Criteria for Collaborators Search

Yacine Lafifi

LAIG laboratory, Computer science department, Guelma University, BP 401, Guelma 24000,

Algeria laf_yac@yahoo.fr

Tahar Bensebaa

Computer science department, Annaba University, 32200, Ananba, Algeria t_bensebaa@yahoo.com

Abstract—The purpose of this paper is to extract the criteria using for looking for collaborators and examine their relevance. These search criteria are used by a search engine that is part of a distance computer supported collaborative learning. The goal of the developed engine is to facilitate the collaborators search for a learner who asks for collaboration. The learner must specify his preferences and his needs so that the system can take them into account. The needs are related to the behavioural (social) and cognitive levels of learners and their current states of knowledge (the learning unit being acquired). The learner’s preferences concern his judgment on the tasks of previous collaboration activities and his opinion about the behavioural state of the other learner (requested for collaboration) (he often accepts requests for collaboration; he rarely accepts collaboration, etc.). We tested this system at Guelma University for three months. We have recorded good results, which are discussed and analysed in the end of this paper. Furthermore, we give the results of a questionnaire that was submitted to learners (containing 31 questions) and we present some interfaces of the system with the problems encountered during this experiment.

Keywords-CSCL; Collaborative learning; Collaborators search; Search criterion; Search type; Cognitive level.

I. INTRODUCTION Several authors have demonstrated the positive effects of collaborative learning on the cognitive and behavioural levels of learners [1, 5, 6, 7, 8, 9, 10]. Among the first objectives of this learning strategy is the elimination of the isolation of the learner. This isolation may increase when the student does not have sufficient information on potential collaborators (identity, level of knowledge, behavioural level, etc.). To eliminate the effects of such isolation, we propose to offer to the learner the opportunity to choose his colleagues. He can express his preferences or letting this task to the system which can take into account his needs (after some time of using the system.) This leads us to ask a few research questions: How to represent the learners? How to express the needs and the

preferences of the learners to find good collaborators? What are the criteria for such search? To answer all these questions, first of all, we propose to identify the criteria reflecting the needs of learner. Then, they should be presented to learner in an easier way so that the latter control them and profit from them. These criteria must take into account in particular the characteristics of learners (personality, cognitive level, behavioural level, etc). For their use, we developed a set of tools for seeking collaborators satisfying these criteria. We think that offering several manners of seeking collaborators allows learners to diversify their choices and to maximize their chances to choose “good” learners which correspond to their needs.

The object of this paper is to present a set of criteria used by a system for collaborators searching which belongs to a collaborative learning environment. The latter offered several activities to learners such as: learning, assessment and collaboration.

II. CSCL ENVIRONMENTS WITH SEARCH COLLABORATORS

In collaborative learning, the relations between learners are crucial for the success of a collaboration session. The study of the social relations is a complex field. These relations are relative, i.e. a person who is good for X can be bad for Y or the opposite. Therefore, within the framework of the personal relations, it is preferable to leave the choice to the concerned people [2].

By consulting Internet, we did not find enough systems that used the functions of searching collaborators. The only system which can be quoted is “I-Help” [11] which uses in its search for Helper only one criterion relating to the state of knowledge of learner. Thus, our interest was directed towards the field of social sciences where we have found some criteria used by the sociologists within the framework of group dynamics (behaviour of a man, sociability, positivity,…) [3].

III. SEARCH ENGINE COLLABORATORS

The goal of the developed engine is to facilitate the search for collaborators asking for

collaboration. The learner must specify his preferences and his needs for being taken into account by the system [3].

A. Search criteria of collaborator The task of extracting the search criteria was

conducted in two stages. In the first one, we prepared a questionnaire which contained 15 questions (in collaboration with a teacher in cognitive psychology from Guelma University). The questions refer to the citation of the criteria and to the validation of certain criteria already given.

Several criteria were cited by learners (17 cited

criteria). We can mention some of them: • Sociability • Knowledge field • Appreciations on previous collaborations • Personality • Confidence • Intelligence • Age • Understanding

In Figure 1, we give the criteria with the number

of citations by the learners. As it is mentioned in figure 1, each gender

offers its own criteria. For example, females prefer confidence, non-aggression, understanding and sociability. Males prefer intelligence, knowledge area and age. So, we can say that the sex of the learner influence on the choice of search criteria.

0

20

40

60

80

100

120

Sociability

P ositivity

S ame kno wledg e are

P rev ious a ppreciat ion

Personn ality

Co nf id ence

Intelligence

No n-aggre ss io n

Un derstand ing AgeOthers

FemaleMale

Figure 1. List of criteria given by learners.

In the second stage, we had to resort to

interviews with specialists to extract the criteria that can be implemented. The criteria chosen for implementation are:

a) Cognitive profile:

It indicates the cognitive level of required learner (excellent, good, average, weak and very

weak). This profile is the results of the assessment process concerning the exercises presented to learner. This profile can be associated to a section of a matter, a matter or all the matters. b) Behavioural profile:

It indicates the behavioural state of learner (collaborator, insulated, etc.). It is given according to some criteria in particular his participation in each communication tool and his answers on the collaboration calls coming from the others learners.

c) Current state of knowledge:

Learner can seek a collaborator who has the same current state of knowledge as he or a specified state (according to the preferences of the learner). The state of knowledge determines the currently knowledge, the attempts of assessment, etc.

d) Positivity of learner:

It indicates the behaviour of learner with respect to the collaboration calls coming from the others learner (he is classified very positive if he answers the majority of the collaboration requests favourably).

e) Appreciation on collaborations already carried out:

In order to offer more advisabilities for the success of a forthcoming collaboration, the previous opinions concerning already completed collaborations are taken into account for a new search for collaborators. In our system, after any collaboration activity, the learner requesting for collaboration can appreciate this collaboration activity by choosing one value among the following ones: very pleasant, pleasant, neutral, unpleasant, very unpleasant.

B. Search types To facilitate the process of searching

collaborators, we propose several search types. They are divided into two categories. The first one concerns the specifications of the learner’s needs (specification of the values of the selected criteria). The second one concerns the non-specification of such values of criteria (taken into account by the system). a) Simple search:

Here, learner must specify only one criterion from the five criteria mentioned in the previous section. Other criteria can be used such as: name, sex, age, etc. Its simplicity lies in the fact of using only one criterion without any use of the Boolean operators.

b) Advanced search: All the criteria can be used. For each criterion a

set of values are associated. Boolean operators (And, Or and Except) are available in order to express more complex requests of search. This can give a list of collaborators more restricted or more widened (according to the combination of the operators).

c) Automatic search:

It is the search type which takes into account the learner’s needs. With this intention, the system establishes a classification according to the value of a parameter called: coefficient of collaboration. This last one is calculated according to the criteria previously stated with weightings. These weightings must be established by specialists in social sciences.

d) Search by theme (or by key words):

It is the traditional type of search. It consists in seeking all learners satisfying a criterion given by learner (such as: date of collaboration, place of collaborator, name, age, etc.). In this case, learner can introduce key words expressing his search independently of the search criteria (similar to the search carried out by the traditional search engines, except that here, it is the data base of learners which is used).

C. Scenario of Search for collaborators Learner can launch his request for searching

collaborators by specifying his needs (a set of criteria). After having chosen the type and the values of these criteria, a list is posted by the system containing learners (possible collaborators) satisfying these criteria with their various characteristics and their availabilities on line. Learner seeking collaboration must choose one collaborator among the learners of this list. The following figures show some use cases. They present the various search types.

Figure 2. Search types.

Figure 3. Advanced Search.

Figure 4. Search by theme.

D. Search results After specifying the search type and the criteria

values, the search engine gives as result a list of possible collaborators satisfied the criteria mentioned previously. For providing the learner with more information about the collaborators, we have used several methods for presenting the results. To each possible collaborator, the following information is offered:

1. availability on line, 2. personnel information, 3. previous collaboration activities done

between the two learners, 4. cognitive and behavioural profiles.

Figure 5. Search results (Automatic search).

Figure 6. Search results (Simple search).

IV. SCENARIO OF COLLABORATION After having received the list of the possible

collaborators, learner can choose one of them. When the chosen learner will connect to his own space, he will find a collaboration call. The solicited learner can accept or refuse this request (or let this request on standby). His answer is transmitted to learner requesting the collaboration. If the answer is favourable, the communication tool as well as other information relating to the synchronous and asynchronous modes of collaboration must be specified. For example, if the tool is “the chat” then the date and the hour must be specified.

In our system, we used the following

collaboration means [4]: • Chat, • Forums: we have developed three types of

forums: public, private (for each group) and per subject (concerning each subject to be taught),

• Electronic mailing, • Semi structured interface with sentence

opener [10].

V. EXPERIMENT

A. Results and Discussion An experimental study was realized at computer

science department at Guelma University (Algeria) with the students of the 2nd year licence where the subject to be taught was “Algorithmic”. Sixty learners could have access to the system using any computer connected to intranet of the University. The subject to be taught was composed of about ninety concepts. Batteries of exercises have been associated to each concept of the subject.

After three months of the system’s use, we have

obtained some results. Firstly, learners have appreciated the whole functionalities of our system concerning learning, assessment and collaboration. Secondly, the majority of learners found that the search engine of collaborators is sufficient.

To know the utility of our search system, we

have submitted a questionnaire to learners at the end of their use of the system. We give some results:

1. Concerning the suggested search types, learners preferred simple search (50%), 10% of them preferred advanced search, 30% of them preferred the type of automatic search and 10% of them

preferred the search by theme (by key word).

2. The set of the search criteria used was qualified as “sufficient” by the majority of learners (80%). Only, 10% of learners said that the criteria are not sufficient.

3. Lastly, an important remark is related to the contents of the sent messages. They are written in several languages (Arab, French, English, mixture Arab-French, etc.) and comprise many spelling mistakes. Teachers announced the problem of the incomprehension of messages sent by certain students.

We give in what follows some answers of

learners concerning some questions of the submitted questionnaire (which contains 31 questions).

Question 1: Do you think that this software is:

• Very useful • Useful • Not useful

0

5

10

15

20

25

30

35

Very useful Useful Not useful

Question 2: What are the reasons of your collaboration?

• Learning concepts • Resolving exercises • General Discussion • Other.

0

5

10

15

20

25

30

Learning concepts Resolving exercises General Discussion Other

Question 3: You prefer collaboration?

• Always • Some times • Rarely • Never

0

5

10

15

20

25

30

35

Always Some times Rarely Never

Question 4: What do you think about the search engine of collaborators?

• Very sufficient • Sufficient • Not very sufficient • Not sufficient

0

5

10

15

20

25

30

35

Very sufficient Sufficient Not very sufficient Not sufficient

Question 5: According to you, which search type do you find important to find good collaborator?

• Simple search • Advanced search • Automatic search • Search by theme

0

5

10

15

20

25

30

Simple research Advanced research Automatic research Research by theme

Question 6: In the advanced search, the criteria used are:

• Very sufficient • Sufficient • Not very sufficient • Not sufficient

0

5

10

15

20

25

30

35

40

45

Very sufficient Sufficient Not very sufficient Not sufficient

Question 7: After the research, how you find the result?

• Very effective • Effective • Not very effective • Not effective

0

5

10

15

20

25

30

Very effective Effective Not very effective Not effective

B. Faced problems According to the students, the most frequent

problems they meet are: - Some pages of the system seem to be full

of information (especially those concerning collaboration).

- The type of advanced search is incomprehensible and contains a lot of information.

- No information about requests for collaboration (sent by learners) which do not have a response (pending).

VI. CONCLUSION While several collaborative learning systems

were implemented supporting collaboration, few of them were interested by the choice of collaborators by learners requesting collaboration. We developed a list of criteria taking into account the cognitive and the social profiles of learners seeking collaborators, the positivity of learner during previous requests, and the nature of the activities of collaboration already carried out. Lastly, to offer more chances for the success of the collaboration process, the current state of knowledge of learner requesting collaboration was taken into account.

By using these criteria, we developed several search tools of collaborators. The multitude of these tools allows learners to choose those who are appropriate to them and who satisfy their needs.

To be able to test the system effectiveness, a step of experimentation is necessary. An experiment with students of the 2nd year licence of computer science department (Guelma University) shows some results which are qualified as very satisfactory.

Finally, we have studied the influence of factors

such as the sex and the culture on the criteria for selecting collaborators, which allows us working on the adaptation rules that can be used for the adaptation of the search engine system.

REFERENCES [1] S. Labidi, C.M. Lima, “Modeling Agents and their

Interaction within SHIECC: A Computer Supported Cooperative Learning framework”. Revue d'information scientifique et technique. Vol. 10, No1-2, 2000, pp 41-54.

[2] Y. Lafifi, T. Bensebaa, “Outils pour favoriser une collaboration effective dans SACA”. Manifestation des jeunes chercheurs en STIC, MajectStic 2006, Lorient, France, November 2006. Available at: http://web.univ-ubs.fr/lester/www-lester/Evenements/Majecstic/papers/ IHM /102_lafifi.pdf

[3] Y. Lafifi, “SACA : un système d’apprentissage collaboratif”. PhD thesis, Annaba university, June 2007.

[4] Y. Lafifi, T. Bensebaa, “Pedagogical Scenarios in SACA : a collaborative learning system”. International Review on Computers and Software, Vol 2, N 1, 2007. pp. 73-79.

[5] J. Lonchamp, “Supporting synchronous collaborative learning: A generic, multi-dimensional model”. International Journal of Computer supported collaborative learning, Vol 1, Issue 2, 2006.

[6] T.S. Roberts, “Computer Supported Collaborative Learning in Higher Education”. Idea Group Publishing, 2005.

[7] N. Santos, M. R. S. Borges, C. Systems. “Computer supported cooperative learning Environments: A framework for analysis”. Proceedings of EDMEDIA / ED-TELECOM 99. World Conference on Educational Multimedia & Hypermedia and Telecommunications, Seattle, Washington, USA, June 19-24, 1999.

[8] F.C. Serce, S. Yildrim, “A web-based synchronous collaborative review tool: a case study of an on-line graduate course”. Educational Technology & Society, 9(2), 2006. pp 166-177.

[9] R.O. Smith, “Working with difference in online collaborative groups”. Adult Education Quartely Vol 55, N°3, 2005.

[10] A. Soller, “Supporting social interaction in an intelligent collaborative learning system”. International journal of artificial intelligence in education, Vol 12, 2001.

[11] J. Vassileva, G. McCalla, J. Greer, “Multi-agent multi-user modelling in I-Help”. User modelling and user adapted interaction (2002) E. Andre and A. Paiva (eds.), Special issue on user modelling and intelligent agents, 2002.