process Data ◽  
Detection Algorithms ◽  
Lexical Feature ◽  
Machine Learning Approach ◽  
The Future ◽  
Semantic Properties

In this paper, we want to review one of the challenging problems for the opinion mining task, which is sarcasm detection. To be able to do that, many researchers tried to explore such properties in sarcasm like theories of sarcasm, syntactical properties, psycholinguistic of sarcasm, lexical feature, semantic properties, etc. Studies conducted within last 15 years have not only made progress in semantic features but have also shown increasing amounts of methods of analysis using a machine-learning approach to process data. Therefore, this paper will try to explain the most currently used methods to detect sarcasm. Lastly, we will present a result of our finding, which might help other researchers to gain a better result in the future.

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