Achievements and challenges of science classes incorporating manufacturing activity to the elementary and junior high school students

小・中学生を対象にした科学教育においてものづくり活動を取り入れた実践を行った．具体的には，浮力の学習において天秤を製作させ，それを用いて塩水中の卵に働く浮力の大きさを測定させた．感度のよい天秤を製作するために試行錯誤をする様子が見られ，ほとんどのグループが天秤を完成させて浮力を測定することができた．また，製作する過程において話し合いを取り入れたグループでは，試行錯誤が多くなり，天秤に様々な工夫を施した．測定器具を製作する「ものづくり」活動を行うことは科学教育において有意義であることが明らかになった．Introducing design and manufacture of a balance as “monodukuri education”, the authors conducted the science learning about buoyancy for the elementary and junior high school students. Students could make balances and measure buoyancy utilizing that. During activities, some students discussed making processes would make an effort to think of methods or ways by processes of try and error, after that they could make some devices. Manufacturing activities in science class may be fruitful such as this practice

Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment

Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning are promising for enhancing clinical outcomes, including the chairside prognostication of tooth loss. We aimed to evaluate the risk of bias in prognostic prediction models of tooth loss that use machine learning. To do this, literature was searched in two electronic databases (MEDLINE via PubMed; Google Scholar) for studies that reported the accuracy or area under the curve (AUC) of prediction models. AUC measures the entire two-dimensional area underneath the entire receiver operating characteristic (ROC) curves. AUC provides an aggregate measure of performance across all possible classification thresholds. Although both development and validation were included in this review, studies that did not assess the accuracy or validation of boosting models (AdaBoosting, Gradient-boosting decision tree, XGBoost, LightGBM, CatBoost) were excluded. Five studies met criteria for inclusion and revealed high accuracy; however, models displayed a high risk of bias. Importantly, patient-level assessments combined with socioeconomic predictors performed better than clinical predictors alone. While there are current limitations, machine-learning-assisted models for tooth loss may enhance prognostication accuracy in combination with clinical and patient metadata in the future

Discussion in teaching of Life-science subjects in University\u27s General Education with a point of view of transition from elementary school, junior high school and high school to University

秋田大学の教養教育科目の「ライフサイエンスＩ－生命の連続性－」では，履修においては，過去の学習履歴等の前提条件を課していない。そのため，主な受講者である大学１，２年生のライフサイエンス系分野における知識や学習履歴はさまざまである。そこで，大学の教養教育におけるライフサイエンス系科目への小学校・中学校・高等学校からのつながりを考察したので報告する。In general education in Akita University, Life-science subject I (its subtitle is continuity of life) is opened to anyone who had learned or not biology in his or her high school. In this study, we discuss how teaching of Life-science subjects in University’s General Education is suitable, with a point of view of transition from elementary school, junior high school and high school to University

Hitherto-Unnoticed Self-Transmissible Plasmids Widely Distributed among Different Environments in Japan

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