2022 Fiscal Year Final Research Report
Building of top-down education specialized for KOSEN students approaching from cyber security
| Project/Area Number |
20K03113
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 09070:Educational technology-related
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| Research Institution | Kisarazu National College of Technology |
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Principal Investigator |
Yonemura Keiichi 木更津工業高等専門学校, 情報工学科, 准教授 (90369942)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | サイバーセキュリティ人材育成 / 高専生 / サイバーセキュリティ / 学習意欲の高さ / 没入感の深さ / サイバーセキュリティ教材開発 / サイバー攻撃とその対策 / 積極的サイバー防御スキル |
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| Outline of Final Research Achievements |
We aimed to establish a new educational model to effectively develop cyber security personnel by developing and utilizing cyber security educational materials that are highly compatible with technical college students, who are characterized by high motivation and depth of immersion. A five-level self-assessment on knowledge and skill level before and after attending a practical exercise-type lecture was used as a learning score, a motivation score was defined based on interest and expectation, and the depth of immersion was classified based on the continuous playing time and the continuity of the strategy elements. Analysis of the results of the practice of lectures with multiple factors showed that a high motivation score contributes to higher learning scores regardless of the depth of immersion. Based on the above, we proposed a methodology to enhance learning effectiveness by incorporating prior learning that contributes to increased motivation into a series of educational models.
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| Free Research Field |
サイバーセキュリティ人材育成
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| Academic Significance and Societal Importance of the Research Achievements |
高専生に特化して、サイバーセキュリティ分野への興味、講義に対する期待、講義後のスキルアップへの期待、サイバーセキュリティ分野を独学で学習することへの困難さからモチベーションスコアを定義し、様々なモチベーションスコア群における学習効果を分析した結果、モチベーションスコア75%以上であることが効果的な学習スコアの向上に寄与するという定量的な成果を得た。有効なモチベーションスコアを明確に得たことで、事前学習によってモチベーションスコアを高めるサイクルを教育モデルに組み込んだ方法論の提案に至っており、没入感の深さに依存しない学習効果は、むしろモデル適用対象の拡張による汎化の可能性を示唆する。
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