IoT-based Learning Material and Its Management System for STEAM in Primary and Secondary Education

Kayama, Mizue; Nagai, Takatoshi; Tachi, Nobuyuki; Asuke, Takehiko; Taguchi, Naomi; Futagami, Takao

doi:10.1007/978-3-030-85720-2_9

Cited by 1 publication

(2 citation statements)

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“…In the context of mathematics in Japan, units on mathematics related to statistics have been introduced at every grade level, as indicated by the enhancement of statistical education, and learning activities using computers and other tools. A recent study has proposed the use of programming environments to support the learning of statistics according to learner's grade (Kayama et al, 2022). To support learners use of such environments, it is important for learners to be able to figure out which exercises are in which grade level of Table 1 Explanation of each organization part of the units (MEXT, 2009) Part Description…”

Section: Mathematical Education In Japanmentioning

confidence: 99%

“…Automatic labeling can help reduce teacher workload (Tian et al, 2022) and develop mathematics workmanship (Fishback & Schlicker, 1996) by introducing systems that require labels (Vie et al, 2019;Wang et al, 2022). For further development of mathematics in Japan, a programming environment on supporting units on mathematics using data (Kayama et al, 2022) and clarification of unit structure for knowledge association in learning (Taniguchi & Itoh, 2023) have been proposed. The labeling method in this study allows labels to be assigned to unlabeled mathematical instructional materials by learning the text of the labeled materials, even though they are not formatted suitably for extracting text, such as handmade materials by mathematics teachers.…”

Section: Practical Educational Implications In This Researchmentioning

confidence: 99%

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Automated labeling of PDF mathematical exercises with word N-grams VSM classification

Yamauchi,

Flanagan,

Nakamoto

et al. 2023

Smart Learn. Environ.

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In recent years, smart learning environments have become central to modern education and support students and instructors through tools based on prediction and recommendation models. These methods often use learning material metadata, such as the knowledge contained in an exercise which is usually labeled by domain experts and is costly and difficult to scale. It recognizes that automated labeling eases the workload on experts, as seen in previous studies using automatic classification algorithms for research papers and Japanese mathematical exercises. However, these studies didn’t delve into fine-grained labeling. In addition to that, as the use of materials in the system becomes more widespread, paper materials are transformed into PDF formats, which can lead to incomplete extraction. However, there is less emphasis on labeling incomplete mathematical sentences to tackle this problem in the previous research. This study aims to achieve precise automated classification even from incomplete text inputs. To tackle these challenges, we propose a mathematical exercise labeling algorithm that can handle detailed labels, even for incomplete sentences, using word n-grams, compared to the state-of-the-art word embedding method. The results of the experiment show that mono-gram features with Random Forest models achieved the best performance with a macro F-measure of 92.50%, 61.28% for 24-class labeling and 297-class labeling tasks, respectively. The contribution of this research is showing that the proposed method based on traditional simple n-grams has the ability to find context-independent similarities in incomplete sentences and outperforms state-of-the-art word embedding methods in specific tasks like classifying short and incomplete texts.

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Section: Mathematical Education In Japanmentioning

confidence: 99%

Section: Practical Educational Implications In This Researchmentioning

confidence: 99%