© 2021 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
The analysis of remote discussions is not yet at the same level as the facetoface ones. The present paper aspires twofold. On the one hand, it attempts to establish a suitable environment of interaction and collaboration among learners by using the speech acts via a semi structured synchronous communication tool. On the other, it aims to define behavioral profiles and interpersonal skills hybrid grid by matching the BALES’ IPA and PLETY’s analysis system. By applying the fuzzy logic, we formalize human reasoning and, thus, giving very appreciable flexibility to the reasoning that use it, which makes it possible to take into account imprecisions and uncertainties. In addition, the educational data mining techniques are used to optimize the mapping of behaviors to learner’s profile, with similaritybased clustering, using Eros and PCA measures. In order to show the validity of our system, we performed an experiment on realworld data. The results show, among others: (1) the usefulness of fuzzy logic to properly translate the profile text descriptions into a mathematical format, (2) an irregularity in the behavior of the learners, (3) the correlation between the profiles, (4) the superiority of Eros method to the PCA factor in precision.
BALES’ IPA, clustering, fuzzy logic, hybrid grid, multi variate time series, PLETY grid, principal component analysis, similarity measure
The learning systems are complex and, often unpredictable. They are designed by exploiting, combining and implementing multidisciplinary theories <<psychology, cognitive sciences, didactics, ergonomics, social sciences, IT … >>.
The COVID19 pandemic has delivered an unprecedented shock to education systems, disrupting the lives of nearly 1.6 billion pupils and students in more than 190 countries on all continents. Lockdown has heavily affected the lives 94% of the world's educated population, and up to 99% in low and lowermiddleincome countries [1]. Shifting attention to remote learning has therefore become inevitable, necessitating urgent and radical changes in an aspiration to protect populations and curb the spread of the pandemic. Nevertheless, several challenges can hinder the practicality of distant learning. For instance, the content of the interactions during a working session, the participation and motivation levels of students as well as the exchanges structuring.
The inevitable global introduction of distance education during the covid19 pandemic must encourage the establishment of inclusive education systems to overcome the shortcomings inherent in sole reliance on inclass education. More specifically, it ought to boost the potential of individuals and promote collective development in all areas of life, including teaching and learning. Algeria is no exception to this constatation.
In order to draw up a "Learning Profile in Algeria » a study encompassing 112 students and 24 teachers in the Department of Computer Science at the University Sétif (UFAS1, Algeria) was conducted [2]. This study should allow us, among other things, to situate ourselves in relation to existing learning models in order to propose an adequate material solution to the problems relating to the distant quality of learning such as the sociological isolation of the learner, the loss of motivation, etc. The survey results show that learners assimilate knowledge in different ways. Some prefer group work others assimilate better individually [2]. According to this study, a large majority of the learners have no difficulties collaborating (77%). Seven in ten learners (70%) prefer the group work to the individual one, and 67% assimilate better in a group. Among the scrutinized learners, 66% found that an interaction between learners is fruitful on the educational plan.
The partisans of group work find that this mode constitutes the basis of the collective intelligence. They argue that it allows them to accelerate the thinking and the understanding process, to learn to empathetically listen to others, to exchange information, to communicate effectively, to test and improve their capacities and ideas, to overcome their inadequacies and to share the knowledge and work methods. They find that interactions among learners is pedagogically fruitful. As a result, they openly agree to collaborate with other learners [2].
Through teamwork, collaborative learning is achieved more adequately. A team is perceived as a group of people interacting actively to carry out a common target. It necessitates the strategic distribution of tasks and the convergence of his members’ efforts [3].
Lewin [4] defined the group cohesion as the willingness of individuals to stick together, and believed that without cohesiveness a group could not exist: the group is a whole which is not reduced to the sum of its parts. It establishes, with its closet circles, a dynamic structure (a field), the main elements of which are the subgroups, the members, the communication channels, the barriers … In this case, the mutual ignorance of the participants is not desirable.
In an attempt to directly or indirectly help students as participants in technologically mediated activities, the teachers as observers of these activities and the tutor as assistant and academic counselor, the computerbased interactions analysis is deployed (see Figure 1). The main contributions of this work are: (1) Drawing up a profile of the learners using a hybrid analysis grid by combining Bale’s IPA (Interaction Process Analysis) and PLETY grid, (2) fuzzy analysis of the interactions (3) calculating the similarity between behavioral profiles evolving over time of pairs of learners using PCA factor and Eros method, (4) grouping together learners with similar behaviors using hard and soft clustering, (5) experimenting the system on realworld data.
Figure 1. Synoptic of the interaction analysis process
The remainder of the paper is organized as follows: initially, we present the related works in the area of the learners interactions analysis, in section2. In section 3, we present our hybrid analysis grid combining both Bales IPA and PLETY system. Section 4 details the proposed approach. The experimental of our system are discussed in Section 5. Section 6 concludes the paper.
In a socioconstructivist approach, interactions between learners play a dynamic role in individual learning [5]. Nonetheless, the analysis of remote discussions is not yet at the same level as the facetoface ones. Evidently, the retrieved data tends to be more imperfect than those obtained from direct interaction. In this line, several studies have focused on the implementation of automatic systems for analyzing traces of interactions.
The Bales IPA has been used in some research in the area of Computer Sciences. Birnholtz et al. [6], for example, presented an experimental study of twopeople groups (dyads) editing documents together. The focus of the study, was to analyze group maintenance, impression management and relationshipfocused behavior. Savolainen [7] studied the extent to which blogs are used as interactive forums in which people can ask and share information by considering four reactions of the IPA categorization: showing positive and negative reactions, and asking and answering questions.
There are some related works that rely on IPA classifications but these ones are made manually by human experts. Kim et al. [8], for example, manually classified video recordings segments into IPA categories. Löfstrand and Zakrisson [9] followed a similar approach with the aim of identifying competitive and noncompetitive behavior.
Approaches such as Academic Talk [10], Group Leader Tutor [11] and EPSILON [12] attempted to reduce the effort needed to encode group dynamics into IPA categories by introducing the concept of sentence openers. Sentence openers predefine a limited set of sentences that are allowed to start a communication act. Each sentence opener is directly matched to one IPA category.
Contrary to Bales, a limited number of researchers have relied on PLETY’s research on ethology. George [13] has proposed an environment called SPLASH that uses the PLETY grid to analyze mediated synchronous conversations.
The present review of the literature allows us to highlight the differences with our approach that will be presented in this article:
3.1 BALES IPA and PLETY grid
The learners interactions analysis is the process of automatic or semiautomatic analysis, based on data obtained by the participants' own activity, with the aim of understanding the activity mediated by technology. This understanding allows human or even artificial stakeholders to participate in the control of the activity, helping them with awareness, selfassessment, or selfregulation.
Many psychosociologists were interested in the study of the interactions in a workgroup.
This IPA supplies a tool which allows on one hand to make a quantitative and qualitative analysis of the interpersonal relations and the dynamics of the group and on the other hand to establish individual profiles, to analyze the quality of the socioemotional relations and the behavior directed to the work from the number and of kind of interactions. This tool is called: the grid of Bales. The principle of application of the IPA method in online groups is very close to that deployed for the face to face groups. It is a classification of behavioral acts (act by act), to analyze the data in order to obtain descriptive indications of the functioning of the group and extract factors influencing this process.
The grid of Bales is a set of 12 categories which allow to describe, according to their nature, positive aspects of the intervention, and conversely negative aspects [15].
From these observations, PLETY highlights four (04) typical profiles of behavior: the organizer, the verifier, the seeker and the independent [13].
According to this study, these four profiles find themselves in almost all the analyzed groups (16 in all). PLETY brings an element of answer, speaking about learners groups distributed on network, by underlining that he met, curiously, the same aspects of membership, cohesion and leadership there that in the ordinary groups [13].
3.2 Hybrid interaction analysis grid
The question that arises, is to know how to transpose the results of Bales and PLETY works into a conversation context, using IT (Information Technologies)?
The grid which we propose arises from the crossing of both Bales IPA and PLETY analysis system, by considering only the following situations:
Table 1 summarizes our hybrid grid arises after having rearrange both grids of Bales and PLETY.
The hybrid grid gives us a picture of the social behavior of a learner during a collaborative learning session.
Table 1. Hybrid grid

Interventions 
Driven reactions 
PV 

Acts+ 
Acts 
Acts+ 
Acts 

Organizer 
P 
E 
A, M 
 
Large 
Verifier 
A, M 
D, E 
 
S, C 
Large 
Seeker 
 
E 
M 

Small 
Independent 
 
S 

S, C 
Low 
Collaborator 
Orientation aspect 
Decision aspect 


Acts+ 
Acts 
Acts+ 
Acts 


P, M 
E, C 
A 
D, S 


P: To propose A: To approve D: To disapprove C: To decline E: To elucidate S: To stand mute M: To demonstrate Acts+: Positive acts Acts: Negative acts PV: Participation volume 
It underlines two categories of profiles:
 Behavioral profiles: Including 5 PLETY patterns, which are determined by:
These patterns are:
 Relationship profile: Inspired by Bales IPA, we have here the collaborative profile of the learner by means of the orientation and the decision aspects of the learner. These two aspects are described by positive and negative acts.
4.1 Behavior fuzzy analysis
The fuzzy logic is an extension of Boolean logic, introduced with the 1965 proposal of « fuzzy set theory » by Lotfi Zadeh. It gives very appreciable flexibility to the reasoning that use it, which makes it possible to take into account imprecisions and uncertainties [17].
One of the interests of fuzzy logic to formalize human reasoning, is that the rules are expressed and stated in natural language. This is appropriate with our desire to draw up a learner profile in the group according to the discussions made during a collaboration session, although it is always difficult to try to model human behavior.
The fuzzy system [18] is divided into Five steps: (1) the input, (2) the Fuzzification, (3) the Inference engine, (4) the Defuzzification (5) the Output.
For each of the behavioral profile pattern (Organizer, Verifier, Seeker and Independent) and for the relational profile (Collaboration) we will do a fuzzy analysis what will generate 5 fuzzy systems (as shown in Figure 2).
Figure 2. Synoptic view of the fuzzy systems
Step 1: Profiles coefficients
To analyze automatically the conversations between learners, we have to calculate some heuristic formulas using the indications given above and the messages exchanged between learners. In reality we’ll use the intentions of the exchanged messages and not their contents, by means of positive and negative speech acts [19].
The Orientation Index (Ind_ort_p (L)):
Ind_ort $(L)=\frac{\sum A c t_{} \text {pos_ort }(L)}{\sum \text { Act_neg_ } \operatorname{ort}(L)}$ (1)
where:
The Decision Index (Ind_dec_p (L)):
$\operatorname{Ind}_{} \operatorname{dec}(L)=\frac{\sum A c t_{} p o s_{} \operatorname{dec}(L)}{\sum A c t_{} n g_{} \operatorname{dec}(L)}$ (2)
where:
The Intervention Coefficient (Coef_int_p(L)): We see that the intervention coefficient of each profile is a ratio between the number of participations that characterize this profile and the total number of participations of the learner during a session.
Coef_int_p $p(L)=\frac{\sum A c t_{} \text {int } p(L)}{\sum \text { Act }_{} \operatorname{int}(L)}$ (3)
where:
The Reaction Coefficient (Coef_reac_p(L)): We see that the reaction coefficient of each learner is a ratio between the reactions of his peers to the interventions during a session.
Coef_reac $_{} p(L)=\frac{\sum \text { Act_reac }_{} p(L)}{\sum \text { Act_int } p(L)}$ (4)
where:
The Intervention Ratio (IR(L)): It is the learner's participation or interventions (Act_int(L)) rate compared to the group's average participation during a session (Avr_int_grp).
$\operatorname{IR}(L)=\frac{\sum A c t_{i n t}\quad(L)\quad}{\sum A v^{*}_{} \text {int }_{} g r p}$ (5)
Step 2: The fuzzification
The second step involves construction of membership
functions. It corresponds to the identification of the linguistic variables. Two profiles’ analysis are to be implemented:
Step 2 Stage 1: Behavioral PLETY analysis:
We have three inputs for each of the profiles "Organizer, Verifier, Seeker and Independent” with as variable, the interaction coefficient for input1, the reaction coefficient for input2 and the intervention or participation ratio for input 3.
In light of the profiles’ characteristics shown in the hybrid grid the following fuzzy sets (linguistic variables) are proposed:
The membership function of input1 as shown in Figure 3, for all the behavioral profiles, is a sigmoid function defined on the interval [0 .. 1] and given by the formula Eq. (6):
$f 1(x)=1 /\left(1+e^{\pm 14(x0.5)\quad}\right)$ (6)
The membership function f2 of input 2 (Eq. (7)), for the behavioral profiles «Organizer, Independent and Seeker», is a sigmoid function too (Figure 3), defined on the interval [0 . 1]:
$f 2(x)=1 /\left(1+e^{\pm 14(x0.5)\quad}\right)$ (7)
Figure 3. Linguistic variables of the organizer
The membership function f_{v}2 of input 2, for the Verifier profile is a gaussian function in the interval [0..0.5] and a sigmoid function in the interval [0.25..1] (Eq.(8)) as shown in Figure 4:
$f_{v} 2(x)=\left\{\begin{array}{l}1 / e^{(x0.25) / 0.02} \\ 1 /\left(1+e^{\pm 11(x0.625)\quad}\right)\end{array}\right.$ (8)
The membership function f3 of input3 (Figure 3 and Figure 4), for every behavioral profile is defined by Eq. (9) as follow:
$f 3(x)=\left\{\begin{array}{l}1 / e^{(x1) / 02} \\ 1 /\left(1+e^{10(x1.5)\quad}\right) \\ 1 /\left(1+e^{10(x0.5)\quad}\right)\end{array}\right.$ (9)
Figure 4. Linguistic variables of the verifier
For the Output, the following fuzzy sets are proposed:
The membership function (Figure 5), for all the behavioral profiles is defined as a gaussian function (Eq. (10)) on the interval [0 .. 12]:
$F(x)=1 / e^{(x\mu)^{2} / 0.8}$ (10)
where, μ is the expected value.
Step 2 Stage 2: Relational analysis
It concerns the Collaborator profile. The latter has three variables: orientation index for input1, decision index for input 2 and collaboration index for output.
The fuzzy sets proposed for this profile are:
The Input1 and input2 membership functions (Figure 6) are both sigmoid defined on the interval [0 . 2] (Eq. (11)):
$f_{c} 1(x)=f c 2(x)=1 /\left(1+e^{\pm 7(x1)}\right)$ (11)
Figure 5. Output Linguistic variables of the verifier
Figure 6. Linguistic variables (Orientation, Decision and Collaboration)
For the Output, the membership function as shown in Figure 6, is a gaussian function (Eq. (12)) defined in the universe of discourse X = [0.12]:
$F c(x)=1 / e^{(x\mu)^{2} / 0.8}$ (12)
Step 3: The inference engine
The third step involves developing a set of rules for the knowledge base.
Given below are the set of rules using IFTHEN logic.
The fuzzy knowledge base is made up of all the fuzzy rules according to each profile. Let’s take as an example the two profiles: the organizer and the collaborator.
Step 3 Stage 1: Definition of fuzzy rules
The organizer inference rules:
Step 3 Stage 2: Selection of fuzzy operators
The choice of the fuzzy operators allows to determine the inference engine, which is generated in our case by using the Min/Max Zadeh operators [12].
Let S be the set of ordered pairs (fuzzy variables, crisp variables).
S = {(Coef_int_o(Active), Xo1), (Coef_int_o(Passive), Xo2), (Coef_reac_o(Positive), X’o1), (Coef_reac_o(Negative), X’o2), (IR(Important), I1), (IR(Average), I2), (IR(Low), I3), (Coef_organization(Good_o), Yo1), Coef_organization (Satisfactory_o), Yo2), (Coef_organization(Medium_o), Yo3), (Coef_organization(Insufficient_o), Yo4), (Coef_organization (Weak_o), Yo5), (Ind_orient(Good), X1), (Ind_orient(Weak), X2), (Ind_dec(Good), X’1), (Ind_dec(Weak), X’2), (Ind_collab(Good_c), Y1), (Ind_collab(Average_c), Y2), (Ind_collab(Weak_c), Y3)}.
Step 3 Stage 3: Apply fuzzy rules
We apply the fuzzy rules, quoted above, with the MIN/MAX operators and obtain:
The organizer:
Yo1 = Min (Xo1, X’o1, I1)
Yo2 = Max (Min (Xo1, X’o1, I2), Min (Xo1, X’o2, I1),
Min (Xo2, X’o1, I1))
Yo3 = Max (Min (Xo1, X’o1, I3), Min (Xo2, X’o1, I2),
Min (Xo1, X’o2, I2))
Yo4 = Max (Min (Xo1, X’o2, I3), Min (Xo2, X’o2, I1),
Min (Xo2, X’o2, I2), Min (Xo2, X’o1, I3))
Yo5 = Min (Xo2, X’o2, I3)
The collaborator:
Y1 = Min (X2, X’2)
Y2 = Max (Min (X1, X’1), Min (X2, X’2))
Y3 = Min (X1, X’1)
Step 4: The defuzzification
The Mean of Maxima (MOM) method is applied here [20]. In this method, the defuzzied value is taken as the element with the highest membership values. When there are more than one element having maximum membership values, the mean value of the maxima is taken.
Let p a behavioral profile, p$\in$ {o, v, s, i}.
Yp = Max (Yp1, Yp2, Yp3, Yp4, Yp5).
Y = Max(Y1, Y2, Y3).
We calculate the inputs (or abscissas) named Ap, A corresponding to the outputs Y_{p}, y respectively:
$Y=e^{(A\mu)^{2} / 0.8}$ (13)
$Y_{p}=e^{\left(A_{p}\mu_{p}\right)^{2} / 0.8}$ (14)
where, μ $\in$ {2, 6, 10} and μp $\in$ {2, 4, 6, 8, 10}
Let A be a fuzzy set with membership function Y(x) defined over x $\in$ X, where X is the universe of discourse.
The defuzzied value is let say x^{∗} of a fuzzy set is defined by the formula Eq. (15):
$x^{*}=\left(\sum_{x_{i} \in M} x_{i}\right) /M$ (15)
where:
Step 5: The output decision
It corresponds to the definitive decision:
4.2 Learnertolearner behavior similarity
The analysis of interactions between learners is based on data obtained by the participants' own activity during collaborative sessions. The learner profiles calculated for each of these sessions allow us to obtain a multivariate time series (MTSLP = Multivariate Time Series for Learner Profiles). Each MTS [21] concerns a learner. It is a series of final indices xi (t); [i = 1, .., n; t= 1, … , m], calculated sequentially through discrete time (collaborative sessions) where i indexes the calculation made at each time point t, thereby n = 5, since the columns of the matrix represent the collaboration, animation, check, quest and independence indices, calculated above (Figure 7).
Figure 7. Synoptic of an MTSLP
An MTSLP:
Step 6: Learner to learner similarity
In this section, we aim to calculate the learnertolearner similarity which provides a way of quantifying the degree of agreement between two learners behaviors.
The multidimensional similarity measure aims to indicate the level of similarity between several databases or groups of data simultaneously.
To measure the similarity between two learners L_{A} and L_{B}, we consider their MTSLP A, B of respective dimension d_{A} × 5 and d_{B} × 5.
These matrices (MTSLPs) have the same number of columns (5), but not necessarily the same number of rows, because:
We will use two methods dealing with Multivariate Time Series MTS:
Both methods apply to principal components of matrices. Hence, we need to preprocess our data. Algorithm1 computes the principal component of two MTSLPs:
Because SVD is applied to covariance matrices, the eigenvectors and the principal components are used interchangeably [22].
Algorithm 1 Computing the principal component of a multivariate time series of a learner A profiles MTSLP_{A} 
Input: An n x 5 MTSLP_{A}, where n is the number of sessions and 5 is the number of profiles Output: The right eigenvector matrix V_{A} and the left eigenvector matrix U_{A} 1: MA ← Covariance (MTSLPA) 2: UA Σ VA ← SVD (MA) 
Step 6.1: Learner to learner similarity using PCA factor
PCAS computes the similarity between the first k principal components [23].
$\operatorname{PCAS}(A, B)=\sum_{i=1}^{k} \sum_{j=1}^{k} \cos ^{2} \theta_{i j}$ (16)
where: θ_{ij} is the angle between the i^{th} principal component of A and the j^{th} principal component of B.
The result of the calculations carried out during this step between each pair of learners, will make it possible to obtain the learnertolearner similarity matrix X as shown in Figure 8.
Step 6.2: Learner to learner similarity using EROS method
The intuition behind this method lies in its ability to process a different set of observations for each group of data with the same number n of variables (5 profiles).
Figure 8. Learner to Learner PCA similarity matrix X
Figure 9. Learner to Learner Eros similarity matrix Y
This method is freed from the dimension problem by considering not the group of data but the eigenvalues and eigenvectors of the covariance matrix which, themselves, are of identical sizes [24].
Another advantage provided by this method concerns the dimension reduction of the data.
The Eros similarity between two MTSLP A and B; to obtain the learnertolearner similarity matrix Y as shown in Figure 9; is given by:
$\operatorname{Eros}(A, B, w)=\sum_{i=1}^{n} w_{i}\left\cos \theta_{i}\right$ (17)
where:
$\sum_{i=1}^{n} w_{i}=1$ and $w_{i} \geq 0$ (18)
w vector is obtained by:
$w_{i} \leftarrow w_{i} / \sum_{j=1}^{n} w_{j}$ (19)
The range of Eros is between 0 and 1, with 1 being the most similar.
4.3 Learners’ profiles clustering
We’ll use two clustering approaches: soft clustering and hard clustering, and we’ll focus on three main methods:
Step 7.1: Hierarchical Ascendant Classification (HAC)
It is a technique whereby an indexed hierarchy is constructed among the elements of the data set [25]. Elements are successively ''merged,'' that is, subsumed into entities or clusters comprising two or more elements, based on their distance in factor space, and on application of a merging rule. As distance, the generalized Euclidean distance in factor space is normally used (Eq. (20)). In accordance with the merging rule, each merger yields an index of similarity, which can be used to construct a tree that depicts the hierarchical relationships. Such a tree is called a dendrogram [26].
$d\left(x_{i}, y_{i}\right)=\sqrt{\sum_{i=1}^{n}\left(x_{i}y_{i}\right)^{2}}$ (20)
Step 7.2: Kmeans
Unlike HAC methods, which are difficult to implement on large datasets, Kmeans [27] is a clustering algorithm (algorithm 2) which is widely used in such situations. It is an iterative algorithm that proceeds as follows:
Algorithm 2 Computing the Kmeans clusters 
Input: An n x 5 Learners profiles matrix, where n is the number of learners and 5 is the number of profiles. Output: K learners clusters

The Euclidean distance, given by Eq. (20), is generally considered to determine the distance between each data object and the centers of the cluster.
The Euclidean distance (d) between two vectors X = {x_{1}, x_{2},…, x_{n}} and Y = {y_{1}, y_{2},…, y_{n}} is given by:
When all data objects are included in some clusters, the first step is complete and early grouping is performed. This iterative process continues until the criterion function is minimized.
Step 7.3: Fuzzy cmeans
The application of fuzzy logic for various scientific and technical goals has been commented on for decades [28]. This approach differs from the classical hard clustering where each object of the data set finds its own cluster. Thus, an object either belongs to a defined cluster or is out of it. The application of Fuzzy theory to the problem of finding similarity between objects of interest leads to the conclusion that a particular object can belong simultaneously to more than one cluster, but with different degrees of membership (DOMs) between 0 and 1 [29].
To calculate the weights and determine the clusters of a set of data points X = {x1, x2, x3 ..., xn} we perform the steps of Algorithm 3. Firstly, we randomly select a set of centroids V = {v1, v2, v3 ..., vc}, then, for each data point and for each cluster, we calculate the fuzzy membership of a data i to the cluster j using Equ. 21. Next, we calculate the new fuzzy centers v_{j} using Equ. 22. We repeat the calculation of the fuzzy memberships and the fuzzy new centers until the change in the center of the clusters between two iteration steps k and k+1 is less than the termination criterion β (Z^{(k+1)} – Z^{(k)} < β) or I iterations value is achieved.
$z_{i j}=\frac{1}{\sum_{k=1}^{c}\left(\frac{\left\x_{i}v_{j}\right\}{\left\x_{i}v_{k}\right\}\right)^{2 /(m1)}}$ (21)
$v_{j}=\frac{\sum_{i=1}^{n} z_{i j}^{m} \cdot x_{i}}{\sum_{i=1}^{n} z_{i j}^{m}}$ (22)
where:
Algorithm 3 Computing the Cmeans clusters 
Input: An n x 5 Learners profiles matrix, where n is the number of learners and 5 is the number of profiles Output: C learners clusters with fuzzy membership

As we were faced with a lack of adequate datasets to evaluate and test our system, we collected our own data from 28 second year undergraduate students, from the computer science department of the science faculty, at Ferhat Abbas University of Setif, over a period of two months. These students were divided into groups of 4 learners, meeting remotely for collaborative work: this is a miniproject on RISC processors. The interactions of the learners in terms of the number of language acts were collected. After the data preprocessing, the experiment process is as following: we will calculate the different coefficients of the profiles using fuzzy logic, then we will proceed to the calculation of the similarity between pairs of learners using the two methods EROS and PCA. The next step is to group learners together using the three clustering methods: HAC, FCM, and Kmeans. After each step we will discuss the results obtained.
5.1 Collaborative session structuring
We recommend a centralized structure within the group. The coordinator "team leader" is the only member having the right to make a decision after consulting the other members.
We use the participation model to structure our collaborative learning. During a working session the team leader exposes the problem then invites every present member to give its opinion. The team leader intervenes once the round table discussion is ended. He takes notes and presents the clear conclusions: if all the members approve the proposal, the problem is considered as solved, if instead the members disapprove of the proposal then a new proposal to be discussed is made.
The members can ask to the initiator of the solution to clear up it better. The initiator can decline the request of clarification as he can explain or demonstrate his solution what returns us to a new evaluation phase.
Time constraints make that in case of not consensus, the team leader adopts a proposal as a solution.
The finitestate machine of Figure 10 allows to illustrate the various states of the collaborative session where every interaction imposes the aforesaid constraints to answer a previous interaction or activate other interactions.
Figure 10. Illustration by a finite state machine of the collaborative session states
5.2 Results and discussion
Figure 11. Evolution of four Learners’ profiles
Emotions, the change in temperament and cognitive and sociological evolutions make analyzing human behavior a fairly challenging task. The study of educational systems, in particular the learners’ behavior, is complex hindering the understanding the results. The deployment of data mining reveals more or less precise models. The study carried out enabled us to identify the results postulated infra:
1. As shown in Figure 11, we notice that:
2. Figure 12 shows some correlations between profiles:
These correlations are due to the interlacing and interconnection of the profiles characteristics.
3. Figure 13 shows that Eros and PCA give two learnertolearner similarity indices. Recall that, in general, there are three types of transformation that should be considered for similarity measures, i.e., shift, scale and time warping. The similarity measures for time series should then be invariant to those transformations. Eros measures the similarity between two MTS items by comparing how far the principal components are apart using the aggregated eigenvalues as weights taking into account the variance for each principal component.
Thus, Eros gives the best precision and elapsed computing time than PCA [24].
4. We can see, that the groups are not really independent and that some learners can belong to several groups at the same time but with different degrees, as shown in Table 2.
5. The experimental results show a slightly difference between the hard clustering methods and the cmeans one as shown in Figure 14. This result is expected and if the size of the dataset was larger, we would have found more differences. Because, one of the weaknesses of the chosen clustering methods is that they give varying results on different executions of an algorithm. A random choice of cluster models produces different results, which leads to inconsistency.
Figure 12. Profiles correlations
Figure 13. Learner to learner similarity
Table 2. ClusterLearner membership
Learner 
FCM: Cluster’s membership 
HAC clusters 
Kmeans clusters 

Cluster1 
Cluster2 
Cluster3 

1 
0,331 
0,330 
0,339 
3 
3 
2 
0,334 
0,335 
0,330 
3 
3 
3 
0,334 
0,330 
0,336 
3 
3 
4 
0,331 
0,335 
0,333 
1 
1 
5 
0,334 
0,329 
0,337 
3 
3 
6 
0,331 
0,339 
0,330 
1 
1 
7 
0,334 
0,331 
0,333 
3 
3 
8 
0,332 
0,332 
0,336 
3 
3 
9 
0,335 
0,332 
0,332 
2 
2 
10 
0,332 
0,336 
0,332 
1 
1 
11 
0,335 
0,335 
0,330 
2 
2 
12 
0,332 
0,339 
0,329 
1 
1 
13 
0,332 
0,336 
0,332 
1 
1 
14 
0,336 
0,330 
0,334 
3 
3 
15 
0,332 
0,329 
0,338 
3 
3 
16 
0,333 
0,338 
0,329 
1 
1 
17 
0,333 
0,335 
0,332 
1 
1 
18 
0,332 
0,331 
0,337 
3 
3 
19 
0,336 
0,327 
0,337 
3 
3 
20 
0,336 
0,333 
0,331 
2 
2 
21 
0,335 
0,333 
0,332 
2 
2 
22 
0,333 
0,328 
0,339 
3 
3 
23 
0,334 
0,331 
0,336 
3 
3 
24 
0,333 
0,330 
0,337 
3 
3 
25 
0,334 
0,332 
0,333 
2 
2 
26 
0,332 
0,336 
0,331 
1 
1 
27 
0,331 
0,334 
0,334 
1 
1 
28 
0,334 
0,335 
0,331 
2 
2 
The distances used in clustering in most of the times do not actually represent the spatial distances. In general, the only solution to the problem of finding global minimum is exhaustive choice of starting points.
In our case, both Kmeans and HAC clustering produce fairly higher accuracy but the Kmeans method requires less computation. FCM clustering produces close results to Kmeans clustering, yet it requires more computation time because of the fuzzy measures calculations involved in the algorithm.
Figure 14. Hard and soft clustering
The current study aimed to inspect collaborative learning. More specifically, it examined difficulties associated to collective work such as displaying a feeling of membership in the group, positive evolution of the learner and interaction among individuals. It proposed a tool which allows to provide a learner with a profile in a collaborative learning situation. The profiler uses heuristics indices given by the crossing of Bales grid and that of PLETY. The fuzzy logic supplied a mathematical formalism to implement these indices. The analysis should therefore allow the creation of groups of learners with similar behaviors using both Eros and PCA methods to calculate the similarity between two MTSLP.
The study would have been more comprehensive if larger data had been used. As a perspective, we would like to conduct the experiment on a larger population of learners and over a longer period while including several knowledge components. By means of other analysis grids and others computing ways, we aim improving the implemented tool. To deepen this topic, we propose the use of a deep learning model (CNN for example) to straighten and correct the classes predicted by the used methods, namely: FCM, Kmeans and HAC.
[1] United Nations. (2020). Policy brief: Education during COVID19 and beyond. https://www.un.org/development/desa/dspd/wpcontent/uploads/sites/22/2020/08/sg_policy_brief_covid19_and_education_august_2020.pdf
[2] Harbouche, K. (2013). Intelligent agentbased environment work for collaborative learning on the web. PHD thesis, Computer Science Department, Ferhat Abbas University.
[3] Khentout, C. (2006). Interfaces et assistance à l’apprenant en enseignement à distance. PHD thesis, Computer Science Department, Ferhat Abbas University.
[4] Lewin, K. (1992). La dynamique des groupes. Sciences humaines, 14: 1011.
[5] Doise, W., Mugny, G., James, A.S., Emler, N., Mackie, D. (2013). The social development of the intellect. https://doi.org/10.1016/01918869(87)901929
[6] Birnholtz, J., Steinhardt, S., Pavese, A. (2013). Write here, write now! An experimental study of group maintenance in collaborative writing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 961970. https://doi.org/10.1145/2470654.2466123
[7] Savolainen, R. (2011). Asking and sharing information in the blogosphere: The case of slimming blogs. Library & Information Science Research, 33(1): 7379. https://doi.org/10.1016/j.lisr.2010.04.004
[8] Kim, M.S., Kim, Y.S., Kim, T.H. (2007). Analysis of team interaction and team creativity of student design teams based on personal creativity modes. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 48043: 5567. https://doi.org/10.1115/DETC200735378
[9] Löfstrand, P., Zakrisson, I. (2014). Competitive versus noncompetitive goals in group decisionmaking. Small Group Research, 45(4): 451464. https://doi.org/10.1177/1046496414532954
[10] Mcalister, S., Ravenscroft, A., Scanlon, E. (2004). Combining interaction and context design to support collaborative argumentation using a tool for synchronous CMC. Journal of Computer Assisted Learning, 20(3): 194204. https://doi.org/10.1111/j.13652729.2004.00086.x
[11] Israel, J., Aiken, R. (2007). Supporting collaborative learning with an intelligent webbased system. International Journal of Artificial Intelligence in Education, 17(1): 340.
[12] Goodman, B.A., Linton, F.N., Gaimari, R.D., Hitzeman, J.M., Ross, H.J., Zarrella, G. (2005). Using dialogue features to predict trouble during collaborative learning. User Modeling and UserAdapted Interaction, 15(1): 85134. https://doi.org/10.1007/s112570045269x
[13] George, S. (2001). Apprentissage collectif à distance: splash: un environnement informatique support d'une pédagogie de projet (Doctoral dissertation, Le Mans).
[14] Fournier, M., Austin John, L. (2011). In: L'abécédaire des sciences humaines. Chapter 1. http://www.scienceshumaines.com/austinjohnl_fr_12618.html
[15] Fahy, P.J. (2006). Online and facetoface group interaction processes compared using Bales' interaction process analysis (IPA). European Journal of Open, Distance and ELearning.
[16] Plety, R. (1996). L’apprentissage coopérant, Ethologie et psychologie des communications. ARCI Presse Universitaire, Lyon, France.
[17] Godjevac, J. (1999). Idées nettes sur la logique floue. PPUR presses polytechniques et universitaires Romandes. Lausanne, Switzerland.
[18] Gacôgne, L. (2001). Logique floue et applications. In: Institut d’Informatique d’Entreprise. Evry, France.
[19] Searle, J.R. (1990). Consciousness, explanatory inversion, and cognitive science. Behavioral and Brain Sciences, 13(4): 585596. https://doi.org/10.1017/S0140525X00080304
[20] Nodelman, U., Allen, C., Perry, J. (1995). Stanford encyclopedia of philosophy. Bryant University. 20060723. Retrieved 20080930. https://plato.stanford.edu/entries/logicfuzzy
[21] Hoppner, F. (2002). Learning dependencies in multivariate time series. In Proc. of the ECAI’02 Workshop.
[22] Jackson, J.E. (2005). A User's Guide to Principal Components. John Wiley & Sons. https://doi.org/10.2307/3172717
[23] Abdi, H., Williams, L.J. (2010). Principal component analysis. Wiley interdisciplinary reviews: Computational Statistics, 2(4): 433459. https://doi.org/10.1002/wics.101
[24] Yang, K., Shahabi, C. (2004). A PCAbased similarity measure for multivariate time series. In Proceedings of the 2nd ACM International Workshop on Multimedia Databases, pp. 6574. https://doi.org/10.1145/1032604.1032616
[25] Sasirekha, K., Baby, P. (2013). Agglomerative hierarchical clustering algorithmA review. International Journal of Scientific and Research Publications, 3(3).
[26] Zhou, S., Xu, Z., Liu, F. (2016). Method for determining the optimal number of clusters based on agglomerative hierarchical clustering. IEEE Transactions on Neural Networks and Learning Systems, 28(12): 30073017. https://doi.org/10.1109/TNNLS.2016.2608001
[27] Fahim, A.M., Salem, A.M., Torkey, F.A., Ramadan, M. (2006). An efficient enhanced kmeans clustering algorithm. Journal of Zhejiang UniversityScience A, 7(10): 16261633. https://doi.org/10.1631/jzus.2006.A1626
[28] Taherpour, A., Cheshmeh Sefidi, A., Bemani, A., Hamule, T. (2018). Application of Fuzzy cmeans algorithm for the estimation of Asphaltene precipitation. Petroleum Science and Technology, 36(3): 239243. https://doi.org/10.1080/10916466.2017.1416632
[29] Zhang, Y., Wang, W., Zhang, X., Li, Y. (2008). A cluster validity index for fuzzy clustering. Information Sciences, 178(4): 12051218. https://doi.org/10.1016/j.ins.2007.10.004