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長距離通勤・通学者向けの場所に応じた暗記項目提示手法の提案とその検証
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剣太 佐藤
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第101回グループウェアとネットワークサービス研究会,口頭発表にて使用したスライド
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5 Public speaking tips from TED - Visualized summary
5 Public speaking tips from TED - Visualized summary
ChatGPT and the Future of Work - Clark Boyd
ChatGPT and the Future of Work - Clark Boyd
Getting into the tech field. what next
Getting into the tech field. what next
Google's Just Not That Into You: Understanding Core Updates & Search Intent
Google's Just Not That Into You: Understanding Core Updates & Search Intent
How to have difficult conversations
How to have difficult conversations
Introduction to Data Science
Introduction to Data Science
Time Management & Productivity - Best Practices
Time Management & Productivity - Best Practices
The six step guide to practical project management
The six step guide to practical project management
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
長距離通勤・通学者向けの場所に応じた暗記項目提示手法の提案とその検証
1.
長距離通勤・通学者向けの場所に応じた 暗記項目提示手法の提案とその検証 佐藤 剣太,中村 聡史 (明治大学
総合数理学部)
2.
背景 語学・資格試験の需要 TOEIC• TOEFL• 情報処理技術者試験• 限られた期間での効率的な学習が重要 学習範囲が 膨大!!
3.
背景 電車による通勤・通学 健康づくりのための睡眠指針 [厚労省14] 大学• に通う学生の通学時間:2時間2分
[本田06] 成人• の理想的な睡眠時間:6∼7時間 活動時間:約17∼18時間 家や学校、会社で学習可能な 時間が限られてくる!
4.
背景 行き:2時間 帰り:2時間
5.
通勤・通学中の学習 背景 スマートフォン• やタブレット端末の普及 混雑時• は手で操作する余裕がないことや、 痴漢の
罪のリスクなどもあり、作業が困難 音声提示を用い、乗車中でも手軽に 学習を行えるようにする必要性あり
6.
背景 -関連研究① Mnemonic DJ
[伊藤 2011] ユーザ• の既知の楽曲に対し単語を割り当て、替え歌 を生成 単語• リストの順序を入れ替えた場合/そのままの場合 に対応しており、様々な暗記対象に適用可能
7.
背景 効率的な暗記方法 •物語法 -ストーリーを作って整理 場所法• 暗記内容- と場所を関連づける •就寝前に暗記 academic inhabitant
8.
背景 -関連研究② Memory-Organizer[村上 2001] 個人•
の外化記憶を構築し、記憶作業を支援 地理空間• ブラウザ:記憶対象を地図上に配置可能
9.
背景 暗記項目と場所を対応づける 技術の習得には時間が必要 場所法 academic inhabitant 時間がかかる…
10.
背景 ユーザが場所に抱くイメージは様々に存在する 駅は大きいし 混雑もすごいよなあ… 近くにアニメショップ 多いよなあ… 場所のイメージと暗記項目を自動的に 結びつけることで効率的に記憶可能?
11.
目的 暗記項目をユーザの通学区間に対応づけ、 現在地に応じた項目を音声提示することで 効率的な暗記を支援 通学区間内• の駅と暗記項目の自動的な対応づけ ユーザ• の理想的な対応づけを提案手法で再現可能か 検証 ユーザ•
の現在地に応じた項目の音声提示による暗記の 有効性を検証
12.
手法 - ①駅の関連語の収集 各駅に関連する語を自動で収集 •HeartRails
APIを用いて駅の経緯度を取得 •Google Places APIを用いて、各駅の周辺にある施 設情報と、それに関連する語を複数収集 百貨店 雑貨 大手 公園 桜 環境
13.
手法 - ②通学区間との対応付け •対象項目の関連語を収集 -
Wordnetを使用 •駅の関連語グループと単語の関連語グループを比較し 共通部分の多い組み合わせは関連が高いとみなす 大手 百貨店 雑貨 major = 大きい, 主要な 公園 花 環境 planting = 緑化 緑
14.
精度評価 被験者:• 19∼23歳の大学生8名 各通学区間• の正解データを作成してもらう ユーザの理想的な対応付けを再現可能か? •3種類の手法で比較 ①ランダムな対応づけ(=従来の場所法) ②訳語と駅名のローマ字表記の類似度で対応づけ ③提案手法による対応づけ
15.
精度評価 -ローマ字比較 駅名と訳語をローマ字にしたものを比較 例)新宿• →
shinjuku truth(真実)→ shinjitsu Gestalt Pattern Matching• アルゴリズムに基づいて 類似度を計算
16.
結果 ユーザ A B
C D E F G H 駅数 16 28 24 43 31 11 13 43
17.
考察 -精度評価 駅数• が多い人ほど正解データとマッチしづらい 提案手法は他の2手法と比較して有意差なし •難易度の高い単語は類義語の取得が困難 学習実験では、人の手によって 分類されたデータを利用
18.
暗記学習実験 学習前 テスト 通学中に 学習 学習後 テスト 対象:正解• データを作成した大学生8名 条件:片道• の乗車時間が60分以上 仮説:ランダムな項目再生と比べて、 場所に応じた項目再生のほうが効率的に暗記可能
19.
暗記学習実験 学習前 テスト 通学中に 学習 学習後 テスト 実験手順
20.
暗記学習実験 -学習前テスト 解答内容:英単語(• 300語)の日本語訳 制限時間:•
60分
21.
暗記学習実験 学習前 テスト 通学中に 学習 学習後 テスト 実験手順
22.
暗記学習実験 -通学中の学習 A B
C D E F G H ランダムに項目を提示 ユーザの現在地に応じて 項目を提示 ➡著者が主観で割り当てた ものを使用
23.
暗記学習実験 -通学中の学習 •現在地から最も近い駅を決定 モバイル端末からの音声提示 •駅に対応づけられた単語を再生
24.
暗記学習実験 -通学中の学習 green(緑)、 calm(穏やかな)、 … office(会社)、 crowded(混雑した)、 …
25.
暗記学習実験 -通学中の学習 電車• に乗車している間は常に学習 使用期間:•
5日間 学習(登校) 学習(下校) 5 = 10回
26.
暗記学習実験 学習前 テスト 通学中に 学習 学習後 テスト 実験手順 •学習前と同じテストに解答
27.
結果 -スコアの変化 •A∼D(ランダム) •E∼H(場所に応ずる)
28.
結果 -スコアの変化した倍率
29.
結果 -正解したデータの内訳 駅周辺の施設と結びつけることで 効率的な暗記が可能?
30.
追加実験 実験協力者• 8名に、1ヶ月以上間隔をおいて再解答 長期間の記憶には適しているのか?
31.
考察 -学習実験 •著者と実験協力者の、通学区間に対する認識の差 場所に応じた項目提示による学習は、ユーザに よって差あり 施設内• にある物体、施設で行う動作を表す単語は適切 に記憶可能
32.
応用 提案手法:• 施設の情報のみを利用 Twitter• 上の投稿を用いた対応づけ -
地域に関するユーザの認識を直接的に取得可能? 迷子 ラーメン 買い物
33.
まとめ ユーザ• の通学区間と暗記項目を対応づけることで、 長距離通勤・通学の時間を暗記学習に活用する手法 を提案 [今後の展望] アルゴリズム• の改善 英単語以外•
の暗記項目を対象とした実験 学習実験• にて、ユーザによって効果に差が出ることを 確認 精度評価• にて、提案手法は従来手法と比較して有意差 なし
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