Transcript 下載/瀏覽

The Effect of Incorporating Good
Learners’ Ratings in e-Learning
Content-based Recommender System
Source：Educational Technology & Society
Author：Khairil Imran Ghsuth and Nor Aniza Abdullah
Presenter：Huang Kun-Yi
1
Outline
• Introduction
• Related Work
• Learning Materials Recommendation
Frameworks
• Experimentation and Results
• Conclusion
2
Introduction
• Web-based learning environments are
becoming very popular nowadays as a
means of delivering lectures or
simply as place to share notes.
• We propose a new e-learning
recommender system that is able to
recommend quality items to learners.
3
Related Work
• The most of the researchers use the
data mining approach and the
information retrieval technique as
the recommendation(Zaiane, 2002;
Liang et al., 2006; Kerkiri el al.).
• Zaiane (2002) proposed the use of web
mining techniques agents.
4
Related Work
• Khribi et al. (2008) compute online
automatic recommendations based on
learners’ recent navigation
histories.
• Liu et al. (2007) propose the system
is implemented by integrating the
techniques of LDAP and JAXB to reduce
the load of development of search
engine.
5
Related Work
• Tai et al. (2008) proposed e-learning
course recommendation based on
artificial neural network (ANN) and
data mining techniques.
6
Learning Materials Recommendation
Frameworks
7
Learning Materials Recommendation
Frameworks – Modeling phase
• The document weight wi,j is calculated
using TF-IDF.
f
w 
max f
i, j
i, j
z
D
* log  
 
 di 
z, j
fi,j：Frequency a term i occurs in document j.
maxzfz,j：All the z keywords that appear in document j.
D：Total number of documents that can be recommended to the learners.
di：The number of documents that contain term i.
8
Learning Materials Recommendation
Frameworks – Modeling phase
• Calculate the similarity value
between the two items.
cos( wc, ws ) 
w w
w w
c
s
c
s
C is user.
S is document.
9
Learning Materials Recommendation
Frameworks – Recommendation phase
• The good learners’ average rating is
obtained by calculating the average
rating of good learners’ ratings on
a particular item.

r
R 
N
N
i 1
i, j
i, j
j
ri,j is the rating of good learner i on item j.
Nj is the total number of good learners that rated item j.
10
Learning Materials Recommendation
Frameworks – Recommendation phase
• The item has not received any rating
from the good learns, then the item
will be recommended with good
learners’ prediction rating that is
calculated as follows：
N
P 
i
n 1
sim (d i , d n) * Rn
sim (d i , d n)
sim(di,dn) is the similarity between item i and item n.
Rn is the good learners’ average rating on item n.
11
Experimentation and Results
• The experiments were conducted on 95
university students (second year students
of Software Engineering).
– Group 1 consists of 21 students.
• Use the e-learning without a recommender system.
– Group 2 consists of 21 students.
• Use the e-learning with a content-based recommender system.
– Group 3 consists of 24 students.
• Use the e-learning with the proposed recommender system.
– Group 4 consists of 29 students.
• Use the e-learning with a collaborative filtering
recommender system as proposed by (Soonthornphisaj et al.,
2006).
12
Experimentation and Results
• Use Mean Absolute Error (MAE) to measure
the accuracy of the recommender systems.
 p r
MAE 
N
i 1
i
i
N
pi is the predicted rating for item i.
ri is the user-given rating for item i.
N is the total number of the pair ratings pi and ri.
13
Experimentation and Results
CF：collaborative filtering；
CBF：content-based filtering；
CBF-GL：content-based filtering with good learners rating strategies；
14
Experimentation and Results
• The mathematical formula for Precision,
Recall, and F-measure are given as follows：
tp
Pr ecision 
tp  fp
tp
Re call 
tp  fn
2 * Pr ecision * Re call
F  measure 
Pr ecision  Re call
15
Experimentation and Results
16
Experimentation and Results
17
Experimentation and Results
18
Conclusion
• The experimental results show that the
system’s accuracy of the proposed system
is increased by 83.28% when compared to
collaborative filtering technique and
48.58% when compared to content-based
filtering technique.
• The student’s performance has also
increased by at least 12.16%.
19
Thanks for your attention
20