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Building an Effective Groupware System Mohammed A. Razek, Claude Frasson, Marc Kaltenbach Computer Science and Operations Research Dept. University of Montreal C.P. 6128, Succ. Centre-villeMontral, Qubec Canada H3C 3J7 {abdelram, frasson, kaltenba}@iro.umontreal.ca Abstract Although, the growing number of Internet users who can play a prominent role in building collaborative groupware systems, the ability to find helpers (tutors or other learners) is still a challenging and important problem. Helpers could have a lot of useful information about courses to be taught, however, many learners fail to understand their presentations. We suggest a new filtering framework, called a pyramid collaborative filtering model, to trickle the number of helpers down to just one. The proposed pyramid has four levels. Moving from one level to another depends on three filtering techniques: domain model filtering; user model filtering; and credibility model filtering. Our experiments show that this method greatly improves filtering effectiveness.

1

Introduction

Diaa is a student at Al-Azhar University at Egypt. He registers in a course on data structure and studies a specific concept: ”queue”. Unfamiliar with it, he desperately wants someone to help him. Maybe he can find another student in his class. With that in mind, Diaa turns to a recommender system. Even though most recommender systems have their advantages, however, they are ineffective for this purpose. To solve this problem and others, we have developed a groupware system called Confidence Intelligent Tutoring System (CITS) [4]. It provides a collaborative intelligent distance learning environment for a community of online learners. For sound collaboration, the CITS searches its online learners and recommends a reliable helper to establish a learning session with Diaa. A major part of this paper tries to answer the following questions: Does the helper have information that Diaa needs? Will he or she present information as Diaa can understand? And can we guarantee that he or she will collaborate effectively with Diaa? We propose a new Filtering

framework called a Pyramid Collaborative Filtering Model (PCFM) to gradually diminish the number of helpers to just one. The proposed model has four levels. Moving from a level to another depends on three filtering techniques: domain model filtering; user model filtering; and credibility model filtering. Most of the recommendation systems [3, 9] rely on ratings from users, however, and do not consider their credibilities. As a result, when unreliable users recommend bad concepts; therefore, it pushes the system to recommend incorrect items. The greater the credibility and knowledge of helpers, the more successful the collaborative learning will be. We define ”credibility” as the dependability degree of the learners on the information presented by helpers during a learning session. It would be changed according to the helper’s level of knowledge, learning styles, and goals. One of this paper goals is to find an efficient way of calculating credibility. For that, we represent the interactions among a community of online learners as a social network problem. The rest of this paper is organized as follows. Section 2 gives a brief introduction to previous work. Section 3 discusses the role of the pyramid collaborative filtering model. Section 4 presents the results of experiments conducted to test our methods. And section 5 concludes the paper.

2

Related Work

A great deal of work has been done on building systems that use filtering to recommend an item or a person. Two approaches are particular important in this context: collaborative filtering and matchmaking systems. Two approaches to collaborative filtering systems, in turn, are prevalent: Collaborative Filtering (CF) and Content-Based (CB) methods. CF systems build user profiles of user ratings of available concepts. They use the similarities among user profiles to figure out which one is most similar to that of the requester. The k-Nearest Neighbor (KNN) method is a common method for collaborative filtering. However, KNN is considered the key technique

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to find k nearest neighbors for a given user to predict his interests; it suffers from two fundamental problems: sparsity and scalability [3]. Zeng et al. [9] uses the memorybased collaborative filtering to present solutions for these two problems. Using positive training examples, Pazzani [3] represented user profile as a vector of weighted words. For predicting, he applied CF to the user-ratings matrix. Match [2] is a matchmaking system that allows users to find agents for needed services. It can store various types of advertisement coming from various applications. Sycara et al. [8] proposed an agent capability description language, called Language for advertisement and Request for Knowledge Sharing (LARKS), which allows for advertising, requesting, and matching agent capabilities. LARKS produces three types of match: exact match (most accurate), plug-in match (less accurate but more useful), and relaxed match (least accurate). The matchmaking process of LARKS has a good trade-off between performance and quality.

3

The Role of Learners Filtering

Before describing the framework of learner classification, it is vital to shed light on the collaboration problem. In other words, on how to structure social activates in a communities of online learners. To represent the problem, suppose that a group of online learners Π participates in CITS. This group contains N learners Π = {Li }N i=1 . It is frequently hard to recognize who knows whom, who prefers to collaborate with whom, how learners collaborate, and how they come to know each other. This work suggests three questions. We claim that answering them can overcome the difficulties of collaboration. These questions are as follows: Does the helper have information that a learner needs? Will he or she present information so that the learner can understand it? And can we guarantee that this helper will collaborate effectively with the learner? The role of learners’ classification depends on answering these. We suggest a classification framework. As shown in Figure 1, it has four layers. Moving from one to another depends in turn, on three classification techniques: domain classification; user model classification; and credibility classification.

3.1

Dominant meaning Filtering

We consider that domain knowledge consists of some concepts. Each concept is represented by some dominant meanings that best fit it. Razek et al. [6] clarifies the role of constructing these dominant meanings. For an effective classification of the helper’s knowledge level, we associate each of them with their visited concepts. At any time any

Figure 1. Pyramid Collaborative Filtering Model

user studies a concept, he or she will be linked with this concept. Accordingly, the dominant meanings of that concept will be associated with his or her user profile. As a result, whenever the concept that a learner needs will be known, we could predict a list of users whom are linked to this concept. The prediction depends on similarity to the active learner. This similarity is calculated according to the common dominant meaning appearing in user profile. Using this similarity, we implement a domain classification algorithm to group the helpers. We use the dominant meaning space between a concept and a user profile to measure the closeness between them. Once dominant meaning space has been computed, a threshold must be set for the minimum acceptable value. We suggest the following measure to compute the distance between user profile and concept. Suppose that Ch is a concept that learner Lv is trying to understand. Suppose also that a set of the concept’s dominant meanings is {w1 , , wm }, and that the dominant meaning set of the Lv ’s user profile is {v1 , , vs }. At the moment, the problem is to evaluate users so that their profiles have the highest degree of similarity to concept Ch . Here is how we can evaluate the dominant meaning similarity S(Lv , Ch ) between user Lv and concept Ch . S(Lv , Ch ) =

s m  1
1 
Θ(wi , vj ) s i=1 m j=1

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(1)



where Θ(wi , vj ) =

1 if wi = vj 0 if wi
= vj

Some disagreements could emerge here. Why do we not compute the similarity between user profiles rather than between user profiles and concepts? To answer that question, we can say the underlying idea is to find a helper who has more knowledge than the learner and could therefore answer his or her questions.

3.2

there are links to the three chunks that define it. And each chunk consists of specific units such as text, images, audio, and video. After participating, we could have a visiting vector for each user. Visiting vector Vk = (vik )li=1 represents the atomic units in concept Ck , which have been visited by user v. Component vik is equal to zero if v does not visit it and equal to one if he or she does. Therefore, we can compute the similarity between users vand u as follows:

User Model Filtering

k Bu,v

In e-learning, where both helpers and learners are separated geographically, user modeling is one of the most important challenges. Through a learning session, the discussion is derived according to the helper’s own point of view, which depends on his or her learning styles and behaviors. As a result, the learner will find it hard to understand. To avoid this situation, we classify helpers in according to their learning styles and behaviors. We must know the learner’s style, his or her behavior, and the course contents to establish learning level, propose, or capability. The next section shows how we can do that. The purpose of this section is to group helpers according to user modeling. The algorithm we use can be summed up as follows: • Calculate the similarity between users’ learning styles LSu,v .

1 k ), U M D(u, v) = (LSu,v + Bu,v 2

l

vik uki l k 2 k 2 i=1 (vi ) i=1 (ui )

k βv,u =  l

3.2.2

i=1

Learning Style Similarity

Following [5], we consider six learning styles (LS): visual V ; auditory A; kinesthetic K; visual and kinesthetic V K, visual and auditory V A; and visual and auditory and kinesthetic V AK. the learning style similarity between two users u, v is computed as follows:  LSu,v =

1 1/2 2/3 2/3

if LSu if LSu if LSu if LSu

= LSv ∈ {V, A, K}&LSv ∈ {V K, V A, KA} ∈ {V K, V A, KA}&LSv ∈ {V KA} ∈ {V, A, K}&LSv ∈ {V KA}

(4)

Therefore, from formula (3), and (4), we can get the value of formula (2). In the next section, we illustrate the role of credibility value and show how to compute it.

3.3

Credibility Model Filtering

(2)

where U M D(u, v), LSu,v , and Bu,v represents the similarity degree between the user’s modeling, learning style, and behavior respectively. Next, we show how to calculate those degrees. 3.2.1

(3)

where D is the number of concepts, and

k . • Compute the similarity between user behaviors Bu,v

• Compute the User Modeling Degree (U M D) between two users u, v with respect to the concept Ck as follows:

D 1
k = β , D i=1 v,u

User Behaviors Similarity

To keep tack of users behaviors, we represent domain knowledge as a hierarchy of concepts. There are some dominant meanings to define and to join each concept. As we just mentioned in subsection 3.1, these dominant meanings consist of a set of meanings that best fit a concept or that reflect the particular view of a specific learner. In our approach [7], the course consists of concepts. Each concept consists of 5 fragments: background; definition; problems; examples; and exercises. For each fragment, moreover,

To illustrate why we use learner credibility to evaluate the significance of a segment, consider learner Lj , who recommends segment Γk . In fact, however, many segments are better than that one. We attribute this mistake to the learner’s credibility. If he or she has good performances and enough credibility, therefore, his or her recommendation will rank at or near the top. A learner might have much background knowledge, but his or her credibility as a tutor might become doubtful if his or her answer to another learner’s question is inadequate. Actually, credibility depends not on what the learner believes about himself or herself but rather on what other people believe about himself or herself. In a sense, the credibility of a response is based not on the sender but on the receiver or observer. If we observe communication among learners, we can represent the problem of how to calculate the credibility of each learner as a collaboration network problem. As

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shown in Figure 2, that is a complete directed graph G= (L, E), where L is the set of nodes of G and E the set of directed edges. Each edge has a non-negative length. Each node represents a learner. For any two learners, Li and Lj , there are three kinds of the direct link, Lij , Lji , and Lii , as shown in Figure 2. Link Lij gives the collaboration space from learner Li to learner Lj , and Lii represents the learner’s level of knowledge. In other words, it represents the amount of information that learner Li has. Therefore, we can use collaboration value Lij as the number of questions, answers, and other forms of help that learner Li has offered to learner Lj plus the number of questions that learner Li has received from learner Lj .

Figure 2. Collaboration Network Graph Using the graph in Figure 2 as an example, we can represent the credibility problem as a matrix : 

L11  L21 CM =  .. .

L12 L22 .. .

 ... ...   LN N

(5)

Using this matrix, we can calculate the credibility of learner Li as follows:

Ωv = Lvv +

N
f =1,f =v

N


Lvf −

Lf v

(6)

f =1,f =v

In the next section, we present our experiments and results.

4

Experiments and Results

In this section, we illustrate the data set, metric, and methodology for evaluating variants of our pyramid filtering algorithms.

Table 1. Collection used for experiment Collection Description Number of messages Number of users Number of topic (concepts) Average messages per week

4.1

502 95 5 15

Data Set

We use IFETS Discussion List1 to collect a set of messages in order to evaluate the performance of our algorithm. The IFETS discussion is provided by International Forum of Educational Technology & Society, which is a subgroup of IEEE Learning Technology Task Force2 . There are two kinds of discussion in the IFETS: formal and informal ones. Formal discussions are topic-based and occur for one to two weeks. There are moderators who conclude and summarize most of the individual estimations about the suggested topic; their summaries become visible in the forum’s electronic journal3 . Informal discussions happen daily. Any participant can submit new topics or questions to the forum. Each day, users discuss several topics related to educational technology. We accumulated a collection of messages from 27 Feb 2002 to 31 July 2003 and considered users who had submitted 2 or more messages. Table 1 presents the collection features and indicates the number of messages, the number of users, the number of topics discussed, and the average number of messages per week. We used 80% of the collection as a training set and 20% as a test set. All messages have already been classified manually into 5 concepts. To evaluate the accuracy of a prediction algorithm, researchers used two main approaches: statistical accuracy metric and decision support metric [1]. To measure statistical accuracy, we used the Mean Absolute Error(MAE) metric-defined as the average absolute difference between predicted and actual provide list. We used MAE to account prediction experiments, because they seem to be a simplest measure of overall error.

4.2

Experimental Results

In this section, we discuss the experimental results conducted on the first and third levels of the pyramid filtering model. As mentioned above, we ran this experiment on our training data and used a test set to compute MAE. In this experiment, we compute the number of users on the actual provided list (i.e. those who actually participated 1 http://ifets.ieee.org/discussions/discuss.html 2 http://lttf.ieee.org/ 3 http://ifets.ieee.org/periodical/

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on the concept discussion); the entire predicted list, using dominant meaning approach (including correct and incorrect users); the correct predicted list, using dominant meaning approach (including correct users only); and so on The average percentage of correct users predicted by the experiment is directly proportional to the number of the users who participated. To the best of our knowledge, none of the currently used filtering algorithms takes into consideration the credibility of users. They are based only on the rating capabilities of users. Based on user credibility, we conducted an experiment to illustrate its effectiveness on prediction task. The plot of the MAE of the first filtering and the credibility of the filtering algorithm are depicted in Figure 3. The figure shows that our credibility method yields more performance improvement according to MAE measures. The level of improvement changes at two measures. This changing depends on the number of users. According to the MAE, the error increases directly with respect to the number of users. On the other hand, precision is directly proportional to the number of users.

Figure 3. MAE for First level vs. Credibility Filtering

5

to the significant potential of our approach. These results show that filtering using dominant meaning and credibility significantly outperforms current filtering algorithms. Although, the MAE gives a clear explanation, it fails to explain why the average percentage of correct users predicted by both experiments is directly proportional to the number of users who participated. Future work is expected to suggest another measure to evaluate the accuracy of a system by finding the numerical scores of the corrected users predicted compared to the actual provided users.

6

Acknowledgments

We would like to thank Arnoldo Rodrguez for his help with the experiments.

References [1] J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In ACM/SIGIR ’99, pages 230–237, 1999. [2] M. Paolucci, Z. Niu, and k. Sycara. Matchmaking to sup-port intelligent agents for portfolio management. Proceedings of AAAI’2000, January 2000. [3] M. J. Pazzani. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6):393–408, 1999. [4] M. A. Razek, C. Frasson, and K. M. A confidence agent: Toward more effective intelligent distance learning environments. Proceedings of ICMLA’02,Las Vegas, USA, pages 187–193, 2002. [5] M. A. Razek, C. Frasson, and K. M. Using machine learning approach to support intelligent collaborative multi-agent system. TICE2002,LYON, France, November 2002. [6] M. A. Razek, C. Frasson, and K. M. Granularity degree: Towards adaptive course presentation on-line. ICCE2003,Hong Kong, December 2003. [7] M. A. Razek, C. Frasson, and K. M. Web course selfadaptation. IEEE/WIC on IAT 2003, Halifax, Canada, 2003. [8] K. Sycara, J. Lu, S. Klusch, and S. Wido. Matchmaking among heterogeneous agents on the internet. Proceedings of AAAI’1999, Stanford, USA, 1999. [9] C. Zeng, c. Xing, and C. Zhou. Similarity measure and instance selection for collaborative filtering. WWW2003, Budapest, HUNGARY, May 2003.

Conclusions and Future Works

This paper describes a novel model for processing online users according to their credibilities and profiles. Our approach satisfies the desire of many users to find helpers in specific domains. We suggested a pyramid collaborative filtering model. It is based on dominant meaning, user profile, and credibility. Based on the proposed model, we implement a guide agent, which can gather helpers and recommend reliable helpers. The experimental results testify

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