Support for Resource-Based Learning on the Internet

Support for Resource-Based
Learning on the Internet

Resource Based Social Search
Learning Model

Personal
CROKODIL Resource
Network

Recommendation
Systems
Collaboraive Community
Filtering Resource
Semantic Network
Ranking Tagging
Recommendation
Results Explanations
Activity Ratings

Tag Types

KOM - Multimedia Communications Lab
Feedback Loop
Prof. Dr.-Ing. Ralf Steinmetz (Director)
Dept. of Electrical Engineering and Information Technology
Diplom.-Inform. Mojisola Anjorin Dept. of Computer Science (adjunct Professor)
TUD – Technische Universität Darmstadt
Rundeturmstr.10, D-64283 Darmstadt, Germany
Mojisola.Anjorin@KOM.tu-darmstadt.de Tel.+49 6151 166150, Fax. +49 6151 166152
Tel.+49 6151 166160 www.KOM.tu-darmstadt.de

GKEL___Support for RBL on the Internet___v.12___2011_MA.ppt 03. May 2011
© 2011 author(s) of these slides including research results from the KOM research network and TU Darmstadt. Otherwise it is specified at the respective slide

Thursday, May 19, 2011
Welcome to my talk on “Support for Resource-Based Learning on the Internet”.

Resource-Based Learning

KOM – Multimedia Communications Lab
2

Resource-based learning (RBL) means learning by using resources found on the Web such as on
blogs, YouTube, Wikipedia, in forums or searching via Google. This form of learning started about
10 years ago. Even school pupils are now asked to prepare a homework using information found
on the Internet. As we can see, search is an important part of RBL.

Types of Search Behavior

A. Broder, 2002: A Taxonomy of Web Search

Informational Navigational Transactional
59.3% 28% 12.7%
3
Search on the Web has been classified by Information Management scientists. For example,
Broder in 2002 classifies search as Transactional search, where a user goes directly to a website
like Amazon and buys a book. Navigational like when a user goes directly to a known website like
Spiegel Online to read the news. Informational search however is more a browsing or a search
for information like googling to find out the meaning of folksonomy. The most often performed
search is informational search and this fits to the RBL form of searching.

Social Search Model

B.M.Evans & E.H.Chi, 2009: An elaborated model on social search
Gather
Social Interactions
Before Search Requirements

Formulate
Social Interactions Representation

During Search Foraging
Social Interactions

Social Interactions Sensemaking

After Search

Organize Distribute Do Nothing

Social Interactions
4
The social search model from Evans and Chi describes the search process not as a solitary act
but rather as a social process. In the Before Search stage, the reasons for the search and what
to be searched for are determined. For example, a school pupil gets the assignment to present a
talk on a topic in the news. The pupil probably goes on SchülerVZ and asks others in her class
what they will be presenting and asks where they are searching for information. During the
search, the user browses several web pages going from one page on to the other and trying to
find information which is relevant to the topic...this is called foraging and sensemaking. In the after
search phase, the user saves the found pages and can send these by email to others or prints
these out or hopes to use these later.

Gather
Before Search Create Activity Tree Requirements Text Messages
Chat
Form Groups
Formulate
Representation

During Search Add Resources to Activity
Foraging
Recommendations

Tagging
Sensemaking

After Search Browse Knowledge
Network
Text Messages
Chat

Activity
Experiences Organize Distribute Do Nothing

Recommendations Tagging
5
CROKODIL is a BMBF project with the aim to provide support for RBL in a learning community. I
have mapped the activities in CROKODIL to the social search model to classify the features
offered. In the Before Search stage, the learners can create an activity, like the assignment to
prepare a presentation, groups are formed having this common assignment or having similar
tasks. The interaction here is supported on the platform with a chat and text messages. During
the search, resources found on the Web are attached to the activities created before the search,
recommendations are also made by the system informing about similar resources already stored
by others on the platform. The learner can also give tags to these resources to use these to
classify the resource in order to find the resource later on. After the search, the learner can
browse the knowledge network and see what others have stored, find what other resources have
the same tags...add new tags to the resources, recommendations are made showing similar
resources from other activities or users. Finally, the learner writes about her experiences when
working on the activity as a form of reflection when the activity is completed. In this stage, the
learner can again contact other learners via the chat or text message functionality on the platform.

6
CROKODIL has a platform, where learners can set up a profile, set up friends or groups, create
activities, view their resources and read their text messages. The chat function is in the upper
right hand corner and the tags used are shown down in the right hand lower corner. The platform
is used for the before and after stages of searching.

7
During search, CROKODIL offers a Firefox addon which is installed in the browser and shows the
activity tree. A section of text on the website is highlighted and pulled across to the addon, where
it is attached to the activity which is currently activated. When saving, tags can be attached to the
resources.

Semantic Tagging

New
s

Nu clear April
Energy 2011
Japan
8
In CROKODIL, several concepts are implemented such as the pedagogical concept with the
activities and the reflection of the experiences made. We also have implemented the concept of
semantic tagging. For example I am a learner in CROKODIL, I find a news article about nuclear
energy and tag this with News, I read this in April 2011, the disaster happened in Japan and it’s
about a nuclear energy disaster.

Personal Resource Network

my resource

Resource

my tag
has tag

Nuclear pril
TagA
Energy 1
New 201
Japan s

9
This forms a structure: me, a learner, having a resource, having my tags which tag my resource.
This forms a personal resource network or a personal knowledge network.

Personal Resource Network (Personomy)

my resource

Resource

my tag
has tag

Tag

10
Viewing this abstractly, this forms a graphical structure...objects form the nodes and the
relationships the edges between the nodes. This is known as a personomy.

Semantic Tagging?!

my resource

Resource

my tag
has tag

Nuclear pril
TagA
Energy 1
New 201
Japan s

11
So why do we call this semantic tagging? I have shown here the tags having different colors, this
shows a sort of classification...Nuclear energy describes the topic of the news article, it is a news
article as the green tag shows, the location of the nuclear energy disaster was in Japan and this
event happened around April 2011

CROKODIL Tag Types

Person Tag

has tag
has tag Event Tag

has tag
Tag Resource

has tag Topic Tag

has tag
has tag

Location Tag Type Tag

12
In CROKODIL, we have these 6 tag types, the generic tag having no type, a location tag, a type
tag, a topic tag, an event tag and a person tag for example Angela Merkel.

Community Resource Network

owns tag
has friend
owns resource
Resource
Wikipedia
Type Tag
owns resource

owns tag
has tag has tag

kodil
has tag
Resource
Cro
Topic Tag

Pr oject

13
So back to the graphical structure which gets built...when several learners are on the platform and
they store resources and tag them, resources could be connected by the same tag. This is called
a community resource network or Community knowledge network.

Community Resource Network (Folksonomy)

owns tag
has friend
owns resource
Resource
Type Tag
owns resource

owns tag
has tag has tag
has tag
Resource
Topic Tag

14
This builds such a graphical structure which combines individual personomies to build a
folksonomy.

CROKODIL Groups and Activities

belongs to has activity
Participate in
Group Activity
GKEL Activities

has sub-activity

Prepare GKEL
Sub-
PreActivity n
sentatio
belongs to

has resource

has activity

Resource
owns resource

15
In CROKODIL we additionally have groups and activities. For example the activity GKEL activities
belongs to the group GKEL and I just prepared a GKEL presentation.

CROKODIL Groups and Activities

Group Activity

has sub-activity

Sub-
Activity
belongs to

has resource

has activity

Resource
owns resource

16
Abstractly this can be viewed as a folksonomy with additional social relationships and activities to
give pedagogical structure to the knowledge network.
With these structures, information about other learners or connected activities can be inferred by
traversing the relationships between nodes. One challenge here is identifying what is important
especially when the network grows to a larger network over time. Another challenge is finding
connections between sub-graphs where no structural connection exists.

Recommender Systems

17
We all know recommender systems such as in Amazon. Recommendations help to identify what
is important - as well as what could be new and interesting

Recommender Systems

Collaborative-Filtering Structure-Based
Activity

has sub-activity

Sub-
Group Activity

my group
belongs to
my activity

owns tag
my friend
my resource

Resource
Type Tag
owns resource

my tag
has tag

Hybrid
has tag
has tag
Resource
Topic Tag

Knowledge-Based Content-Based

Furniture

Chair


There are different kinds of recommender systems. Most systems combine different approaches
in hybrid systems. Collaborative-filtering uses the ratings of users on the platform to determine
which resources or tags are important or most used or known. Structure based recommender
systems we just saw when the relationships between nodes are used to traverse the graph to
find related nodes. Content based methods compare the content of the web pages to
determine the similarity between them. Knowledge based methods use other sources of
information to determine relationships between objects. Such as Furniture an Chair may not
be connected but via Wikipedia, the category Furniture-Chair is determined and the
connection made.

What is recommended in CROKODIL?

Nuclear pril
A Tags
Energy 1
New 201 Resources
Japan s

Friends Participate in
GKEL Activities
Groups Activities
Prepare GKEL
Presentation

19

In CROKODIL, tags and resources, friends,groups and activities can be
recommended.

CROKODIL Activity Ratings

Participate in
Activity
GKEL Activities

has sub-activity

Prepare GKEL
Sub-
Resource has resource Presentation
Activity

has resource
my activity

Resource
my resource

20
I will be focusing on introducing collaborative filtering to CROKODIL by introducing rating of
resources in the context of a CROKODIL activity. For example, I am preparing a new presentation
and I see this picture has been rated with 5 stars and this presentation with 4 stars because
maybe the picture was used by others but the presentation not.

Ranking Recommendation Results

Activity

has sub-activity

Sub-
Group Activity

my group
belongs to
my activity

owns tag
my friend
my resource

Resource
Type Tag
owns resource

my tag

1.
has tag has tag
has tag
Resource
Topic Tag

Recommendations

Furniture

2.
Chair

21
In general all recommendation methods weight the resources and offer suggestions of how these
resources could be ranked. For example according to the ratings, the picture is ranked higher
than the presentation.

CROKODIL Features

Nuclear pril Groups
A
Energy 1
New 201 Tag Types
Japan s Social
Relations

Participate in
GKEL Activities

Activity Tree
Prepare GKEL
Presentation
Activity Ratings
22
In CROKODIL, it will be interesting when ranking to consider the tag types, for example giving the
tag type topic more weight than the tag type type or event or location, then one could consider the
social relationships to determine what to prioritize, for example common friends or groups. Also
interesting will be the information the hierarchical structure the activities present as well as the
ratings in the context of these resources.

Explaining Recommendations

ahh... now I get it!

23
My next idea is explaining recommendations. This helps the user to understand the
system better and helps to build trust.

Explaining Recommendations

Most of your friends love this picture!

This presentation was also used in your
“Introduction to CROKODIL” activity.

24
The learner reflects on these explanations and learns about relationships that were not obvious
before. For example, that most of my friends like this picture...now the picture might be more
interesting to me or reminding me that I have already made a presentation introducing
CROKODIL...maybe I could use this again.

User Feedback to Recommendations

Most of your friends love this picture!

This presentation was also used in your
“Introduction to CROKODIL” activity.

25
Explanations also motivate the learner to give feedback, for example I do not find the picture
relevant to my new presentation even though my friends find this picture good. I rather find the old
presentation as interesting.

Feedback Loop

1.
Rank

2. Explain

Feedback

26
The system ranks the resources and suggests the first two selections, the user is given
explanations for this and the feedback is received.

Feedback Loop

1.
Rank

2. Explain

Feedback

27
The feedback now influences the recommendation algorithms and I realize you prefer the
presentation, this tells the recommendation system to maybe recommend similar presentations
belonging to you rather than popular resources from your friends.

Research Questions

How to exploit social relationships
between learners to improve
personalized recommendations?

How to exploit
folksonomy structures
for recommendation
algorithms?

How to generate explanations for
collaborative and structural
recommendations?

28
I plan to investigate these three questions. How to use the social relationships in CROKODIL, the
friendships and groups. As well as the graphical structure especially considering the special
features in CROKODIL such as the activities, the ratings, the tag types. Then how best to
generate explanations using this structure and the ratings or tags.

Summary

Resource Based Social Search
Learning Model

Personal
CROKODIL Resource
Network

Recommendation
Systems
Collaboraive Community
Filtering Resource
Semantic Network
Ranking Tagging
Recommendation
Results Explanations
Activity Ratings

Tag Types

Feedback Loop

29
A mind map as
summary

Questions & Contact

30
Any
questions?

Support for Resource-Based Learning on the Internet

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