=Paper=
{{Paper
|id=Vol-1664/w10
|storemode=property
|title=Supporting Learner-to-Learner Interactions Using Online Social Network Information
|pdfUrl=https://ceur-ws.org/Vol-1664/w10.pdf
|volume=Vol-1664
|authors=Pasquale De Meo,Fabrizio Messina,Domenico Rosaci,Giuseppe M. L. Sarné
|dblpUrl=https://dblp.org/rec/conf/woa/MeoMRS16
}}
==Supporting Learner-to-Learner Interactions Using Online Social Network Information==
https://ceur-ws.org/Vol-1664/w10.pdf
1
Supporting Learners-to-Lerners Interactions Basing
on Online Social Networks Information
Pasquale De Meo and Fabrizio Messina and Domenico Rosaci and Giuseppe M. L. Sarné
Abstract—E-Learning students can benefit from proper class experiences [15], [21]–[26] we designed a model to manage
formation process based on the student needs. In particular, formation and evolution of e-Learning classes by using user
Online Social Networks make available data concerning users’ information available on OSNs. These information are linearly
interactions, as skills and trust relationships, that are behind
the dynamics of thematic social network groups, and can be combined in a measure, named convenience, used to suggest
explouted to form e-Learning classes. To this aim, we propose a the best class (student) to join with or leave (to accept or
model based on such information, which are properly combined remove) to a user (to the class itself). First of all, the skills of
to support the dynamics of e-Learning classes on Online Social a student with respect to a set of topics of interest represent
Networks. The approach provide a way to give suggestions to the basic aspect we considered to give teaching-homogeneity
users about the best classes to join with and to class adminastrors
the best students to accept. The proposed approach has been to the class [27], in order to balance “supply” and “offer” of
tested by simulating an e-Learning scenario within a large social support requests (i.e. interactions). Trust represents the second
network by showing its capability to satisfy all the actors. component, which is computed by combining several specific
Index Terms—Social Networks; Software Agents; Thematic factors – which are related to specific e-Learning concerns –
Groups giving a complete trust model based on reliability and repu-
tation criteria and on some countermeasures for erroneous or
I. I NTRODUCTION malicious opinions. The model is designed to assists students
and classes by means of personal software agents delegated
E-Learning (EL) represents a good solution for courses, as to create, manage and update the profiles of their owners on
it provides time and location flexibility, low costs and informa- the basis of information found on the OSNs. The convenience
tion sharing [1]. In this context, among the factors affecting measure is exploited by a distributed procedure, named Class
learners progresses there are personal attitudes, initial skills Formation (CF), that allows learner/class software agents to
and the level of mutual trust, which influences the attitudes appropriately cooperate to form classes.
of peers to start interactions [2] and minimizes the cold start
The experimental trials have shown that running the CF
effect. Given that those information are widely available in
algorithm allows students and class administrators to improve
Online Social Networks (OSNs), EL activities can benefit from
the average value of the convenience within classes.
synergies with OSNs. Besides, many OSN platforms [3], [4]
The rest of the paper is organized as follows: Section II
support thematic groups that, for their relevance, have been
introduces the context and the Expertize, Trust and Advan-
largely investigated [5]–[9].
tage measures. The proposed architecture is described in
In addition, software agents can support EL class formation
Section III, while Section IV discusses the GF algorithm.
processes by suggesting to students (classes) about the best
Section V presents the experiments we carried out, Section VI
classes (students) to join with (to accept) [10]–[12]. Studies
examines related literature and, finally, in Section VII we draw
confirmed that, within social communities, users start to inter-
our conclusions.
act and share information with other peers also based on the
level of mutual trust existing among users [13]–[18]. Besides,
also in forming OSN groups existing trust relationships can
II. E -L EARNING INTERACTIONS AND MEASURES
give a significant contribution, in addition to a similarity
criterion [13], [19], [20]. Let be N the set of OSN members, (||N || = N ), C the set
It is obvious that, due to the huge amount of data of OSNs of classes (||C|| = C), with each class c ∈ C consisting of a
and the huge number of thematics groups, examining the number of learners and at least a teacher. We also suppose that
entire space of data to suggest suitable solutions for learners’ each user ui ∈ N is associated with a software agent [28] ai
needs is impracticable. Therefore, based on previous research able to obtain a view on the ui background and attitudes and
Pasquale De Meo is with the Dept. DICAM, University of Messina, Viale
to assist him/her in joining with or leaving classes. Similarly,
Andrea Doria, 6 - 01010 Messina, Italy, e-mail: p.demeo@dmi.unime.it each manager is assisted by a software agent, denoted as Ai ,
Fabrizio Messina is with the Dept. DMI, University of Catania, Viale in deciding whether a new member can be accepted in the
Andrea Doria, 6 - 01010 Catania, Italy, e-mail: messina@dmi.unict.it
Domenico Rosaci is with the Dept. DIIES, Unversity Mediterranea of
class.
Reggio Calabria, Loc. Feo di Vito - 89123 Reggio Calabria, Italy, e-mail: We also define a behavioral measure, which is related to the
domenico.rosaci@unirc.it interactions carried out by a learner (see Section II-A) and a
Giuseppe M. L. Sarné is with the Dept. DICEAM, Unversity Mediterranea
of Reggio Calabria, Loc. Feo di Vito - 89123 Reggio Calabria, Italy, e-mail: trust measure, which considers the level of mutual trust among
sarne@unirc.it OSN members (see Section II-B).
56
2
A. Behavioral Measures other OSN users to compute their respective reputations. Such
The principle behind the definition of the behavioral mea- feedbacks refer to the quality of these interactions (remember
sures is that, in order to form classes, a balance between re- that users’ skills are evaluated by the Behavioral measures).
quired and/or offered skills should be desirable, as each learner Let ηp,r and ρp,r be respectively the measures of reliability
is interested to improve his/her knowledge by joining with and reputation that the OSN user up (i.e. agent ap ) computes
classes where the other members have suitable capabilities for the OSN user ur (i.e. agent ar ). The trust measure τp,r is
and managers of classes are interested to include users holding obtained by combining the reliability (ηp,r ) and the reputation
skills and attitude to interact. (ρp,r ) weighted by means a coefficient βp,r ∈ [0, 1] ∈ R:
Classes. Let’s define a class c as a tuple hS, W, Vc , oi where:
(i) S = {s1 , s2 , . . . , sm } is the skill set required by the class 0.5 if Ip,r = 0
τp,r =
manager of c; (ii) W = {w1 , w2 , . . . , wm } is the weight set βp,r · ηp,r + (1 − βp,r ) · ρp,r if Ip,r > 0
used to evaluate the students’ skills; (iii) Vc is the minimum where Ip,r is the number of interactions occurred be-
overall skill grade, computed over the specific skill set S, tween the two actors. Note that for new learners the initial
required to join with c; (iv) o is the reference topic or subject or trust/reputation is set to 0.5 to contrast whitewashing strate-
goal of c. More formally, for an OSN user uk and a skill set S, gies [31]. For computing βp,r , we consider that its value
Pm
V (k, S) = wi · g(k, si ), where g(k, si ) ∈ [0, 1] ∈ R is the increases with the number of interactions occurred between
i=1 the two learners because their direct knowledge improves
knowledge grade of uk for the skill si , while wi ∈ [0, 1] ∈ R
P
m over time and decreases when the reliability in providing
is set by the class manager to weight gi with wi = 1. recommendations decreases (the reputation or some peers may
i=1
The User attitude H. The attitude of the user uk to require be affected by malicious behaviors); Therefore, the coefficient
and/or offer interactions for his/her skills is computed as: βp,r is computed as:
H(k) = α · H(k) + (1 − α) · H(k) βp,r = M ax(β1 , β2 )
where the new value of H ∈ [0, 1] ∈ R combines, weighted Ip,r
where β1 = min Imax
(t)
, 1 and β2 = 1−Ωp,r . The parameter
by a system parameter α ∈ [0, 1] ∈ R, the previous value and (t)
a contribution H for the new interactions computed as: Ωp,r is the average confidence at time t for the current set of
recommenders that provided at least a recommendation to ap
|h(k)
H(k) = 1 − h(k)req
−h(k)
of f |
if h(k)req + h(k)of f 6= 0 (t) PRp,r (t−1)
rec +h(k)of f about ar computed as Ωp,r = ||Rp,r 1
|| i=1 |σp,r −τq,r | and
or H(k) = 0.5 otherwise, and where h(k)req and h(k)of f , where Rp,r is the set of agents provided an opinion about ar . It
with respect to uk , respectively are the evaluation of the minimizes the effect of untrustworthy opinions by giving more
interactions for a number Nreq and Nof f of skills subset relevance to those mentors evaluated by ap as the most similar
Si ⊂ S requested and offered at the new step obtained by: to it. Ip,r is the number of interaction that is incremented at
PNreq each step and when it is greater than the threshold Imax then
1
h(k)req = Nreq i=1 g(k, Sreq,i ) the “knowledge” between two users is considered maximum.
PNof f
1
h(k)of f = Nof f i=1 g(k, Sof f,i ) As a result, the contribute of the reputation in computing trust
decreases as much as the number of the interactions occurred
Therefore, when h(k)req ≈ h(k)of f , then H ≈ 1, i.e. the between the two involved learners constantly increases.
user uk asks and provides interactions to the same extent. 1) Computation of Reliability: The reliability measure,
Vice versa, his/her attitude is mainly to offer (or require) ηp,r ∈ [0, 1] ∈ R, is computed by up (i.e. ap ) about ur
interactions, i.e. H ≈ 0. (i.e. ar ) as ηp,r = ϑp,r · σp,r + (1 − ϑp,r ) · ηp,r , where the
Class Behavior. The class behavior for the class cj , denoted parameter ϑp,r weights in a complementary way the feedback
as B(j) ∈ [0, 1] characterizes its tendency to offer or require parameter σp,r ∈ [0, 1] ∈ R computed on the last interaction
P||cj ||
interactions and it is defined as B(j) = ||c1j || k=1 H(k). occurred between up and ur at time-step t and the value of
ηp,r computed at time-step (t − 1).
B. Trust Measure The parameter ϑp,r considers the relevance assigned to the
The second measure is based on the concept of trust [29] interaction between up and ur , let it be Ψp,r . In principle,
and it is computed by combining two factors, namely reliabil- malicious behaviors aimed to gain good reputation with low
ity and reputation. The former measure derives by the direct value interactions (Ψ ≪ 0.5) but high reliability (σ ≫ 0.5)
knowledge between truster and trustee due to their interactions can start on interactions of high relevance (Ψ ∼ 1) (due to
occurred in the past, while reputation is an indirect knowledge a good reputation) but providing poor performance (σ ∼ 0).
derived by the past interactions occurred among the trustee Therefore, the closer the ratio Ψ/σ to 1, the higher the value
with other counterparts different from the current truster [30]. of ϑ; the farther the value Ψ/σ from 1, the lower the value of
An interaction between two generic OSN learners up and ur ϑ. A possible choice for ϑ is represented by the adoption of
2 2
consists of a process where up starts with one or more learning the Gaussian centered in 1, as ϑ = e−(Ψ/σ−1) /v . ϑ acts as
tasks with ur . Consequently, their software agents ap and ar a “filter” for those values of σ which, for the correspondent
observe the interactions of their owners to register the interac- values of Ψ, may reflect a malicious behavior, while large
tion features (type, topic, duration) and collect feedbacks about values of v will select only those values of σ for which σ/ϑ
57
3
is close to 0 by ensuring that almost the whole history of class and Aj the associated software agent, the following tasks
feedbacks σ is considered in computing η. are triggered by the interactions among software learner agents
2) Computation of Reputation: The reputation measure ak and the class agent Aj as: (i) Any message of ak containing
ρp,r ∈ [0, 1] ∈ R is computed by up (i.e., ap ) with respect updated Behavioral and/or Convenience measure will trigger
to ur (i.e. ar ) as a value ranging in [0, 1] ∈ R: agent Ak to update Behavioral and/or Convenience measures
||Rp,r || for the whole class; (ii) Whenever the Behavioral measure of
X the class cj has changed, Aj will send the updated measure
1
ρp,r = ||Rp,r || τq,r
q=1
to all the learner agents of the class cj ; (iii) Ak will assist
the class manager of cj to take decision about the requests
Through the usual meaning of these indexes, 0/1 means that coming from agents ak to join with or leave classes.
ur is totally unreliable/reliable.
IV. T HE DISTRIBUTED PROCEDURE FOR C LASS
C. Convenience Measure F ORMATION (CF)
Behavioral and trust measures are combined to measure the In our approach, each Learner Agent has: (i) to update
convenience, for a user, to join with the class cj . The asymmet- all the proposed measures whenever one or more interaction
ric nature of the trust measure implies also the asymmetry of occurred, (ii) to send the new values to its class agents and (iii)
the convenience. In particular, let φ be a parameter computed to assist its own user to take decision about joining with or
as φ = (1−|H(k)−B(j)|)
||cj || , where ||c|| is the number of users leaving classes by executing the CF algorithm (For this aim, it
(i.e. agents) affiliated with c. Then the convenience (γu,c ) for will receive behavioral and trust measures from its own class
the user u to join with the class c, and that (ηc,u ) of the class agents).
c to accept the affiliation request of a user u are computed as: Each class agent has (i) to wait for learner agents messages
X X in order to update the proposed measures of the entire class,
γk,j = φ τk,i ηj,k = φ τi,k
(ii) to send updated behavioral measure for allowing learner
ai ∈cj ai ∈cj
agents to update their own convenience measures and (iii) to
Both measures increase with the difference between the assist its own class manager to take decision about the requests
behaviors of ak and cj . As a consequence of the asymmetric coming from learner agents to join with or leave classes.
nature of trust, the procedure described in Section IV is dis-
tributed among the agents assisting learners and those assisting
A. The distributed CF procedure.
class managers. As it will be discussed in the experimental
Section, the aim of the distributed procedure is to let the Let T be the time between two consecutive steps of the CF
system to reach a balance in terms of convenience among all procedure executed by the generic learner agent, in order to
the considered actors of the proposed OSN EL scenario. [32] join with a set of classes of the same topic. We also suppose
that agents can query a distributed database named CR (Class
III. T HE MULTI - AGENT E -L EARNING ARCHITECTURE Repository) on which the list of the classes is stored.
The CF procedure performed by the learner agents (See
In the proposed approach, OSN users (i.e. learners) are sup- Fig. 1(a)). Let Xn be the set of the classes the agent an is
ported by intelligent software agents [33] capable to perform affiliated to, and NM AX the maximum number of classes an
all the activities aimed at organizing classes basing on the agent can analyze at time t, with NM AX ≥ |Xn |. Besides,
measures presented in Section II. All the agents execute a set suppose that an stores into a cache the class profile of each
of tasks which are briefly summarized below, and categorized class contacted in the past and the timestamp d of the last
as Learner Agent Behavior and Class Agent Behavior. run of the CF procedure for that class. Let the timestamp
The Learner Agent Behavior. The behavior of a learner ξn and χn ∈ [0, 1] be two thresholds fixed by the agent an .
agent consists of several tasks periodically executed to main- The ratio of the procedure for the learner agent is to improve
tain data useful to run the CF algorithm (see Section IV). Let the convenience in joining with a class. Therefore, firstly the
uk be the generic learner and ak his/her agent, the following values of convenience are recalculated if older than ξn (lines
tasks are triggered by the learner and executed by the agent 1-4). Then, candidate classes are sorted in a decreasing order
as: (i) Any interaction of learner uk with one or more peers based on their Convenience value (line 5). In the loop in lines
will trigger agent ak to update Behavioral measures; (ii) Any 7-16 the NM ax classes are selected. If the classes in the set
reliability change of a user uj that interacted with a peer, Lgood are not in the set Xn , then agent an could improve the
will trigger ak to update the Reliability measure; (iii) The convenience of the owner if the classes accept the user for
Convenience measure will be updated for any change in the joining with.
Reliability measure; (iv) Behavioral and Trust measures are The CF procedure performed by the class agent - Fig. 1(b).
periodically sent to the class agent once and if they have been Let Kc be the set of the agents affiliated to the class c, and
recalculated; (v) The generic software agent ak will assist user KM AX the maximum number of learners allowed to be within
uk to take decision about joining with or leaving classes. the class c 1 with ||Kc || ≤ KM AX . Suppose that the class
The Class Agent Behavior. The behavior of a Class Agent agent Ac stores into a cache the profile P of each user u
consists of several tasks executed periodically to maintain data
useful to run the CF algorithm (see Section IV). Let cj be a 1 For convenience it is assumed the same for all the classes and topics
58
4
Input: by cj ∈ C for all its students ui ∈ cj . To measure the
Xn , NM AX , ξn , χn ; T global convenience
P of all the classes of N , we computed the
Y = {c ∈ S C} a random class set : |Y | ≤ NM AX , Xn Y = {0},
mean M AC = cj ∈C ACj /||C|| and the standard deviation
Z = (Xn Y ) qP
1: for c ∈ Z : dc > ξn do
cj ∈C (ACj − M AC) /||C|| .
DAC = 2
2: Send a message to Ac to retrieve the profile Pc .
3: Compute γun ,c A first test involved three scenarios consisting of 50, 100,
4: end for and 200 e-Learning classes, as summarized in Table I. To
5: Let be Lgood = {ci ∈ Z : i ≤ j → γun ,ci ≥ χn }, with
|Lgood | = NM AX compute the convenience, we assumed that 20% of OSN
6: j → 0 members is unreliable. Behavioral coefficients hreq and hof f
7: for c ∈ Lgood ∧ c 6∈ Xn do and the values of trust (τ ), have been sampled from a normal
8: send a join request to Ac
9: if Ac accepts the request then distribution [34] around specific mean and standard deviation
10: j → j+1 (stdev), see Table I. In particular, τr is the mean of generated
11: end if trust values for reliable users, while τu is the mean for unre-
12: end for
13: for c ∈ {Xn − Lgood } ∧ j > 0 do liable users. Moreover, for this set of experiments, the ratio
max ·|C|
14: Sends a leave message to c r= K Nmax ·|U | was set to 1. Besides, the starting composition of
15: j → j−1 classes is random. Table II shows the results of the execution
16: end for
of the CF algorithm for the three scenarios reported in Table I,
that shows the initial value of MAC/DAC (epoch T0 = 0) and
Input: S
Kc , KM AX , ωc , πn , ar , Z = Kc {ar }; the final one (epoch Te = 20). Indeed, we have verified that
after 20 epochs of executions, the M AC has reached a very
1: if (V (r, Sc ) < Vc ∨ |Kc | ≥ KM AX ) then stable value. It can be observed that, the improvement, in terms
2: Send a reject message to ar of MAC, at the end of the experiments, is about the 8% for all
3: else max ·|C|
4: for a ∈ Kc do the configurations and, since the ratio K Nmax ·|U | is the same for
5: if du ≥ ωc then the three scenarios without relevant variations, the subsequent
6: ask to a its updated profile were driven by r.
7: end if
8: end for For the second set of experiments we assumed a variable
max ·|C|
9: for a ∈ Z do value of r = K Nmax ·|U | , as shown in Table III, ranging from
10: compute ηc,a
11: end for
0.1 to 0.9. A value r < 1 say us that users, in overall, can join
12: Let be Kgood = {a ∈ Z : γc,a ≥ πc } more places (Nmax · |U |), than the total allowed (Kmax · |C|).
13: for a ∈ Kc − Kgood do In particular, the best improvement, in terms of M AC, is for
14: send a leave message to a.
15: end for
r = 0.4 (+20%), r = 0.5 (+16%) and r = 0.6 (+20%).
16: if ar ∈ Kgood then It means that, from one hand, finding a class to improve the
17: the request of ar is accepted personal convenience γ is a bit more difficult for the user
18: end if
19: end if
when r < 1, therefore the CF algorithm helps to improve
the MAC with respect to the random composition of classes.
Fig. 1. CF algorithm. Top (a): Learner Agent. Bottom (b): Class Agent Nevertheless, the algorithm clearly needs a certain degree of
freedom to give some benefits. Therefore, when r is very
small, the improvements, in terms of M AC are comparable to
managed by his/her learner agent a ∈ Kc and the timestamp
those given for values of r close to 1. In overall, these results
du of its acquisition. The procedure run by Ac is triggered
point out that the CF algorithm gives, on average, a relevant
whenever a join request by a learner agent ar (in the interest of
ur ) is received by Ac (with the profile Pr ). Let the timestamp
ωc and πn ∈ [0, 1] be two thresholds fixed by the agent Ac .
TABLE I
If the class has reached this maximum, no more students will CF ALGORITHM . S IMULATION PARAMETERS
be accepted. By lines 4-8 the class agent asks the updated
profile of its students to update their convenienceS γc,a (lines
Sc. |C| |U | KM ax NM ax
9-11) so that a new sorted set Kgood ⊂ {Kc ar } is built 1 50 200
2 100 400 20 5
(line 12). Then, the class agent (i) will send a leave message 3 200 800
to all the learner agents a having a convenience γc,a or (ii) if
τr τu {hReq , hof f }
ar ∈ Kgood (line 16), the agent request is accepted. mean 0.8 0.3 0.5, 0.5
stdev 0.2 0.2 0.2, 0.2
V. E XPERIMENTS
In order to evaluate the described approach, we performed TABLE II
R ESULTS WITH r = 1.0
some experiments to investigate on the convergence of the
CF algorithm described in Section IV. As a measure of Sc 1 Sc 2 Sc 3
the internal convenience for a class cj , we introduced the MAC DAC MAC DAC MAC DAC
concept of Average Convenience (AC), computed as the T0 0.63 0.12 0.62 0.12 0.62 0.12
Te 0.67 0.10 0.66 0.10 0.67 0.12
average of all the measures of convenience ηj,i computed
59
5
TABLE III By means of these data, suitable metrics can be created to
MAC AND DAC weight the “edges” between users. The proposed algorithm to
r=0.1 r=0.2 r=0.3 r=0.4 r=0.5
form groups simply explores the whole OSN to find a minimal
T0
MAC
0.61
DAC
0.07
MAC
0.59
DAC
0.02
MAC
0.60
DAC
0.03
MAC
0.60
DAC
0.04
MAC
0.60
DAC
0.08
number of proper candidates to form a group able to optimize
Te 0.61
r=0.6
0.08 0.63
r=0.7
0.08 0.69
r=0.8
0.04 0.70
r=0.9
0.10 0.73 0.06
a group EL experience. Differently, we exploit the concept of
T0
MAC
0.60
DAC
0.07
MAC
0.63
DAC
0.09
MAC
0.63
DAC
0.09
MAC
0.62
DAC
0.11
trust by combining reliability and reputation. Finally, in [43]
Te 0.70 0.09 0.69 0.08 0.68 0.08 0.67 0.10 the student use of Facebook at the University of Cape Town
is analyzed by showing positive benefits to build EL micro-
communities on Facebook but certain existing challenges, as
improvement of the convenience for the classes. [35]
including ICT literacy and uneven access, remain opened.
In order to test the effectiveness of the trust model we have
verified, by simulations, that the class formation algorithm
will lead to high and stable values of average convenience. VII. C ONCLUSIONS AND FUTURE WORK
Simulations have shown that the CF algorithm will lead Class formation in e-Learning is a critical task for the
significant benefits in terms of average quality of interactions. quality of such activities. In this work we focused on a
distributed algorithm supported by a trust model and some
VI. R ELATED W ORK behavioral measures based on information coming from the
OSN (i.e. users trust relationships, interaction quality, histor-
Group/class formation is an important task to promote EL ical attitude to interact with peers) to improve the metrics
activities and obtain effective results [36]. In particular, form- for dynamic class composition in OSNs. This flexibility is
ing random groups/classes may cause absence of participation aimed at improving the quality of learning experiences and it
and motivation [37]. A recent survey on group/class forma- is obtained by combining information about trust and previous
tion [38] analyzes about 250 works. The authors discovered interactions in a unique measure named “convenience”. In this
that the 20% of studies on group formation in collaborative work we have shown a first set of experimental results obtained
EL and a 20% of them adopt probabilistic models, while by simulating an artificial scenario with a variable number
the remaining studies rely on various AI techniques. Among of users and groups. The results have shown that the class
them, an interesting work deals with strategies for group formation algorithm will lead to high and stable values of
formation based on individual behaviors [39] obtained by average convenience.
monitoring communication data. The results show that the As future work, we will perform a further experimental
students participation in small groups is correlated with their campaign in order to verify that the convergence to high values
behavior in the class. Therefore, authors suggest to use these of convenience leads to significant benefits in terms of average
information to allocate heterogeneously initial classes into quality of interactions. Moreover, a further set of experiments
small groups. It partially differ from our approach that is is needed to verify the effectiveness of the trust model to
aimed at grouping individuals with similar behaviors, in terms limit malicious behaviors, in order to give trust values which
of “positive” and “negative” interactions. Besides, a relevant reflect the actual behavior, in terms of overall reliability, of
component in our proposal are the trust relationships from the students.
OSN data that in [39] is neglected.
A recent survey [40] dealt with the recommender systems R EFERENCES
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