• Journal of The Korean Association For Science Education
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• v.24 no.2
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• pp.320-326
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• 2004

This study investigated whether algorithmic problem solving and conceptual problem solving influenced each other or not. Four classes of 12th grade (N= 112) that are equal in prior achievement were randomly assigned to group AC (Algorithmic-Conceptual problem) and group CA (Conceptual-Algorithmic problem). Students of group AC solved the conceptual problems after learning the related algorithmic problems, and those of group CA solved the same problems in reverse order. The results revealed that learning the algorithmic problems improved students' ability to solve the related conceptual problems, but learning the conceptual problems did not help students solve the related algorithmic problems. Regarding the confidence on problem solving, learning the algorithmic problems had little effect on the related conceptual problems. Learning the conceptual problems also had little effect on students' confidence on solving of the related algorithmic problems.

• Journal of The Korean Association For Science Education
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• v.25 no.2
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• pp.79-87
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• 2005

We compared algorithmic problem solving and conceptual understanding of chemistry with three types (algorithmic, pictorial- and wordy-formatted conceptual) of problems. The familiarity, confidence, and preference to the three type of problems were also examined. The chemistry problem solving ability test was administered to 228 students from two top high schools in the province of Gyeonggi who were preparing the chemistry examination among the four optional subjects (biology, chemistry, earth science, physics) for enter university. After administrating the chemistry problem solving ability test, the degree of familiarity to some problems and the degree of confidence of their answers in a Likert scale were asked to the students. Besides, the students were asked to place preference to the type of problems in order. The students scored better on the algorithmic problems than on the conceptual problems (pictorial and wordy problems), and were also most familiar with the algorithmic problems. The students were more confident of their answers on both of types pictorial and algorithmic problems, and preferred pictorial problems rather than both of types algorithmic and wordy problems.

• Journal of The Korean Association For Science Education
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• v.26 no.1
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• pp.1-8
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• 2006

This study investigated the causal relationships between high school student multiple-choice algorithmic chemistry problem solving and 1) the motivational variables of achievement goal (task goal/performance goal/performance-avoidance) and perceived ability, and 2) the cognitive variables of learning strategy (deep learning/surface learning) and self-regulation. Path analysis supported a causal model in which perceived ability and task goal were found to positively influence algorithmic chemistry problem-solving ability via self-regulation. In particular it was found that perceived ability directly influenced algorithmic chemistry problem-solving ability. Moreover, deep learning was found to have been influenced by perceived ability and task goal, while surface learning was influenced by performance-avoidance goal. Lastly, there did not appear to be any causal relationship between learning strategy and algorithmic chemistry problem-solving ability.

• Journal of the Korean School Mathematics Society
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• v.18 no.3
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• pp.241-258
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• 2015

The purpose of this paper is to examine the students' mathematics problem solving process in the intuitive stages. For this, researcher developed the questionnaire which consisted of problems in relation to intuitive and algorithmic problem solving. 73 fifth grade and 66 sixth grade elementary students participated in this study. I got the conclusion as follows: Elementary students' intuitive problem solving ability is very low. The rate of algorithmic problem solving is higher than that of intuitive problem solving in number and operation areas. The rate of intuitive problem solving is higher in figure and measurement areas. Students inclined to solve the problem intuitively in that case there is no clue for algorithmic solution. So, I suggest the development of problems which can be solved in the intuitive stage and the preparation of the methods to experience the insight and intuition.

• The Mathematical Education
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• v.39 no.1
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• pp.49-58
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• 2000

The purpose of this study is to investigate how to teach algorithms in mathematics class. Until recently, traditional school mathematics was primarily treated as drill and practice or memorizing of algorithmic skills. In an attempt to shift the focus and energies of mathematics teachers toward problem solving, conceptual understanding and the development of number sense, the recent reform recommendations do-emphasize algorithmic skills, in particular, paper-pencil algorithms. But the development of algorithmic thinking provides the foundation for student's mathematical power and confidence in their ability to do mathematics. Hence, for learning algorithms meaningfully, they should be taught with problem solving and conceptual understanding.

• Journal of the Korean Chemical Society
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• v.55 no.6
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• pp.1042-1055
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• 2011

The purpose of this study was to compare the conceptual and algorithmic problem solving ability on Boyle's law and Charles's law according to cognitive levels and characteristics of students in middle school 1st grade students. For this study, questionnaire items of conceptual and algorithmic problem solving ability were developed. and the problem solving ability according to cognitive levels and characteristics of students was compared. The long-term memory effect in conceptual and algorithmic problem solving ability according to cognitive levels was investigated, and problem solving process were analyzed by questionnaire items. In the results of this study, conceptual problem solving ability was higher than algorithmic problem solving ability in all cognitive levels. There was statistically significant difference in concrete operational period and transitional period students. In comparison of the long-term memory effect in conceptual and algorithmic problem solving ability, formal operational period students had the long-term memory effect. There was no statistically significant difference in the conceptual and algorithmic problem solving ability according to private education among the characteristics of students. But there was statistically significant difference in the problem solving ability according to experiences of the scientific activities and hopes to related scientific careers. From results of analysis of problem solving process, it is known that the students had a tendency to just remember macroscopic phenomena and to solve the problems without understanding the concepts. Therefore, teaching and learning strategy is necessary to replace unscientific concepts by the scientific concepts through identifying students's unscientific concepts in advance.

• Journal of The Korean Association For Science Education
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• v.20 no.2
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• pp.214-220
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• 2000

The difference (bias) between self-efficacy and chemistry problem-solving ability was investigated for 96 (male: 48, female: 48) high school students. A self-efficacy instrument was administered, which asked the confidence in solving algorithmic and conceptual problems successfully. Their chemistry problem-solving ability was then assessed with 10 algorithmic and 10 conceptual problems as same in the self-efficacy instrument. Although students had higher scores in the algorithmic problems, no significant difference was found in the self-efficacy to solve the two different forms of problems. Therefore, the bias scores in the conceptual problems were higher than those in the algorithmic problems. Two-way ANOVA results for the bias in the algorithmic problems revealed a significant interaction between gender and the previous achievement level. Analysis of simple effects indicated that the bias scores of high-achieving boys were significantly higher than those of high-achieving girls. While most high-achieving boys were in the overconfident category, high-achieving girls were more likely to be in the underconfident category.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.173-178
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• 2020

In the near future, as artificial intelligence and computing network technology develop, collaboration with artificial intelligence (AI) will become important. In an AI society, the ability to communicate and collaborate among people is an important element of talent. To do this, it is necessary to understand how artificial intelligence based on computer science works. An algorithmic education focused on problem solving and learning is efficient for computer science education. In this study, the results of an assessment of computational thinking at the beginning of the semester, a satisfaction survey at the end of the semester, and academic performance were compared and analyzed for 28 students who received algorithmic education focused on problem-solving learning. As a result of diagnosing students' computational thinking and problem-solving learning, teaching methods, lecture satisfaction, and other environmental factors, a correlation was found, and regression analysis confirmed that problem-solving learning had an effect on improving lecture satisfaction and computational thinking ability. For algorithmic education, if you pursue a problem-solving learning technique and a way to improve students' satisfaction, it will help students improve their problem-solving skills.

• The Mathematical Education
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• v.51 no.2
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• pp.95-114
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• 2012

This study examines the use of ill-structured problem to help the 4th graders' problem solving and reasoning. It appears that children with good understanding of problem situation tend to accept the situation as itself rather than just as texts and produce various results with extraction of meaningful variables from situation. In addition, children with better understanding of problem situation show AR (algorithmic reasoning) and CR (creative reasoning) while children with poor understanding of problem situation show just AR (algorithmic reasoning) on their reasoning type.

• Journal of the Korean Chemical Society
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• v.44 no.1
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• pp.68-73
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• 2000

In this study, tlhe relationships of high school students' abilities to solve chemistry problems with cognitive variables (logical thinking ability, mental capacity. and learning strategy) and affective variables(self-efficacy, self-concept of ability, learning goal, and attitude toward science) were investigated. The proportion of variance due to the variables for algorithmic and conceptual problem solving ability was studied by a multiple regression analysis. The results indicated that, among the cognitive variables, the logical thinking ability significantly predicted the algorithmic problem solving ability, and the learning strategy was the best predictor of conceptual problem solving ability although not significant. Among the affective variables studied, the self-concept of alility was the significant predictor of both algorithmic and conceptual problem solving abilities. The seif-efficacy was significantly correlated with conceptual problem solving ability, but it had no predictive power.