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1
KALS
Knowledge-based Annotation
Learning System
NCCU DLLL
Yung-Ting Chen
2018.5.18
2
Raw HTML
Reading Material
Enhancing Digital Reading
KALS
3
KALS Features
KALS
Reading Support
Collaborative
Learning
Behavior Analysis
4
Annotation on Webpage
Highlighters
Rich Text
Note Editor
Multimedia Voice Record
5
External Congnitive Support
● Webpage (Google)
● Dictionary
● Wikipedia
● Video (YouTube)
● Image
(Google Images)
Search
6
Reading with Peers
My Annotation
(with underline)
Others
(with brown color)
7
Collaborative Learning
on Annotation
Peer
A
Peer
B
Me
8
Reading Behavior
Database
Reading Behavior Analysis
Learning Profile
Dashboard
9
Annotation Recommend
Annotation
Score
Annotation
Features
Strategy
Word Count
Speech
Position
Annotation
Consensus
Consensus
Count
Recommendation
Algorithm
10
Reading Sequence Mining
3
4
1
7
3
2
1
3
4
5
1
7
37
3
1
7
3
Early
Later
Reading Guide
Paragraph Number
Student
C
Student
B
Student
A
Algorithm
2 1
11
Anxiety Diagnosis
Diagnosis DashboardRule Tree
12
Thank you
for your attention
http://public-kals.dlll.nccu.edu.tw wKALS

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20180518 kals

Hinweis der Redaktion

  1. 大家好,我是Yung-Ting Chen I want to introduce a digital reading enviroment, named KALS, Knowledge-based annotation learning system. --------------- 投影片:https://docs.google.com/presentation/d/1WgxrS7CkKjpTiNjypDwMJc2iqo2XsLuukR0_hosCwa8/edit?usp=sharing 講稿:https://docs.google.com/document/d/1Jz5uCKo5axHZxgxSt28mxdGscy0YVciKhpdJbUv7Sxw/edit?usp=sharing
  2. 現在人已經很習慣用網頁瀏覽文章,但是網頁只能看,它缺少了很多閱讀需要的支援。 KALS利用JavaScript程式增強了原本的HTML網頁,讓它成為一個多功能的數位閱讀環境 因此KALS可以用於任何以HTML呈現的系統中,包括Moodle、Blog
  3. 網頁在經過KALS增強之後,它有三個主要功能: 閱讀支援(reading support) 合作學習(Collaborative Learning) 行為分析(Behavior Analysis)
  4. 首先,在KALS中,我們可以在網頁的任何文字上撰寫標註(write a annotation on the web content)。 KALS提供學生多種顏色與類型的Highlighters,各別代表不同的閱讀策略(reading strategies)。 學生可以在標註的文字上添增筆記(note),而且這個筆記允許豐富格式(rich format) 學生可以在筆記加入粗體、不同顏色的字、超連結等等。 他們甚至可以加入圖片、影片,或是錄下自己的聲音。 錄音功能在練習英文口說的時候非常有用。 ------------ 多種顏色的標註 有格式的筆記 插入圖片 影片 錄音
  5. KALS也提供了外部認知支援(external congnitive support) 遇到學生不懂的字,他們可以在網際網路上搜尋字的意思 例如在Google上搜尋網頁(search webpages in Google)、Dictionary、Wikipedia、Video in TouTube、Image in Google Images
  6. KALS不僅能夠提供閱讀支援,它也將數位閱讀轉換成一個合作學習的環境 學生在閱讀的時候,不僅可以看到自己所寫的標註,也可以看到其他人的標註內容。 ------------------------ http://exp-kals-moodle-2014.dlll.nccu.edu.tw/mod/page/view.php?id=15
  7. 學生在撰寫標註的時候,下面可以看到其他學生在同一個位置所撰寫標註 這時候學生去回應別人的標註,跟別人進行討論,或是對其他人的內容評分(rating) 像這種基於標註的討論,有助於讓討論主題聚焦在閱讀教材上,提升討論的效率。 ----------------------- 20140120 合作式數位閱讀之標註路徑探勘 - public https://docs.google.com/presentation/d/1LzobWZZTdeAeobTUsUd9gWroWUJvgpuhIeCxLRpZfK0/edit#slide=id.p23
  8. 做一個數位學習環境,KALS記錄了學生的各種閱讀行為。 學生可以透過Learning Profile Dashboard來統計自己撰寫的標註。 我們也利用閱讀行為記錄進行了許多研究。
  9. 第一個應用是標註推薦(annotation recommend) KALS會分析每個標註的特徵和計算標註共識。 標註的特徵包括學生使用的策略,以及標亮文字的詞性(highlighted text’s speech)、字數、和位置 標註共識是指有多少學生都在同個文字上撰寫標註 以及標註獲得的評分 然後我們發展了一套演算法計算它的標註分數(annotation) KALS會對標註分數較低的學生提供建議,推薦它去看標註分數較高的標註內容。
  10. 我們也做了閱讀順序探勘的研究 舉例來說,這邊有三個學生,他們閱讀段落的順序都不一樣。 閱讀段落以編號表示 學生A從第一段循序讀到第五段 但學生B跟學生C都用不同順序跳著讀 KALS使用演算法探勘這群學生共同的順序,最後找出學生們會用1、7、3的順序讀這篇文章 這個探勘結果就可以提供給學生作為閱讀指引(reading guide)
  11. 透過分析學生的閱讀行為,我們還能夠診斷(diagnosis)學生的焦慮狀態。 左邊這張圖是我們透過資料探勘(data mining)歸納出不同閱讀行為和性別的學生可能的焦慮程度的規則樹(rule tree) 有了這樣的規則樹之後,教師就可以在右邊的診斷儀表板(diagnosis dashboard)看到每一位學生的焦慮診斷結果 如果有學生的焦慮程度較高,教師就可以立刻去關心學生的狀況,提供即時的協助。
  12. 接著讓我們來實際展示KALS。 ---------------- 那我的介紹就到這邊,接下來我們請Betty介紹KALS與遊戲結合的應用。