An ITS for Programming to Explore Practical Reasoning
Resumo
Resumo: Research on cognitive theories about programming learning suggests that experienced programmers solve problems by looking for previous solutions that are related with the new problem and that can be adapted to the current situation. On the other hand, an apprentice who does not have any previous programming experiences in mind can only appeal to the sentences of the language that he had learned so far. Inspired by these ideas, programming teachers have developed a pattern based programming instruction. In this model, learning can be seen as a process of pattern recognition, which compares experiences from the past with the current situation. In this work, we present a programming environment in which a student can program using a set of pedagogical patterns, i.e., elementary programming patterns recommended by a group of teachers. In this environment, while the student is editing a program, he can select and insert patterns in order to satisfy subgoals of a given problem. After having a compiled program, the student can submit it to a diagnosis system for detection of (i) possible errors and/or (ii) student's misconceptions on the use of patterns. Finally, in order to propose further extensions to the pattern based programming education, we analyze the programming reasoning in terms of a BDI architecture for rational agents in AI.
Abstract: Research on cognitive theories about programming learning suggests that experienced programmers solve problems by looking for previous solutions that are related with the new problem and that can be adapted to the current situation. On the other hand, an apprentice who does not have any previous programming experiences in mind can only appeal to the sentences of the language that he had learned so far. Inspired by these ideas, programming teachers have developed a pattern based programming instruction. In this model, learning can be seen as a process of pattern recognition, which compares experiences from the past with the current situation. In this work, we present a programming environment in which a student can program using a set of pedagogical patterns, i.e., elementary programming patterns recommended by a group of teachers. In this environment, while the student is editing a program, he can select and insert patterns in order to satisfy subgoals of a given problem. After having a compiled program, the student can submit it to a diagnosis system for detection of (i) possible errors and/or (ii) student's misconceptions on the use of patterns. Finally, in order to propose further extensions to the pattern based programming education, we analyze the programming reasoning in terms of a BDI architecture for rational agents in AI.
Abstract: Research on cognitive theories about programming learning suggests that experienced programmers solve problems by looking for previous solutions that are related with the new problem and that can be adapted to the current situation. On the other hand, an apprentice who does not have any previous programming experiences in mind can only appeal to the sentences of the language that he had learned so far. Inspired by these ideas, programming teachers have developed a pattern based programming instruction. In this model, learning can be seen as a process of pattern recognition, which compares experiences from the past with the current situation. In this work, we present a programming environment in which a student can program using a set of pedagogical patterns, i.e., elementary programming patterns recommended by a group of teachers. In this environment, while the student is editing a program, he can select and insert patterns in order to satisfy subgoals of a given problem. After having a compiled program, the student can submit it to a diagnosis system for detection of (i) possible errors and/or (ii) student's misconceptions on the use of patterns. Finally, in order to propose further extensions to the pattern based programming education, we analyze the programming reasoning in terms of a BDI architecture for rational agents in AI.
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PDFDOI: https://doi.org/10.5753/cbie.sbie.2004.208-217