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情報工学専攻
対象課程 科目名 単位数 科目コード 開講時期 授業科目区分
博士前期課程(修士課程)
グローバルイノベーション特論
Global Innovation
4 2482-01 2024年度
後学期
関係科目
担当教員名
授業科目の学習・教育目標
キーワード 学習・教育目標
1.Biotope/ideathon 2.IoT 3.Data Mining 4.Smartphone Application 5.Hackathon ・Making Solution at the global programs associated with an ecological community ・Developing applications integrating some kind of sensor device, wireless LAN (in cluding LPWA), data mining and smartphone. ・Through Hackathon, deepen mutual understanding among society people, students of both KIT and RIT.
授業の概要および学習上の助言
【Class outline】 Start as an intensive course in summer. The place is Hakusan mountain campus and Ohgigaoka's Challenge Lab. With regard to problem solving concerning global program, students seek that solutions using IoT / ICT / AI technology. 【Conceive】 Students interview to each student about community problems.Brainstorming not only with students but also with business people. 【Technical lecture program】 (IoT) Students study how to make sensor network using microcomputer with ICT (Bluetooth, LPWA). (App) Students study how to make application using Android SDK and Apple iOS. (AI) Students study how to make decision for some problems. 【Design and Implement 】 Through Hackathon, Students create specifications and implement programs using agile development methods. 【Presentation and Operate】 Presentation and operation on results are carried out.
教科書および参考書・リザーブドブック
none
履修に必要な予備知識や技能
1) Acquired basic computer literacy (operation of Windows, operation of text editor etc.) 2) Understanding the basic grammer of C or Python programming (conditional branching, repetition, function etc.)
学生が達成すべき行動目標
No.
To be able to understand and explain what is necessary for innovation in biotope.
In order to create a new business model, understanding what skills is necessary.
Through the technical lecture program, getting skill for practical and overview IoT technology.
Through the Hackathon, learning the usefulness of Agile development. Furthermore, it is possible to create pract
Through the presentation, improvement points can be found from the comments of people in the area.
達成度評価
評価方法 試験 クイズ
小テスト
レポート 成果発表
(口頭・実技)
作品 ポートフォリオ その他 合計
総合評価割合 0 0 30 40 30 0 0 100
評価の要点
評価方法 行動目標 評価の実施方法と注意点
試験
クイズ
小テスト
レポート ・Describe the result of ideathon(idea of Hackathon) about the community problem. ・Describe about getting the each technical skills ・Describe the result of hackathon about the outcome solution program. 【Class assignment】 Day1:Hearing Report Day3-5: Technical Lecture Program
成果発表
(口頭・実技)
Describe as CE (Concrete Experience), RO (Reflective Observation), AC (Abstract Conceptualization), AE (Active Experimentation) in the achievement target sheet, and share it with faculty members.
作品 ・Evaluate about your idea from the audience and lecturer. ・Evaluate the final presentation (poster session). 【Class assignment】  Day2:Idea-base presentation Day9:Final presentation and poster session
ポートフォリオ
その他
具体的な達成の目安
理想的な達成レベルの目安 標準的な達成レベルの目安
・Understand the IoT system deeply ・Can grasp the skill level as my own embedded system engineer ・Plan for future career design ・Understand expert knowledge and terms related to the IoT system ・To be able to exchange opinions with corporate engineers and graduate students related to embedded systems ・Understand the IoT system and can grasp the skill level as my IoT system engineer ・Understand the expertise and terms related to the IoT system ・Understand the contents of corporate engineers and graduate students related to the IoT system. ・Basic development of IoT system can be done using Arduino / Raspberry PI / AWS server which is the basis of IoT system
※学習課題の時間欄には、指定された学習課題に要する標準的な時間を記載してあります。日々の自学自習時間全体としては、各授業に応じた時間(例えば2単位科目の場合、予習2時間・復習2時間/週)を取るよう努めてください。詳しくは教員の指導に従って下さい。
授業明細
回数 学習内容 授業の運営方法 学習課題 予習・復習 時間:分※
Day1 9 :00 - 16:00 [Hearing about the problem] ・Biotope ・ask from global program Lecture Report 240
Day2 9 :00 -1 6:00 [Make ideas with the team.] ・Ideathon Discussion (Ideathon) Presentation 240
Day 3 9:00 - 16:00 [Technical Lecture Program 1] ・Arduino ・Raspberry PI ・LPWA(5G) ・sensor network Practical Training Report 240
Day 4: 9:00 -16:00 [Technical Lecture Program 2] ・Android ・iOS Practical Training Report 240
Day5 9 :00 - 16:00 [Technical Lecture Program 3] ・IBM Watson/Bluemix ・DeepLearning (TensorFlow) ・CNN/RNN ・How to use data set. Exercise Report 240
Day 6: 9:00 - 16:0 0 [Hackathon 1] ・Making of required specification and overview design document Exercise Report Short Presentation 240
Day7: 9:00 - 16:00 [Hackathon 2] ・Making prototype system Exercise 240
Day8: 9:00 - 16:00 [Hackathon 3] ・Making prototype system ・Experiment and improvement on site Exercise 240
Day9 : 9:00 - 16:0 0 [Presentation] ・Final Presentation/ Final Report Exercise Report Final Presentation 240