MDPI AG Acta Medica Okayama 2078-2489 16 7 2025 A Fundamental Statistics Self-Learning Method with Python Programming for Data Science Implementations 607 EN Prismahardi Aji Riyantoko Department of Information and Communication Systems, Okayama University Nobuo Funabiki Department of Information and Communication Systems, Okayama University Komang Candra Brata Department of Information and Communication Systems, Okayama University Mustika Mentari Department of Information and Communication Systems, Okayama University Aviolla Terza Damaliana Department of Data Science, Universitas Pembangunan Nasional Veteran Jawa Timur Dwi Arman Prasetya Department of Data Science, Universitas Pembangunan Nasional Veteran Jawa Timur The increasing demand for data-driven decision making to maintain the innovations and competitiveness of organizations highlights the need for data science educations across academia and industry. At its core is a solid understanding of statistics, which is necessary for conducting a thorough analysis of data and deriving valuable insights. Unfortunately, conventional statistics learning often lacks practice in real-world applications using computer programs, causing a separation between conceptual knowledge of statistics equations and their hands-on skills. Integrating statistics learning into Python programming can convey an effective solution for this problem, where it has become essential in data science implementations, with extensive and versatile libraries. In this paper, we present a self-learning method for fundamental statistics through Python programming for data science studies. Unlike conventional approaches, our method integrates three types of interactive problems\element fill-in-blank problem (EFP), grammar-concept understanding problem (GUP), and value trace problem (VTP)\in the Programming Learning Assistant System (PLAS). This combination allows students to write code, understand concepts, and trace the output value while obtaining instant feedback so that they can improve retention, knowledge, and practical skills in learning statistics using Python programming. For evaluations, we generated 22 instances using source codes for fundamental statistics topics, and assigned them to 40 first-year undergraduate students at UPN Veteran Jawa Timur, Indonesia. Statistics analytical methods were utilized to analyze the student learning performances. The results show that a significant correlation (𝜌<0.05) exists between the students who solved our proposal and those who did not. The results confirm that it can effectively assist students in learning fundamental statistics self-learning using Python programming for data science implementations. No potential conflict of interest relevant to this article was reported.

MDPI AG Acta Medica Okayama 2078-2489 16 7 2025 A Map Information Collection Tool for a Pedestrian Navigation System Using Smartphone 588 EN Kadek Suarjuna Batubulan Graduate School of Natural Science and Technology, Okayama University Nobuo Funabiki Graduate School of Natural Science and Technology, Okayama University Komang Candra Brata Graduate School of Natural Science and Technology, Okayama University I Nyoman Darma Kotama Graduate School of Natural Science and Technology, Okayama University Htoo Htoo Sandi Kyaw Graduate School of Natural Science and Technology, Okayama University Shintami Chusnul Hidayati Department of Informatics, Institut Teknologi Sepuluh Nopember Nowadays, a pedestrian navigation system using a smartphone has become popular as a useful tool to reach an unknown destination. When the destination is the office of a person, a detailed map information is necessary on the target area such as the room number and location inside the building. The information can be collected from various sources including Google maps, websites for the building, and images of signs. In this paper, we propose a map information collection tool for a pedestrian navigation system. To improve the accuracy and completeness of information, it works with the four steps: (1) a user captures building and room images manually, (2) an OCR software using Google ML Kit v2 processes them to extract the sign information from images, (3) web scraping using Scrapy (v2.11.0) and crawling with Apache Nutch (v1.19) software collects additional details such as room numbers, facilities, and occupants from relevant websites, and (4) the collected data is stored in the database to be integrated with a pedestrian navigation system. For evaluations of the proposed tool, the map information was collected for 10 buildings at Okayama University, Japan, a representative environment combining complex indoor layouts (e.g., interconnected corridors, multi-floor facilities) and high pedestrian traffic, which are critical for testing real-world navigation challenges. The collected data is assessed in completeness and effectiveness. A university campus was selected as it presents a complex indoor and outdoor environment that can be ideal for testing pedestrian navigations in real-world scenarios. With the obtained map information, 10 users used the navigation system to successfully reach destinations. The System Usability Scale (SUS) results through a questionnaire confirms the high usability. No potential conflict of interest relevant to this article was reported.