Method Arguments Research Articles

TỔ CHỨC HOẠT ĐỘNG DẠY HỌC THEO PHƯƠNG PHÁP NGHIÊN CỨU KHOA HỌC NHẰM PHÁT TRIỂN NĂNG LỰC KHOA HỌC TỰ NHIÊN CHO HỌC SINH

From 2021 to 2022, 6th graders will begin to study new subjects to develop comprehensive skills for students. An integrated subject of several fields, Physics, Chemistry, Biology and Earth Science, will be taught instead of teaching each separately as in the past to enhance scientific research skills for students. Hence, teachers need to alter and improve how they provide knowledge, innovate, and create their teaching modules to achieve the program's outcome standards and specific requirements. After three years of operating the research teaching method at Vinschool (Times City), the favorite subject surveys and students' academic results indicate that students could apply science skills learning from the subject in handling the actual problems effectively. The shreds of evidence related to international STEM competitions were also provided to support the method's argument. This study is supposed to give a realistic view of the experience of teaching science and organizing teaching activities according to the scientific research method of a teacher who has successfully overcome from a knowledge concentration program of the Ministry of Education to a significant improvement in comprehension skills program designed by Cambridge University.

Selective specialization for object-oriented languages

Dynamic dispatching is a major source of run-time overhead in object-oriented languages, due both to the direct cost of method lookup and to the indirect effect of preventing other optimizations. To reduce this overhead, optimizing compilers for object-oriented languages analyze the classes of objects stored in program variables, with the goal of bounding the possible classes of message receivers enough so that the compiler can uniquely determine the target of a message send at compile time and replace the message send with a direct procedure call. Specialization is one important technique for improving the precision of this static class information: by compiling multiple versions of a method, each applicable to a subset of the possible argument classes of the method, more precise static information about the classes of the method's arguments is obtained. Previous specialization strategies have not been selective about where this technique is applied, and therefore tended to significantly increase compile time and code space usage, particularly for large applications. In this paper, we present a more general framework for specialization in object-oriented languages and describe a goal directed specialization algorithm that makes selective decisions to apply specialization to those cases where it provides the highest benefit. Our results show that our algorithm improves the performance of a group of sizeable programs by 65% to 275% while increasing compiled code space requirements by only 4% to 10%. Moreover, when compared to the previous state-of-the-art specialization scheme, our algorithm improves performance by 11% to 67% while simultaneously reducing code space requirements by 65% to 73%.