• Journal of the Korean School Mathematics Society
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• v.9 no.4
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• pp.521-538
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• 2006

The purpose of this study is to examine the effect of teaching mathematical proofs that made use of the 'proof assisted cards' at the second year of middle school and to investigate students' ability to geometric proofs as well as changes of mathematical attitudes toward geometric proofs. The subjects are seven students at the 2nd year of D Middle School in Daegu who made use of the 'proof assisted cards' during five class periods. The researcher interviewed the students to investigate learning questions made by students as well as the 'proof assisted cards' before and after use. The findings are as follows: first, the students made change of geometric proof ability by proof activity with the 'proof assisted cards' and second, the students made significant change of mathematical attitudes toward geometric proofs by proof activity using the cards.

• Proceedings of the Korea Society of Mathematical Education Conference
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• 2006.10a
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• pp.147-161
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• 2006

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.