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Intelligent Environment for Extending Python Programming Knowledge via Self-learning
Krejčí, Jan ; Nosko, Svetozár (referee) ; Smrž, Pavel (advisor)
This thesis aims to create an intelligent environment for extending the knowledge of Python programming through self-study. A key element of this work is the implementation of feedback mechanisms. For this purpose, the capabilities and limitations of large language models have been analyzed. The developed system uses classification models to provide personalized feedback based on the analysis of student projects. The system has been deployed and tested in the Scripting Languages course at FIT BUT and received positive feedback from students. The outcome presents a comprehensive and functional system that has fulfilled its original intention and contributed to a more effective and interactive Python programming education process.
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Teaching Advanced Python through Automatic Feedback to Student Codes
John, Petr ; Dočekal, Martin (referee) ; Smrž, Pavel (advisor)
This bachelor thesis focuses on the topic of teaching Python programming language assisted by automated system that can provide feedback to submitted solutions. The goal of this thesis was creation of automated system that could evaluate student solutions and provide feedback. The main emphasis was on provided feedback and options for limiting resources that can be used. Created system provides feedback based on the analysis of abstract syntax trees assembled from submitted solutions, allows an administrator to attach tips from third party programs and set restrictions on resources, libraries and functions, that can be used. System was used during summer semester in ISJ course and 60 % of students improved their solution based on feedback given by system. This suggests that created system can be used during Python tuition.
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Predictive Modelling with Python
Duda, Jan ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
The main goal of this bachelor thesis is get to know with the data mining and its domain, also with the Knowledge discovery in databases process. It shows the most importnant approaches, which are implemented in Python language afterwards. The case study contains the prediction of index S&P 500 describing stock market developments on the US stock exchange. Both classification and regression models are used for the forecasting. Model evaluation is reached by the Monte Carlo experimental method.
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Teaching Advanced Python through Automatic Feedback to Student Codes
John, Petr ; Dočekal, Martin (referee) ; Smrž, Pavel (advisor)
This bachelor thesis focuses on the topic of teaching Python programming language assisted by automated system that can provide feedback to submitted solutions. The goal of this thesis was creation of automated system that could evaluate student solutions and provide feedback. The main emphasis was on provided feedback and options for limiting resources that can be used. Created system provides feedback based on the analysis of abstract syntax trees assembled from submitted solutions, allows an administrator to attach tips from third party programs and set restrictions on resources, libraries and functions, that can be used. System was used during summer semester in ISJ course and 60 % of students improved their solution based on feedback given by system. This suggests that created system can be used during Python tuition.
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Predictive Modelling with Python
Duda, Jan ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
The main goal of this bachelor thesis is get to know with the data mining and its domain, also with the Knowledge discovery in databases process. It shows the most importnant approaches, which are implemented in Python language afterwards. The case study contains the prediction of index S&P 500 describing stock market developments on the US stock exchange. Both classification and regression models are used for the forecasting. Model evaluation is reached by the Monte Carlo experimental method.
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Plagiarism Recognizer in Python Source Code
Bártíková, Pavlína ; Soukup, Ondřej (referee) ; Křivka, Zbyněk (advisor)
This thesis deals with the programming language Python and with development of the application that evaluates the similarity of the specified source codes in this programming language. In addition to comparison of comments, the program uses several comparison methods applied to a sequence of tokens that are created from the specified source codes. Namely, the Levenshtein distance, the longest common subsequence and the frequency of tokens. The thesis also includes the results of testing the program on real data. The application is designed to control the plagiarism in the source codes of the school projects written in the programming language Python.
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