E-learning meets the Social Semantic Web
當數位學習遇見了社群語意網
Author：
Carlo Torniai, Jelena Jovanovic, Dragan Gasevic, Scott Bateman, Marek Hatala
Simon Fraser University, Canada- University of Belgrade, Serbia- Athabasca
University, Canada- University of Saskatchewan, Canada
Content Type：Conferences
This paper appears in: Eighth IEEE International Conference on Advanced
Learning Technologies
Issue Date : 1-5 July 2008
Speaker：Pei Mei Chen
Abstract 摘要
The Social Semantic Web has recently emerged as a paradigm in which ontologies
(aimed at defining, structuring and sharing information) and collaborative software
(used for creating and sharing knowledge) have been merged together. Ontologies
provide an effective means of capturing and integrating knowledge for feedback
provisioning, while using collaborative activities can support pedagogical theories,
such as social constructivism. Both technologies have developed separately in the
e-learning domain; representing respectively a teacher-centered and a
learner–centered approach for learning environments. In this paper we bridge the gap
between these two approaches by leveraging the Social Semantic Web paradigm, and
propose a collaborative semantic-rich learning environment in which folksonomies
created from students’ collaborative tags contribute to ontology maintenance, and
teacher-directed feedback.
社群語意網是最近出現的一個模式，其中本體論（其目的在於定義、建造和分享
資訊）和協同作業軟體（用於創建和分享知識）已經合併在一起了。本體論提供
有效的記錄方法和為了回饋的供應而整合知識，和同時採用協同活動以支援教學
理論，如：社會結構。這兩種技術分別在數位學習領域裡發展；為了學習環境分
別以教師中心和學習者為中心的方法來表示。在本篇論文中，我們透過利用社群
語意網來縮小這兩種方法之間的差距，並在群眾分類法下提出一個協作且富有語
意的學習環境，從學生所創建的協同標籤有助於本體論的維護和教師應用的回
饋。
補充資料：
Social Semantic Web (s2w) http://en.wikipedia.org/wiki/Socio-semantic_web
社群語意網可以看作是一個集體的知識系統，該系統能夠提供有用的信息，基於
對人類的貢獻，並得到更好更多的人參與網路。社群語意網結合技術、策略和方
法，從語意網、社交軟體和Web 2.0的。
Social Web 翻譯網：http://www.showxiu.com/
The Social Web (including services such as MySpace, Flickr, last.fm, and WordPress)
has captured the attention of millions of users as well as billions of dollars in
investment and acquisition. Social websites, evolving around the connections between
people and their objects of interest, are encountering boundaries in the areas of
information integration, dissemination, reuse, portability, searchability, automation
and demanding tasks like querying. The Semantic Web is an ideal platform for
interlinking and performing operations on diverse person- and object-related data
available from the Social Web, and has produced a variety of approaches to overcome
the boundaries being experienced in Social Web application areas.After a short
overview of both the Social Web and the Semantic Web, Breslin et al. describe some
popular social media and social networking applications, list their strengths and
limitations, and describe some applications of Semantic Web technology to address
their current shortcomings by enhancing them with semantics. Across these social
websites, they demonstrate a twofold approach for interconnecting the islands that are
social websites with semantic technologies, and for powering semantic applications
with rich community-created content. They conclude with observations on how the
application of Semantic Web technologies to the Social Web is leading towards the
"Social Semantic Web" (sometimes also called "Web 3.0"), forming a network of
interlinked and semantically-rich content and knowledge.The book is intended for
computer science professionals, researchers, and graduates interested in understanding
the technologies and research issues involved in applying Semantic Web technologies
to social software. Practitioners and developers interested in applications such as
blogs, social networks or wikis will also learn about methods for increasing the levels
of automation in these forms of Web communication.
http://eblog.cisanet.org.tw/other/Preview.aspx?ArticleID=776
代表各個社群因網路而連結在一起，聯繫可以是單一或是多重的。提供者需要了
解社群的屬性，以提供適合的內容或服務給這個社群，當獲得社群的支持後，社
交網路自然應運而生。
補充資料：http://www.yufulin.net/2010/05/social-web.html
協同作業軟體：以軟體開發協同作業 (Collaborative Software Development—
CSD)工具創造企業價值
http://www.esast.com/Download/YGP(C).pdf
learner–centered：以學習者為中心的建構主義學習環境的建構
（Learner-centered Learning Environments based on Constructivism）
http://lsc.ecnu.edu.cn/yjxm/jgzy/xmcg/001013.htm
social constructivism 社會結構
http://en.wikipedia.org/wiki/Social_constructionism
社會建構和社會建構主義是社會學的理論知識，其認為如何在發展的社會環境裡
發展社會現象或意識的對象。
Folksonomy 群眾分類法
http://zh.wikipedia.org/wiki/%E5%88%86%E4%BC%97%E5%88%86%E7%B1%B
B%E6%B3%95
又稱「分眾分類法」，是一個合成詞，是由社會性書籤服務中最具特色的自定義
標籤（Tag）功能衍生而來。其是指一種由使用者以任意關鍵字進行分類的協同
工作，且使用者彼此之間並能分享分類的資訊。這個現象源於2004出現的許多社
會性軟體(social software)，例如：分享書籤網站「del.icio.us」、相片分享網站
「Flickr」、目標分享網站「43_Things」等。其見證了Web2.0的發展。
http://www.xxc.idv.tw/dokuwiki/folksonomy
是由資訊架構師Thomas Vander Wal 於2004年在一群資訊架構師討論區中所創。
一開始只是用來含括當時持續出現的Web2.0服務(e.g., Furl, Flickr & Del.icio.us)
中，利用使用者自行分類而形成系統整體分類架構的現象；而後續引申成為一種
與專業者分類相對的，由下而上建構的分類系統。
「群眾分類法」在定義上傾向用來表示: (1) 一種資訊分類架構(classification
scheme)，是由使用者從下而上建構的，而非由系統設計者從上而下建構的；(2) 一
種使用者自行分類取代系統分類的現象。
1. Introduction 序論
The Social Semantic Web has emerged recently as a new paradigm for creating,
managing and sharing information through combining the technologies and
approaches from the Web 2.0 [1] and the Semantic Web. While the Semantic Web
aims at giving information a “well-defined meaning, better enabling computers and
people to work in cooperation” [2] through the definition of ontologies, the Social
Web transforms the “old” model of the Web – a container of information accessed
passively by users – into a platform for social and collaborative exchange; in which
users meet, collaborate, interact and most importantly create content and share
knowledge through wikis, blogs, photos and videos sharing services; and activities
such as collaborative tagging.
社群語意網已經成為一個新的創建、管理和分享資訊的模式，其透過結合從
Web2.0和語意網的技術和方法。而語意網的宗旨是透過定義本體論以提供資訊
一個定義明確的意思，還有使電腦和人在工作上有更好的合作，而社群網改造
“舊”的網路模式 – 一個資訊容器由使用者被動地訪問 – 成為一個社群和合
作交流的平台；其讓使用者滿足、協同合作、互動和最重要的為創建內容和分享
知識，並透過wikis、部落格、照片和影音分享服務和特殊活動，如協同標記。
The idea of merging the best of both worlds (namely, through combining the common
formats to define and structure information with the social mechanisms to create and
share this knowledge) has converged in the concept of the Social Semantic Web, in
which socially created and shared knowledge leads to the creation of explicit and
semantically-rich knowledge representations. The Social Semantic Web can be seen
as a Web of collective knowledge systems, which are able to provide useful
information that is based on human contributions, and which improves as more people
participate [3].
想一個兩全其美的想法（即透過結合常見的格式來定義和利用社群機制去創造、
分享這方面知識的結構資訊）融合在社群語意網的概念裡，在社群方面創建和分
享知識，導致明確的創作品和富有語意知識的敘述。社群語意網可以看作是一個
結合知識的網路系統，該系統能夠提供有用的資訊，而其是基於對人類的貢獻和
提高更多人的分享。
Adoption of the Semantic Web has already been shown to be beneficial for learning
environments, in so far as ontologies can effectively model and interrelate information
describing learning content, learning activities and learners; and thus improving
content personalization and feedback provisioning [4].
採用語意網已經被證實有利於學習環境，在本體論的範圍內能有效模式和使資訊
相互關聯以描述學習內容、學習活動和學習者，並且因此提高內容的個性化和回
饋的供應。
However, despite the many promising aspects that stem from having structured
information with welldefined meaning (intelligent search engines, content correlation
according to semantic annotation, etc.), the Semantic Web is still not widely adopted.
This is mainly due to the difficulties in ontology creation and maintenance, and the
process of semantic annotation.
然而，儘管有許多有前景的觀點，是由定義明確意思的結構資訊所造成的（智慧
搜尋引擎、根據語意註釋的內容關聯性等），但語意網仍然沒有被廣泛地採用。
這主要是由於在本體論的創建、維護和在語意標註的過程中的困難。
The Social Semantic Web paradigm can play a crucial role in the context of e-learning:
on one hand, facilitating a larger adoption of ontology-based elearning systems
(overcoming the difficulties related to domain ontologies creation and update) and on
the other hand, providing enhanced feedback based on collaborative activities.
Moreover, e-learning systems can evolve according to the new forms of creation,
exchange and fruition of knowledge offered by the Social Web paradigm (e.g., wikis,
blogs, and social networks). We envision, as such, the possibility of the birth of the
“Education Social Semantic Web”, where pedagogically focused learning materials
and activities are easily created, shared, and used by students and teachers; without
the need for detailed knowledge engineering skills or know-how of advanced
technologies. Along the lines of this vision, in this paper, we present an integrated
framework that leverages the Social Semantic Web paradigm to enable
folksonomy-driven ontology maintenance and feedback provisioning, based on
students collaborative activities.
社群語意網模式在數位學習裡扮演著很重要的角色：一方面，促進一個大量採用
以本體論為基礎的數位學習系統（克服相關領域本體論的創建和更新的困難），
而另一方面，提供增進基於協作活動的回饋。此外，數位學習系統可以根據社群
網的模式（例如：維基、部落格和社交網路）所提供的知識創建、交流和成果等
新形式的演變。我們設想，就其本身而論，根據學生和教師的教學法可能誕生出
“教育社群語意網”，而其著重於學習的素材和活動是能夠簡單地創建、分享和
使用，而其是不需要詳細的知識工程技術或先進技術的方法。隨著這一個構想，
在本篇論文中，我們提出了一個整合架構，其充分利用社群語意網模式，且基於
學生的協作活動使群眾分類法驅使本體論的維修和回饋的提供。
2. Collaborative Semantic-rich Learning Environment 協同合作且富有語意的
學習環境
In our previous work, we have demonstrated that semantic rich e-learning systems –
in which ontologies interrelate information about learning content, learning activities
and learners – can improve the current state-of- practice in generating feedback for
online educators [4]. In particular, we have developed the LOCO-Analyst tool, which
provides online educators with appropriate and reliable feedback about
student-interactions with learning materials, as well as their mutual interactions
during the learning process. The main motivation for developing LOCO-Analyst was
to inform online educators about behavior and performance of their students, and help
them rethink the content and learning design of the courses they teach. The initial
evaluation of LOCO-Analyst showed that teachers perceived and appreciated the
qualitative improvements it achieves in providing feedback.
在我們先前的作業裡，為了線上的教師在形成的回饋裡，我們已經證明富有語意
網的數位學習系統－在本體論裡有關學習內容、學習活動和學習者的相關資訊－
能改善目前的實踐狀態。特別的是，我們已經開發LOCO分析工具，在學習的過
程中，線上教師與學生透過學習教材彼此互動，以提供適當且可信賴的回饋。為
了發展LOCO分析的主要動機是告知線上教師有關學生的行為和成績，並幫助他
們重新思考他們所教的課程內容和學習設計。在提供回饋上，LOCO分析的最初
評估顯示，教師察覺和讚賞其實現了品質的改善。
LOCO-Analyst is based on Semantic Web technologies. It is built on top of the
LOCO (Learning Object Context Ontologies) ontological framework [5], which we
have developed to enable a formal representation of contextual learning object data.
The LOCO ontologies enable unambiguous representation and integration of
contextual learning data from the different e-learning systems and tools that students
use during the learning process. Furthermore, LOCO-Analyst exploits semantic
annotation (i.e. annotation with ontological concepts) to interrelate the diverse
learning artifacts used, or produced, during the learning process (such as lessons, tests,
messages exchanged during online interactions). Finally, LOCO-Analyst leverages
reasoning to derive meaningful insight about the learning object context data.
LOCO分析是基於語意網的技術。它是建立在LOCO（學習物件情境本體論）本
體論的架構上，而我們已經發展到使情境學習物件資料能夠成為一個正式的敘
述。LOCO本體論在學生學習的過程中使用不同的數位學習系統和工具，而從中
獲取的資料能夠清楚的敘述和整合情境學習的資料。此外，LOCO分析利用語意
註釋（即透過本體論的概念）去關聯不同在學習過程中使用或產生的學習成品（例
如：課業、測驗、在線上互動的訊息交換）。最後，LOCO分析利用推論去取得
有意義且深入了解關於學習物件的情境資料。
LOCO-Analyst was developed as a generic feedback provision tool, which is easily
adaptable for the use with diverse e-learning environments. This is made possible by
implementing feedback functionalities that link with the ontologies of the LOCO
framework. However, the major obstacle for the wider adoption of LOCO-Analyst
lies in the difficulties of creating and maintaining domain ontologies upon which the
tool depends.
LOCO分析被開發作為一個通用回饋供應工具，其為了容易地適應與不同的數位
學習環境的使用。透過實施與LOCO架構的本體論相連結的回饋功能使其成為可
能。不過，為了廣泛採用LOCO分析的狀態，而其主要地障礙是在於創建和取決
於維持領域本體論其工具的困難。
LOCO-Analyst’s focus on providing feedback for teachers, classifies it as a
teacher-centric environment. With the additional aim of improving the learners’
experiences in using learning environments, we have decided to make use of social
constructivist theory by leveraging collaborative activities. Aiming to enable the use
of this pedagogical theory, we have developed the Open Annotation and Tagging
System (OATS), as an open-source tool that allows learners to collaboratively create
and share knowledge, by adding highlights, tags and notes in HTML-based learning
content [6]. OATS is a multi-purpose annotation tool that can be easily integrated into
any e-learning system. For example, it has been integrated with the iHelp Courses
Learning Content Management System (LCMS) [7], and used in several different
e-learning courses deployed at the University of Saskatchewan.
LOCO分析的重點是為教師提供回饋，並以教師為中心的環境來分類。由於附加
的目標，在使用學習環境下提高了學習者的經驗，而且透過利用協作活動我們決
定使用社會結構理論。為了使教學理論能夠使用，我們已經開發了開放註解和標
記的系統（OATS）
，其當作一個開放原始碼的工具，可以讓使用者協作創建和分
享知識，並透過在以HTML為基礎的學習內容裡增加亮點、標籤和註釋。OATS
是一種多用途的註釋工具，其可以很容易地整合任何數位學習的系統。例如：它
已經整合了iHelp的課程學習內容管理系統（LCMS），並使用部署在薩斯喀徹溫
大學裡好幾個不同的數位學習課程。
Obviously, these two tools represent a teacher-centered (LOCO-Analyst) and
learner-centered (OATS) approaches to learning environments. We have decided to
integrate these tools in order to investigate the benefits that their synergy has to
offer,which include:
顯然，這兩種工具是以教師為中心（LOCO分析）和以學習者為中心（OATS）
的代表方法用於學習環境。我們決定整合這些工具是為了調查它們之間協同合作
所提供的好處，其中包含：
i) the facilitation of ontology maintenance based on the results of learners’
collaborative activities;
ii) the provision of enhanced feedback for teachers describing students’
comprehension of the course content.
i) 基於學習者協作活動的結果以便利本體論的維持；
ii) 為了教師描述學生的課程內容的理解能力，以增加回饋的供應。
Accordingly, we have combined LOCO-Analyst, OATS, and the iHelp Courses
LCMS, to create our collaborative semantic-rich learning framework depicted in
Figure 1. This framework allows students to collaboratively tag the learning content
in iHelp Courses LCMS using OATS. The results of their tagging activities are
accessed by LOCO-Analyst which in turn analyzes them, and uses them to provide
teachers with advanced feedback about students’ comprehension of course content
(see Sect. 4).
因此，我們結合LOCO分析、OATS和iHelp的課程學習內容管理系統，以創建我
們的富於語意協作的學習架構，如圖1所示。此架構允許學生在iHelp的課程學習
內容管理系統裡使用OATS來協作學習內容的標籤。根據LOCO分析依序分析他
們訪問標記活動的結果，並使用他們所提供高等的回饋給教師，而且是有關學生
對課程內容的理解力（見第4節）。
LOCO-Analyst also provides interactive visualization of the course domain ontology
aiming to facilitate the process of ontology maintenance (see Sect. 3). We have
developed an algorithm for computing context-based relatedness between students’
tags and ontological concepts, which we use to further assist the teachers' task of
ontology maintenance, by suggesting the most relevant tags for a particular concept.
The algorithm is based on the idea that the ontology itself defines a “context” for its
concepts. So, when computing the relatedness between a concept and a tag, the
surrounding concepts (forming the ‘context’ of the concept in question) must also be
taken into account. To support this notion, we compute context- based relatedness that
contextualizes a semantic relatedness measure (e.g., NSS-Gwikipedia [8]) according
to the domain ontology structure. This algorithm has been applied in two separate
use-cases, described in the next two sections. These sections present, respectively,
how we have implemented: folksonomy-driven ontology maintenance, and feedback
provisioning based on students’ collaborative activities.
LOCO分析還提供課程領域本體論的互動式形象化，其目的是方便於本體論的維
修過程（見第3節）。我們已經開發出一種演算法，是為了計算基於情境與學生
的標籤和本體論的概念之間的關係，並透過建議最相關的標籤給特定的概念，我
們將進一步協助教師本體論的維修工作。而演算法是基於本體論本身為了概念而
定義了一個情境的想法。因此，當在計算概念和標籤之間的關係時，環境的概念
（在問題中形成的情境概念）也必須被考慮。為了支持這個概念，我們計算基於
情境的關係，其考慮一個語意相關方法（NSS - Gwikipedia）根據領域本體論的
結構。此演算法已應用在二個不同的使用情形裡，並於下兩節中描述。目前這些
章節，我們已各別實施：群眾分類法驅使本體論的維修，和基於學生協作活動的
回饋供應。
補充資料：
NSS – Gwikipedia
http://wisdombase.net/wiki/index.php?title=%E8%AE%A1%E7%AE%97%E5%93%
81%E7%89%8C%E7%9A%84%E8%AF%AD%E4%B9%89%E7%9B%B8%E4%B
C%BC%E5%BA%A6%E7%9A%84msr%E6%96%B9%E6%B3%95%E7%9A%84%
E7%A1%AE%E5%AE%9A
互動
檢查
修改/豐富
Figure 1. Collaborative semantic-rich learning environment
協同合作且富有語意的學習環境
3. Folksonomy-driven Ontology Maintenance 群眾分類法驅使本體論的維修
Aiming to facilitate the educators’ task of maintaining domain ontologies, our
framework leverages folksonomies generated out of the tags that students assigned to
the learning content during the learning process.
為了方便教師維護領域本體論的工作，我們的架構利用群眾分類法產生的標籤，
其為學生在學習的過程中的學習內容。
Figure 2 presents the user interface of the LOCO-Analyst extension that enables
teachers to refine the domain ontologies of their courses, which is based on students’
tagging activities, created with OATS. The extension consists of a tag cloud
visualizing students’ tags (B) and a graph-based visual representation of the course
domain ontology (C).
圖2顯示LOCO分析的延伸的使用者介面，其能讓教師改進他們課程的領域本體
論進而創建OATS，而這是基於學生的標籤活動。延伸是由一個標籤雲可視化的
學生標籤（B）和基於圖形的可視化表示課程領域本體論。
補充資料：
標籤雲 http://zh.wikipedia.org/zh-tw/%E6%A8%99%E7%B1%A4%E9%9B%B2
標籤雲或文字雲是關鍵詞的視覺化描述，用於匯總用戶生成的標籤或一個網站的
文字內容。標籤一般是獨立的詞彙，常常按字母順序排列，其重要程度又能通過
改變字體大小或顏色來表現，所以標籤雲可以靈活地依照字序或熱門程度來檢索
一個標籤。 大多數標籤本身就是超級連結，直接指向與標籤相聯的一系列條目。
The domain ontology is presented using an interactive graph that we have
implemented using Prefuse1 open-source Java visualization framework. A teacher can
explore the ontology by zooming in and out and/or changing the focus of the graph
view by clicking and dragging nodes.
領域本體論目前是使用互動的圖示，我們已經使用Prefuse 1，其使用開放原始碼
-Java來實行視覺化的架構。老師可以透過放大和縮小和/或點擊和拖動節點改變
圖形視圖的焦點來探究本體論。
補充資料：
Prefuse
http://bruce620.blogspot.com/2010/12/prefuse-setting.html
prefuse為一套視覺化的套裝工具，其出自於http://prefuse.org/，使用語言為Java，
可用於Social Network。
http://www.open-open.com/open85460.htm
prefuse是一個使用者介面包用來把有結構與無結構資料以具有交互性的視覺化
圖形展示出來，這包括的資料有任何可以被描述成一組實體(或節點)或者可以被
連接在一起的一些關係(或邊緣)。prefuse支援的資料包括具有層次性(如:檔案系
統,組織圖),網路(網路拓撲,網站連結),和甚至是沒有連接的資料集(如:時間線)。
The tag cloud employs the size and color of tags to convey to teachers information
describing the tags popularity and relevancy, respectively. We have found these two
feedback variables relevant for supporting teachers’ task of enriching domain
ontologies. The size of a tag reflects its popularity, which is calculated by the number
of times that tag was used to annotate a particular piece of learning content. The
saturation of a tag’s color reflects its relatedness to the ontological concepts
encapsulated in the content of the currently selected lesson (Figure 2A), as evaluated
from our algorithm – darker colors denote more relevant tags.
標籤雲的標籤分別採用大小和顏色去傳達給教師的資訊，其描述標籤的普及和相
關性。我們發現這兩個回饋變數與支援教師豐富領域本體論的工作有關。標籤的
大小反映了它的普及，這是由標籤用於註釋特殊的學習內容的次數來計算的。標
籤顏色的飽和度在目前選擇的課程裡（圖 2 A的部分），反映其有關本體論的概
念，如從我們的演算法評估 － 較深的顏色表示更多相關的標籤。
合適的縮放 儲存改變
搜尋
Figure 2. An extension of the LOCO-Analyst tool for the ontology maintenance task
為了本體論維修工作的LOCO分析工具之延伸
A teacher’s interaction with the LOCO-Analyst’s extension for ontology maintenance
can be described as follows: as the teacher selects a lesson (or a complete learning
module) from the tree-like representation of the course structure (Figure 2A):
為了本體論的維護教師的互動與LOCO分析的延伸能描述如下：作為教師從課程
結構樹（圖 2 A的部分）選擇一個課程（或一個完整的學習模組）：
1. The visual representation of the ontology (Figure 2C) changes to emphasize the
concepts relevant for the selection being made. The colors of relevant concepts
become darker as the relevancy with explored content increases.
可視化表示的本體論（圖 2 C的部分）的變化，強調為了選擇相關的概念。相關
概念的顏色變深與探究的內容增加是有相關性的。
2. The tag cloud (Figure 2B) is populated with tags related to the selected lesson.
標籤雲（圖 2 B的部分）是位於與選擇課程有關的標籤。
The teacher selects a tag (from the tag cloud view) that (s)he wants to add to the
ontology and simply drags that tag towards the ontology view panel (Figure 2C).
When the tag is ‘over’ the ontological concept it should be related to (according to the
teacher’s opinion), the teacher ‘drops’ it. Instantly, a pop-up menu appears offering
various options for establishing a connection between the selected concept and the tag
(e.g., adding a tag as a sub-concept or as a related concept). As the teacher selects one
of the available options for ontology enrichment, the ontology view gets updated to
reflect the changes to the ontology. In addition, the teacher has an option to postpone
the decision about the tag relation and note it for later consideration. LOCO-Analyst
supports this by adding both the tag and the concept into the teacher’s notes, for later
consideration.
老師選擇一個標籤（從標籤雲的視圖），其他（她）想要添加到本體論，並簡單
地拖曳標籤至本體論圖視窗格裡（圖 2 C的部分）。當標籤是「超出」本體論的
概念，其應該與其相關的教師來丟棄它（根據教師的意見）。立即出現一個彈跳
功能表提供各種為了建立選擇概念和標籤之間的關聯建議（例如：加入一個標籤
作為子概念或作為相關的概念）。為了本體論的豐富作為老師需選擇一個可用的
選項，讓本體論的視圖能得到更新以反映出本體論的變化。此外，為了以後的審
議，教師有一個選項是延遲決定有關標籤的關係和註釋。為了以後的審議，LOCO
分析支援加入標籤和概念到教師的筆記裡。
4. Feedback Provisioning Based on Students Collaborative Activities 基於學生
協作活動的回饋供應
By leveraging data from OATS, our framework allows teachers to be informed about
a student’s comprehension of the course content based on their collaborative tagging
activities.
透過利用從OATS的資料，我們的架構允許教師被通知有關學生基於協作標籤活
動對於課程內容的理解能力。
To present the teacher with this kind of feedback, we have extended the dialog that is
used in LOCO-Analyst for displaying feedback about one particular student (Figure
3). The dialog is based on tab panels, each one presenting a specific kind of
information that LOCO-Analyst contains about the student (i.e., the information
generated from the available data on the student's interaction with the LCMS). In
particular, the dialog comprises four tabbed panels, labeled ‘Forums’, ‘Chats’,
‘Learning’ and ‘Annotations’. Whereas the former two panels are aimed at informing
teachers about the student's online interactions (in discussion forums and chat rooms,
respectively) with other participants in the learning process, the latter two are
intended for presenting information regarding the student's interaction with the
learning content (i.e., reading, annotating, and commenting).
目前利用這種回饋給教師，我們已經延伸至利用在LOCO分析裡顯示有關特定學
生的回饋對話框（圖 3）。該對話是基於標籤窗格，每一個都表達特定種類的資
訊，其LOCO分析包含有關該學生（例如：資訊是從利用LCMS系統的學生在互
動上所產生出有用的資料）
。特別是，該對話框包括四個標籤窗格，為“論壇”，
“聊天”，“學習”和“注釋”。而前兩個窗格的目標是通知教師有關學生在學
習的過程中，分別在線上論壇和聊天室與其他參與者的互動，後兩者是打算表達
關於學生與學習內容的互動資訊（即閱讀、註釋和評論）。
論壇
聊天
預習 筆記
Figure 3. A screenshot of the interface providing feedback based on students
collaborative tagging
基於學生的協作標籤所提供的回饋介面的截圖
Figure 3 presents a screenshot of the Annotations panel, which provides feedback
based on students collaborative tagging activities. On the left hand side of the dialog
(Figure 3A), there is a tag cloud presenting tags that all students used for annotating
the course content. We make a visual distinction between the tags that the selected
student – let us call him Tom – used, and those that other students used but Tom did
not. This distinction is visualized by making active (i.e., mouse pointer turns into a
hand indicating a clickable tag) and blue only those tags that Tom has used; whereas
other tags are not clickable and are painted in grey. This allows the teacher to easily
identify to what extent Tom’s perception of the course content overlaps with that of
his fellow students. After the teacher selects one of Tom’s tags from the tag cloud, the
course content annotated with that tag is presented in the form of a tree as shown in
Figure 3B. The tree root represents the course, branches are lessons annotated with
the selected tag and tree leaves are parts of the lesson’s content annotated with the
selected tag. After the teacher selects one annotation (i.e., a tree leaf), the part of the
lesson forming its ‘context’ is presented in the Annotation Preview panel and the
student’s (i.e., Tom’s) notes related to that annotation are listed in the Notes Preview
panel (Figure 3C).
圖 3給出了一個註釋窗格的截圖，其提供基於學生協作標籤活動的回饋意見。在
左邊的對話框（圖 3 A的部分），有一個標籤雲呈現的標籤，其讓全部的學生用
於註釋課程的內容。我們做標籤之間的視覺區別，使用選擇的學生 － 我們稱他
為湯姆，而這些標籤是其他的學生有使用，但湯姆沒有的。這種視覺化的區別是
根據作出動作（即滑鼠指標變換成手的形狀，就可點擊該標籤）和藍色的只有湯
姆使用的標籤；而其他的標籤是不能點擊，並塗以灰色。這讓老師可以輕鬆地確
定湯姆與其他的同學們在課程內容上看法的重疊性。之後老師選擇湯姆於標籤雲
裡的其中一個標籤，該課程內容註釋的標籤是顯示在樹狀結構裡，如圖 3 B的部
分所示。樹根是代表課程，枝是課程註釋包含選擇標籤，還有樹葉是部分課程內
容的註釋也包含選擇標籤。之後老師選擇一個註釋（即樹的葉），部分的課程形
成它的「情境」是出現在註釋預覽窗格和學生（即湯姆）的相關記錄，其註釋條
列在筆記預覽窗格裡（圖3 C的部分）。
The idea behind the suggested interaction is to help the teacher evaluate the student’s
conceptualization of the course content. The assumption is that the tags that the
student used for annotating the content reflect his perception (or even comprehension)
of the content. The suggested visualization would also help teachers easily spot all
parts of the course that the tags were used with, and thus help them reveal some of the
students’ misconceptions.
建議互動背後的想法是，以幫助教師評估學生課程內容的概念化。假設標籤是學
生為了註釋內容而使用，且反映他對內容的看法（或甚至是理解）。建議的視覺
化也將幫助教師很容易地發現標籤使用過程中的所有部分課程，和從而幫助他們
揭露一些學生的錯誤想法。
We are currently working on some further visual indicators to be added to the content
annotation tree (Figure 3B) to indicate the level of agreement between the teacher’s
and the student’s conceptualization of the content. To accomplish this, we will make
use of the (semantic) annotations of course content with concepts of the domain
ontology: students’ tags and a context-based measure of relatedness between ontology
concepts and tags (a variant of the one used for ontology maintenance). Since the
domain ontology reflects (or at least should reflect) the teacher’s conceptualization of
the course content, using our measure of relatedness between ontology concepts and
tags we can identify where the teacher’s and the student’s conceptualizations overlap
and where they diverge.
我們現在的工作正在進一步的對一些視覺指標，將其添加到內容的註釋樹（圖 3
B的部分），並表明教師和學生之間的內容概念化，以達成一致的水平。而要做
到這一點，我們將利用領域本體論的概念課程內容的（語義）註釋：學生的標籤
和一個基於情境的本體論概念和標籤之間的相關評估（為了本體論維修使用的變
形）。由於領域本體論反映（或至少應該的反映）該教師概念化的課程內容，使
用我們的本體論概念和標籤之間的相關評估，我們能發現教師和學生概念化的重
疊和他們的分歧。
5. Related Work 相關工作
There are many aspects of e-learning environments that are affecting the educational
process, including (but not limited to) domain knowledge, knowledge artifacts,
pedagogical models, user behavior and characteristics, social interactions, and the
platforms for delivery (e.g., mobile phones, or a web-based LCMS). If advanced
educational services are to be provided, all these e-learning aspects need to be
captured and represented in an integrated way - in a unified knowledge space [9]. This
is precisely the reason why Semantic Web technologies have been recognized as one
of the major directions for the next-generation e-learning environments. There have
been numerous proposals for leveraging ontologies in elearning systems, which are
aimed at covering some of the aforementioned aspects of e-learning environments [10]
[11] [12].
有許多數位學習環境的觀點，其影響教育的過程中，包含（但不限於）領域的知
識、人工知識、教師模式、使用者行為和特色、社交互動和傳送平台（如：行動
電話或基於網路的LCMS）。如果要提供先進的教育服務，所有這些數位學習的
觀點在一個整合的方式裡是需要進行採集和描繪 － 在一個統一的知識空間
裡。這正是為什麼語意網技術已被確認為下一代數位學習環境的主要方向之一的
原因。為了利用在學習系統裡的本體論已經有許多建議，而其針對一些涉及上述
觀念的數位學習環境。
On the other hand, Social Web (or Web 2.0) based approaches, such as collaborative
tagging and social bookmarking, have so far gained a high level of adoption even in
every-day e-learning practice (e.g., Elgg is used in more than 170 online courses at
the University of Brighton [13]), and has even impacted the development of new
pedagogical theories such as connectivism [14]. Combining the user-friendliness of
collaborative activities with structured ontological definitions is starting to be seen as
a solution for the convergence of these approaches into the new Social Semantic Web
paradigm. An attempt at leveraging this paradigm in e-learning environments has
been proposed in [15] where Westerski, et al. propose a method of assembling an
on-demand curriculum from existing learning objects provided by e-learning services
suppliers. Essentially, the proposed approach is based on gathering all student
interactions and activities, representing them semantically, and exploiting this
information together with (semantically represented) data of the student’s current
course progression. This approach would allow for accurate selection and sequencing
of learning objects that would best suit the student’s needs. This work is in its early
stage, and for the moment, only the theoretical model has been developed.
另一方面，社群網站（或Web 2.0）基礎的方法，如協作標記和社群書籤，到目
前為止甚至在每一天的數位學習實踐裡已獲得採用高水準（例如：Elgg在布賴頓
大學裡已經超過170個線上課程）
，甚至影響新的教學理論的發展，如連接理論。
結合結構本體論定義的使用者友好的協作活動，其開始被當作為了解決集合這些
方法到新的社群語意網的模式裡。Westerski（人名：諾伯特）在數位學習環境中
已經提出利用此模式嘗試，以及其他建議收集的方法是從利用數位學習服務供應
商所提供的現行學習物件且經要求的課程。從本質上來講，該建議方法是基於收
集所有學生的互動和活動，代表他們的語意，並利用此資訊和學生目前課程進展
的（語意代表的）資料。這種方法將允許準確地選擇和學習物件的排序，其將是
最適合學生需求的一套系統。這項工作是在初步階段，並就目前而言，只有開發
理論模型。
補充資料：
連接理論（connectivism） http://blog.udn.com/robertyjlai/2763417
連接理論（connectivism）是數位時代的學習理論，過去數十年來，學習歷經許
多改變。在許多環境下，行為理論、認知理論與建構理論提供我們對學習的有效
觀點。但當學習進入非正式、網絡化及由科技驅動的時代後，這些理論就不夠用
了。連接理論的基本原則如下：













認知與情緒的整合在「意義的決定（make meaning）」上很重要，思維與
情緒相互影響。只考慮一個維度的學習理論，排除了大部分學習是如何發
生的狀況。
學習要有終點目標—換句話說，就是增加我們「做某件事」的能力。這個
增進的能力可能具有實體意義（例如學習溜冰或使用新的軟體工具的能
力），或是能幫助我們在知識時代更有效的工作（自覺、個人資訊管理等
等）。「學習的全相（whole of learning）」並不只是獲得技能或知道而已—
付諸實踐是一個必須的項目。激勵和快速決策的方式通常決定了一個學習
者是否能將所學應用出來。
學習是一個連接特殊節點（node）或資訊資源的過程。當一個學習者進入
一個既有網絡時，他能以指數曲線的速度改善自我的學習。
學習可能存在於非人際的接觸中。學習（具有知道某些事，但並不一定行
動的特質）可存在於一個社群、網絡或資料庫中。
「知道更多的能力」比「目前知道多少」更為重要。「知道往何處尋找資
料」比「知道這些資料」還重要。
為促成學習，就須要建立與維護連接（connection）
。與外在的連接比單純
只想瞭解一個簡單概念，可獲得更大的回報。
知識與學習存在於多元的觀點中。
學習可在許多不同的方式下產生。例如課程、電子郵件、社群、對話、網
路搜尋、電子郵件論壇或閱讀部落格等。課程不再是主要的學習管道。
現代社會的有效學習需要不同的手法與個人技巧。例如，有能力看到不同
觀念、意見與領域之間的連結，就是一個核心技能。
組織學習與個人學習是一個整合性任務。個人知識包含在網路內，它饋入
組織與機構，然後再回饋到網路，並持續提供給個人學習。連接理論試圖
兼顧對個人與組織學習的理解。
現時性(獲得正確的最新知識)是所有連接派學習者的意願。
決策本身就是一個學習過程。選擇要學甚麼以及賦予所取得資訊的意義，
其實是透過不斷變化的現實來決定的。現在是對的答案，可能明天就錯
了，因為資訊的溫度改變了決策。
學習是一個創造知識的過程….不僅僅是吸收知識而已。學習工具和設計方
法應該設法在這個學習特色上投注心力。
取自 George Siemens http://connectivism.ca/about.html
Elgg http://twpug.net/modules/newbb/viewtopic.php?topic_id=4625
Elgg.org 是一個開放原始碼的社交應用引擎，Elgg 提供優雅、彈性與可擴充的解
決方案，適合組織、社群與個人。透過它可以得到社交技術的好處。
網址： http://elgg.org/
6. Conclusion 結論
In this paper, we have pointed out benefits that the Social Semantic Web paradigm
can bring to e-learning environments. In particular, we have presented an integrated
framework for the combined usage of folksonomies and ontologies. Our framework
eases the process of ontology maintenance and enhances the feedback provisioning
capabilities of semantic-rich learning environments. This contribution is an important
step in the road of wider adoption of Semantic Web technologies in learning
environments - enabled by leveraging Social Web approaches- as well as an
improvement of educational process by better comprehension of students’ activities
from which both learners and teachers will benefit.
在本篇論文中，我們已經指出社群語意網模式能帶來數位學習環境的好處。特別
的是，我們提出了一個為了結合運用群眾分類法和本體論的整合架構。我們的架
構簡化了本體論維修的過程和增加富有語意的學習環境的回饋供應功能。此貢獻
在學習環境中廣泛採用語意技術的道路上是很重要的一步 – 能夠透過利用社群
網的方法 – 以及由於學習者和教師都將受益，所以透過學生活動的更好地理解
力，進而改善教育的過程。
Our future work will deal with the collection of data coming from a deployment of
our framework in an actual e-learning setting. In this way, we will be able to perform
an extensive evaluation of the proposed interfaces and gather feedback on what
additional types of user interactions might be valuable, so that they can be added to
the future releases of the framework. In addition, we intend to collect detailed usage
data of students and teachers within our framework in order to evaluate the
effectiveness of our approach.
我們今後的工作將處理從部署我們的架構裡收集來的資料，並在實際的數位學習
裡設定。這樣，在附加的使用者互動類型可能的價值上，我們將能夠執行廣泛地
評估建議的介面和收集回饋，以便於他們能夠增加到未來發表的架構上。此外，
我們打算在我們的架構範圍內收集學生和教師的詳細使用數據，以評估我們的方
法之有效性。