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This study aimed to develop an energy-based Hp(3) measurement method using a solid-state detector (SSD). Incident and entrance surface air kerma were measured using an ionization chamber placed free-in-air and in front of an anthropomorphic or slab phantom. Subsequently, three SSDs were placed free-in-air, and half-value layer and readings were obtained. After measurements, an X-ray beam quality correction factor $\left ({{k}}_{{Q},{{Q}}_{\mathbf{0}}}^{{SSD}}\right)$, backscatter factor (BSF) and conversion factor from incident air kerma to Hp(3) (C3) were determined. Then, the incident air kerma by SSD $\left ({{K}}_{{a},{i}}^{{SSD}}\right )$, Hp(3) and Hp(3)/${{K}}_{{a},{i}}^{{SSD}}$ were calculated. The ${{k}}_{{Q},{{Q}}_{\mathbf{0}}}^{{SSD}}$ was almost consistent for all SSDs. The C3 and BSF were found to increase as tube potential increased. The Hp(3)/${{K}}_{{a},{i}}^{{SSD}}$ calculated with the anthropomorphic and slab phantoms were consistent within 2.1% and 2.6% for all SSDs, respectively. This method improves the energy dependence of Hp(3) measurement and can estimate the Hp(3) measurement error for dedicated Hp(3) dosemeters.

This article outlines a new way of visualizing curricula (sets of courses for students) using competencies via a combination of cosine similarity, multidimensional scaling methods, and scatter plotting. We have already published a report analyzing syllabi (summary texts that explain the main points of courses or the contents of curricula) with the same methods. In this report, we show that using competencies is more useful than using syllabi. Usually, students are only interested in the course content when they select courses; this new visualization of curricula using competencies gives students a new perspective on their courses.

This article proposes new criteria for using student data in universities. First criteria are called primary data and secondary criteria are called secondary data. We define primary data as those that are not linear combination data, and secondary data as a linear combination of primary data. For example, at the macro-level, primary data are correct and incorrect answers to a question in an examination or students' attendance and absence from a lecture. At the macro-level, secondary data are the total points in an examination or students' total attendance in and absence from a lecture. At the meso-level, secondary data are student records of lectures as well as grade point average, or rank, in the annual record of the university. Primary data are mainly constructed by faculty while secondary data are constructed by administrative staff. To compare primary and secondary data, collaboration between faculty and administrative staff is important.

In Japan, just as student dropout is a problem in universities, early turnover of newly employed nurses is a problem at hospitals. In recent years, investigations and research through institutional research (IR) at universities have gradually accumulated knowledge on prevention of student dropout. In contrast, IR to address early turnover of newly employed nurses is rarely conducted. This is possibly because the concept of IR has hardly penetrated hospitals, and there is a lack of awareness about IR in Japanese hospitals. In this presentation, we will report the significance and necessity of IR in hospital nursing departments based on research trends and issues in prevention of early turnover of newly employed nurses at hospitals in Japan.

Kobe Tokiwa University witnessed its 1st university reform in 2015. This resulted in the start of a basic education based first-year course from the year 2017. The course was taught by 20 teachers to an approximate 320 students. Kobe Tokiwa University constructed the course based on 19 Tokiwa competencies. In order to evaluate the students, who are evaluated by the 20 teachers based on Tokiwa competencies, we constructed a rubric and an evaluation matrix for the first-year course. Our study attempts to address the following research question: Is our evaluation matrix, which is based on the Tokiwa competencies used by teachers to evaluate the students in the 2018 first-year course, a good evaluation tool. To answer this research question, we performed a factor analysis of the evaluation of students by using an evaluation matrix for the 2018 first-year course. The results show that our evaluation is not based on the Tokiwa competencies. Therefore, we constructed a new evaluation matrix for the first-year course. We will make further investigations in the future to check whether this new evaluation matrix is a good method or not.

Recently, we proposed "Eduinformatics," a new field of education that combines both education and informatics. In addition, we introduced new criteria to utilize student data in Institutional Research (IR). In a previous article, we defined "primary data" as the first standard which is not combined linear data and "secondary data" as the second standard which is a linear combination of primary data. However, in this article we will present new definitions of Primary and Secondary data because our analysis of actual educational data has revealed that Secondary data is not only linear data, but also nonlinear. Moreover, we will present examples in which primary data was used to detect elements that could not be founded through the analysis of secondary data, and were pitfalls of compared analysis performed by IR practitioners.

A new challenge for knowledge graph reasoning started in 2018. Deep learning has promoted the application of artificial intelligence (AI) techniques to a wide variety of social problems. Accordingly, being able to explain the reason for an AI decision is becoming important to ensure the secure and safe use of AI techniques. Thus, we, the Special Interest Group on Semantic Web and Ontology of the Japanese Society for AI, organized a challenge calling for techniques that reason and/or estimate which characters are criminals while providing a reasonable explanation based on an open knowledge graph of a well-known Sherlock Holmes mystery story. This paper presents a summary report of the first challenge held in 2018, including the knowledge graph construction, the techniques proposed for reasoning and/or estimation, the evaluation metrics, and the results. The first prize went to an approach that formalized the problem as a constraint satisfaction problem and solved it using a lightweight formal method; the second prize went to an approach that used SPARQL and rules; the best resource prize went to a submission that constructed word embedding of characters from all sentences of Sherlock Holmes novels; and the best idea prize went to a discussion multi-agents model. We conclude this paper with the plans and issues for the next challenge in 2019.

Due to the increasing global frequency of disasters, disaster preparedness training is becoming more important. The fact that Japan has many earthquakes is well known worldwide. In the field of nursing, the importance of disaster preparedness education in universities has gradually increased in Japan. Many people lose their homes in earthquakes and have to live in shelters. The relationship between pneumonia and the oral cavity environment is understood; for example, in certain shelter environments that provide an insufficient water supply, oral cavity hygiene is affected. Keeping a clean oral cavity prevents death from pneumonia, especially in elderly people. To keep a clean oral cavity, the role of dental hygienists is important. In Japan, education on disaster preparedness and response for dental hygienists in vocational university/college is rarely provided. Therefore, this is the focus of our research. We administered an anonymous questionnaire survey to 119 dental hygienist training schools by mailing them surveys asking about their education on disaster preparedness and response for dental hygienists. In this paper, we report on the education on disaster preparedness and response for dental hygienists in vocational universities and colleges in Japan.

Eduinformatics, a new term coined by us, is a field that combines education and informatics, and novel techniques will need to be developed for this field. Earlier, we developed a new visualization method to visualize the curriculum of Kobe Tokiwa University using multidimensional scaling (MDS) and a scatter plot. In this study, our focus is on methods to analyze the relationships between answers to questions in eduinformatics questionnaires. MDS methods are very useful, but have limitations in that their results are difficult to interpret. To facilitate the interpretation of these results, we develop a new visualization method using a network with both parametric and non-parametric correlation coefficients with a threshold (VNCC). VNCC has nine steps. We apply the VNCC method to research on nursing education, and provide an example of the visualization of the result. VNCC methods will be useful in dealing with qualitative research in eduinformatics.

The rubrics have become a widely referenced and utilized form of assessment on campuses across internationally. But rubric can be an asset in any classroom and at any education level but it needs to be implemented correctly. Our research question in this study is whether students were evaluated consistently and equally from teacher to teacher using rubric. To answer this research question, we performed statistical estimation using nonparametric multiple comparisons. This article reports on a normalizing rubric evaluation by nonparametric multiple comparisons in a first-year course called “Manaburu I” offered at Kobe Tokiwa University. “Manaburu” is a word coined by us: “manabu” ‘learn’ in Japanese + English able. Thus, “Manaburu” refers to Self-Directed Learning I. In the course, about 20 teachers teach about 350 students (16–17 students per teacher). Students are organized into groups of about 6. It is of course difficult for 20 teachers to evaluate their students consistently among them, making this course an appropriate site for the evaluation. We constructed a rubric for the course, under which teachers were meant to evaluate students, and presented it to both teachers and students. Our research question was whether teachers evaluated students consistently and equally according to the Steel–Dwass estimation method, a strict statistical estimation method for nonparametric multiple comparisons. The results show that teachers do not evaluate students equally. Suggestions for future research, more attention to validity and reliability, a closer focus on learning and research on rubric use in higher education.

In 2016, an office for institutional research (IR) promotion was constructed at Kobe Tokiwa University. The purpose of this office is to propose, manage, arrange, and collect information on students at the university not only as a general management strategy, but also to provide enrollment management. Work at the IR promotion office officially began 2016, at which point we began to perform university data management and collection duties. A promotion unit for IR was also newly established in 2017. In contrast to IR promotion practices in which the department is run solely by an administrative staff, the IR promotion unit at Kobe Tokiwa University is a collaborative group consisting of both staff and faculty. In this unit, we carried out an analysis focusing on the "student dropout " by using data from both current, graduate, and dropout students.

In 2016, Kobe Tokiwa University constructed an office for institutional research (IR) promotion. The purpose of this office is to propose, manage, arrange, and collect information on students at the university not only as a general management strategy, but also to support enrollment management. Our database currently contains 3,495 points of data (i.e., headcounts), each containing 1,246 items of numerical value. Last year, we reported on an analysis that focused on the "student dropout" phenomenon by using these data from both current graduate and dropout students. This year, we formulated a research question that is centered on predicting the probability of students' progress and academic abilities through Enrollment Management / Institutional Research (EMIR). We obtained results with these data by processing them through a machine learning technique using random forest, which yielded a correction rate of about 90%.

昨今の大学運営において、教職協働の必要性に対する認識が広まっている。今回、本学の教学マネジメントの改善に資するため、教職協働によりその課題の可視化に取り組んだ。その結果、本学の教学マネジメントの全体を俯瞰することができ、なおかつその課題を可視化することができる活動システムを描くに至った。教職協働を「知」の観点から捉えるとき、その取り組みは異なる専門知同士の相互交流とみることができる。とはいえ、知の相互交流は決して容易なことではない。「専門家/非専門家」の境界線を固定的に捉える限り、知の流れは一方向的にしかならない。知の交流が促進されるためには、パラレルな知性が駆使されるよう環境を整備することが求められる。サロンのような場の設定、ファシリテーターの役割を担う人材の配置がその例である。このような環境があってこそ、セレンディピティの可能性が高まり、知の交流に留まらず新たな知の創造に至るのである。, This paper discusses knowledge creation through collaboration amongst faculty at institutions of learning. Particularly, as a case study and in an effort to improve the management at our own university, we attempted to conceptualize this topic from the perspective of collaboration between academic and administrative faculty. We were able to evaluate the school's management as a whole and developed a system of exercises to determine how management can improve with greater collaboration between academic and administrative faculty. Mutual exchange of knowledge between academic and administrative faculty is hindered by the fixation on boundaries between specialists and nonspecialists, ensuring that knowledge can flow only in one direction. We can nurture an environment that is conducive to knowledge exchange, where for example a facilitator exists to bridge the boundary between the specialist and the nonspecialist. Collaboration in such an environment allows unobstructed flow of knowledge and the creation of new knowledge.

知の創造プロセスに関する先行研究では、これまでいくつかのモデルが提唱されてきた。本稿で我々は、知の創造プロセスについて「増殖段階」「混在段階」「創造段階」の3段階からなる【知のネットワーク成長モデル】を新たに提唱する。本モデルが従来のモデルと異なる点は、知の創造理論について、数学の一分野であるノード(点)とエッジ(線)で結ばれた「グラフ」を用いたグラフ理論や、ライフサイエンスの分野である「タンパク質相互作用ネットワーク(PPI: Protein-Protein Interaction)」などを援用して、知の創造プロセスを提唱している点にある。さらに「混在段階」と「創造段階」の溝(キャズム)を越え、混在させた知に新しい価値を付加するためのセレンディピティの必要性についても言及している点にある。, In this paper we describe a novel knowledge creation model, the "three-step knowledge network model," which comprises the propagation step, the mixing step, and the creation step. This model uses knowledge networks first proposed by Willard Van Orman Quine's, and graph theory to describe the knowledge creation process. We use higher education as a example of knowledge creation that this model can describe, and in the future hope to apply this model to other phenomena.

This paper outlines a new way of visualizing curricula using competencies via a combination of cosine similarity, t-distributed stochastic neighbor embedding (t - SNE) methods, and scatter plotting. We have already published a report on the use of multidimensional scaling with the same methods. In this report we show that a t-distributed stochastic neighbor embedding method is more useful than a multidimensional scaling method. We believe that this visualization will be useful when students select their courses.

This study examines a generic support system prototype in eduinformatics for students selecting a seminar. We have proposed a new inter-disciplinary field called Eduinformatics. It is a combination of the fields of education and informatics. Earlier in 2017, we examined students' submission system and procedure and the result of actually employing this system to match students with specific seminars and teachers. Results revealed several issues with this first prototype system. Therefore, we developed a generic support system prototype in eduinformatics for students selecting a seminar, to improve the earlier model.

目的:本学通信制課程の学生が就業する施設で体験するヒヤリ・ハット事例を全国300床以上の病院と比較し特徴を明らかにし、事故報告のしくみや意識についての実態を知る。方法:対象は1年次生188名。2013年質問紙調査を実施。調査内容は背景、事故報告のしくみ、意識、ヒヤリ・ハット体験で、印象的な体験や気持ちは自由記載とした。結果:回収率52%。就業する施設は200床以下の病院、施設が約7割を占めた。ヒヤリ・ハット体験は「療養上の世話」で、熱傷・暴力・誤嚥・入浴・離院、「観察情報」が高率で、自由記載からは注射・与薬・転倒転落が上位を占めた。事故報告の仕組みは約90%が保有し、意識では約30%が報告に消極的であった。結論:学生が就業する施設でのヒヤリ・ハット体験の特徴は、療養上の世話と医師、患者・家族に関する項目が全国調査より高い比率を占めた。事故報告のしくみは有るものの運用実態や事故報告の意識の解明は今後の課題である。, Objective: To reveal characteristics and perception of near-miss errors and its reporting system of hospitals where the students are employed, comparing with a national data of larger scale hospitals over 300 beds.Methods: A questionnaire survey was conducted in 2013 on 188 first-year working students in a correspondence course. Main question items were consisted of the background information, experience, perception, and the system of reporting near-miss errors. Significant events were asked in the form of an open question. Results: The response rate was 52%. Around 70% of the hospitals had less than 200 beds. There was an increase of the near-miss errors of burns, violence, aspirations, bathing, leaving hospitals, and observation on patient care. Also, increased cases at injection, medication, and falls were observed in open questions. Ninety percent of the facilities had a system of reporting near-miss cases but 30% of nurses were passive about the reporting. Conclusion:Features of near-miss errors at targeted hospitals were revealed as there were increased cases at patient care, and items related to doctors, patients or their families. Although most facilities have the system of incident report, analysis of the perception on near-miss errors and actual implementation of the system is further needed.

This article outlines a new way of visualizing curricula using competencies via a combination of cosine similarity, multidimensional scaling methods, and scatter plotting. We have already published a report using syllabi with the same methods. In this report we show that using competencies is more useful than using syllabi. Usually, most students are only interested in the course content when they select their courses. This new visualization of curricula using competencies gives a new perspective on the course to students. We believe that this visualization is useful when students select their courses.

The project in producing the hyperpolarized 3He and 19F are addressed in pursuit of radiation free medical diagnosis. The program for production of the hyperpolarized 3He by the brute force method with the Pomeranchuk cooling and the rapid melting of the solid 3He started a few years ago, and is still on the way, while a new program for production of the hyperpolarized 19F by means of the PHIP (ParaHydrogen Induced Polarization) has just got started. Particular attention is placed upon a new idea of the hyperpolarization catalyst to be used for 19F.