Pengembangan E-Modul Terintegrasi Media Berbasis Adobe Flash CS6 Untuk Meningkatkan Kemampuan Pemecahan Masalah Kimia Mahasiswa

Supporting the implementation of an effective learning process cannot be separated from the use of teaching materials. Improving the quality of the learning process in higher education can be done with various strategies and one alternative that can be taken is the development of teaching materials. This study aims to develop an integrated e-module based on Adobe Flash CS6 media to improve students' chemistry problem solving skills on thermochemical material. The development model used refers to the ADDIE development model. Adobe Flash CS6-based integrated media e-module is designed to be accessible and downloadable by students online. The developed Adobe Flash CS6-based media-integrated e-module has been declared valid and proven to be effective in improving students' chemistry problem solving abilities on thermochemical materials.

INORGANIC CHEMISTRY PROBLEM-BASED LEARNING

The resolution of problem situations under the teacher’s guidance makes students compare, generalize, analyze phenomena, and not only memorize them mechanically. The processes of advancing and resolving problem situations are an unbroken chain, since when a problem is advanced, its solution begins simultaneously, which leads to the formulation of new problems. That is, a contradictory and continuous process of new scientific concepts active cognition is carried out. We see from the experience that using the methods of problem-based learning in the lessons that they promote development of cognitive activity, creative students’ independence, the formation of their worldview, intellectual development, and as a result, the improvement of the knowledge’s quality. Today, it is necessary when learning future specialists, in addition to the implementation of existing educational state standards in this specialty, to focus on the development of their creative qualities, creative thinking, which, ultimately, will promote to the formation of highly professional competent personnel.

Development of a framework to capture abstraction in physical chemistry problem solving

Productive problem solving, concept construction, and sense making occur through the core process of abstraction. Although the capacity for domain-general abstraction is developed at a young age, the role of...

Context-based chemistry problem solving

The purpose of this study was to investigate preservice chemistry teachers’ science expectations on learning science after they completed the introductory Context-Based Chemistry Problem Solving. The sampling of the study consisted of 22 preservice chemistry teachers studying at Hacettepe University, Faculty of Education. The scores obtained by participating groups from the ‘Science Expectations Questionnaire’. In addition, preservice teachers’ interviews are given as evidence. As a result of the evaluation of preservice chemistry teachers’ the expectations about learning science on independence, coherence, concept, reality link, math link and effort dimensions, it is thought that there will be important outcomes in terms of improvement of the quality of teacher training at the university and that there will be an important contribution to literature in this sense. Keywords: Context-based chemistry problem solving, science expectations, teacher education.

Artificial Intelligence Meets Chemistry

We argue that chemistry should be the next grand challenge for Artificial Intelligence. The AI research community and humanity would benefit tremendously from focusing AI re- search on chemistry on a regular basis, as a benchmark as well as a real-world application domain. To support our position, we review the importance of chemical compound discovery and synthesis planning and discuss the properties of search spaces in a chemistry problem. Knowledge acquired in domains such as two-player board games or single player puzzles places the AI community in a good position to solve critical problems in the chemistry domain. Yet, we show that searching in chemistry problems poses significant additional challenges that will have to be addressed. Finally, we envision how several AI areas like Natural Language Processing, Machine Learning, planning and search, are relevant for chemistry.

AI Meets Chemistry

We argue that chemistry should be the next grand challenge for Artificial Intelligence. The AI research community and humanity would benefit tremendously from focusing AI re- search on chemistry on a regular basis, as a benchmark as well as a real-world application domain. To support our position, we review the importance of chemical compound discovery and synthesis planning and discuss the properties of search spaces in a chemistry problem. Knowledge acquired in domains such as two-player board games or single-player puzzles places the AI community in a good position to solve critical problems in the chemistry domain. Yet, we show that searching in chemistry problems poses significant additional challenges that will have to be addressed. Finally, we envision how several AI areas like Natural Language Processing, Machine Learning, planning and search, are relevant for chemistry.

Patterns of Metacognitive Skills and External Representation of Students in Chemistry Problem Solving

This research aims to examine the pattern of external representation and metacognitive skills in chemistry problem solving for students of chemistry education at Tadulako University. This picture will enrich the treasures of thinking skills in the field of science, namely how students/prospective teachers think in the context of metacognitive skills and how these students display an external representation system on chemistry concepts. The subject of this qualitative study was obtained through a purposive random sampling of 97 students who have been programmed Basic Chemistry course 2017/2018 academic year. Subjects were selected based on the results of the screening using a metacognitive skills assessment questionnaire (MCAI). Two problems solved by the subject by setting one-on-one thinking aloud. Subjects who complete the issue by setting further interviews at different times. The problem-solving activity was recorded using a video camera. The subject uses a type of representation and aspects of metacognitive skills and how to use the sequence of activities, analyzed in detail. The results showed that students of chemical education used metacognitive skills and external representations. So students solve chemical problems. Students also can improve knowledge retention related to linking new knowledge with previous knowledge, creating ideas, organizing, and even synthesizing knowledge. Students become productive and find solutions for problem-solving well