=Paper=
{{Paper
|id=None
|storemode=property
|title=Socio-affective
Module for Recommender of Competency Learning Objects MSA-RECOACOMP: a
Study in Development
|pdfUrl=https://ceur-ws.org/Vol-1210/DC_02.pdf
|volume=Vol-1210
|dblpUrl=https://dblp.org/rec/conf/ht/PontesBB14
}}
==Socio-affective
Module for Recommender of Competency Learning Objects MSA-RECOACOMP: a
Study in Development==
https://ceur-ws.org/Vol-1210/DC_02.pdf
SOCIO-AFFECTIVE MODULE FOR RECOMMENDER OF
COMPETENCY LEARNING OBJECTS MSA-RECOACOMP:
a study in development
Walber Lins Pontes Patricia Alejandra Behar Magda Bercht
Federal University Rio Grande do Federal University Rio Grande do Federal University Rio Grande do
Sul - UFRGS Sul - UFRGS Sul - UFRGS
Caixa Postal 5071 - 90.041-970 Caixa Postal 5071 - 90.041-970 Caixa Postal 5071 - 90.041-970
Porto Alegre - RS - Brazil Porto Alegre - RS - Brazil Porto Alegre - RS - Brazil
+55 98 3265-0103 +55 98 3265-0103 +55 51 3308-6817
walberpontes@gmail.com pbehar@terra.com bercht@inf.ufrgs.br
ABSTRACT With the technological advance, new conceptions of teaching and
This article describes the required parameters for the learning emerged as the embodiment of the OAs in face-to-face
development of the socio-Affective Module (MSA) of education or distance learning.
Recommender of learning objects by competencies Such features provide easy access to the themes under
(RECoaComp)-MSA-RECoaComp. This is intended to recognize consideration, enabling the subject engaging independently and
the socio -affective aspects in recommending Learning Objects autonomously.
(OAs) skills. The module is being implemented by a
multidisciplinary team and is on the prototyping phase. In the Given this, one of the challenges of the educator is the selection
first stage were scaled the elements that will support the socio- and organization of these materials in order to contemplate the
affective recognition process. Such data will be extracted by profile of its students and their needs.
MSA-RECoaComp an exisiting environment of distance education The availability of content which is not suited to the needs and
and is used at the institution, ROODA more specifically one of its socio-affective characteristics of students cause an overload of
resources, the Affective Map [14], and the Recommender of information to the user.
competency Learning objects (RECoaComp). Thus, this work
allows you to understand the functionality of the MSA- As suggested solution of this problem is the recommendation
RECOACOMP noting the feasibility of the recommendation systems, according to [5] are intended to assist the user in the
regarding the OAs filtering skills considering the socio-affective search and selection of content focused on profile, working
aspects. literally as information filters.
Categories and Subject Descriptors Thus, the user receives as a result of searches only the closest and
relevant materials, as long as using and feeding system with new
K.3.1 [Computers and Education]: Computer Users in
information, either to the profile or research it carries out.
Education - Collaborative Learning.
The recommendation system that this article is about objectively
General Terms filters the OAs according to skills considering the socio-affective
Performance, Human Factors, Verification. recognition of users.
Note that this article covers aspects of characterization of the
Keywords module developed and does not discuss issues relating to the
Socio-affective recognition; Recommendation skills; Meaningful system itself, considering that it is still being modeled.
learning. The recommendation system has the potential to collaborate on
indication of OAs more suitable socio-affective aspects of the
1. INTRODUCTION user, being an important tool both for classroom education as the
This article describes the structure of the socio-affective distance in different educational contexts. Such a structure is
recognition on recommendation of learning objects based on based on moods and motivational factors of [21] and on the
skills. sociogram [17]. This article is organized into six sections and
section one introduction. Section two introduces the concept of
Such a feature is being developed by a multidisciplinary team in OAs, the section three the OAs recommendation. Section four -
stages, which is a socio-affective recognition. Five - features related systems that
preliminary result of work identifying the socio-affective aspects will support the MSA. Section six presents the socio-affective
to be considered for the MSA in RECoaComp filtering process. aspects to be implemented in the structure of the MSA and their
perspectives for recommendation.
2. LEARNING OBJECTS This approach will give emphasis on collaborative filtering,
The teacher uses the Learning Objects (OAs) to mediate content filtering and the hybrid, by supporting the process of
information in knowledge construction, with Wayne Hodgins the recommendation skills present in the RECoaComp.
first to use the term in 1994 [8] Collaborative filtering is based on systems that perform the
[23] conceptualizes OA as possible digital resources to be reused process of recommendation through the human assistance,
to support teaching. [22] broadens the understanding by resulting from the collaboration of groups interested in that
acknowledging the OA as any additional feature to the learning element. It has limitation on the recognition of the interest and
process, by unlinking it from the need to be a digital element. It understanding of the individual contributor on the object, as well
has five characteristics: 1) the information that must be next to the as on the recommendation process itself.
object (metadata); 2) reusability; 3) accessibility; 4) Content-based filtering is constituted as systems that apply the
interoperability; and 5) durability, presented as rules to recognition of elements that can have common interest implicit or
standardize the development of objectives so that they serve the explicit. The process may happens by distinct approaches, but
characteristic of reuse. with main purpose of recommendation.
Considering the need of reuse, granularity of OA and their Hybrid filtering recognizes the possibility of interacting more than
availability in stores it is necessary to recognize its features and one filtering technique allowing the simultaneous use of two or
functionalities. This way, the recommendation systems allow you more, in order to be provided the limitations of each mode [1] and
to find something inside the large set of OA (s) that (s) he can be [5].
re-used effectively.
4. SOCIO-AFFECTIVE CAPTURE ON
3. RECOMMENDATION OF LEARNING RECOMMENDATION OF COMPETENCY
OBJECTS
The recommendation process considering multiple alternatives for
LEARNING OBJECTS
On the perspective of recommendation of OAs by skills,
a solution. In the search for the most suitable choice generally
considering the socio-affective recognition,three elements are
performs a direct choice, or through previous recommendations
considered for the student's interaction with the OA: 1) socio-
[16].
affective space; 2) motivational factors and 3) State of mind.
He thus considers the need to recommend content, elements or
information matching the expectations of the individual [21].
4.1 Socio-Affective Space
[6] emphasizes the challenge of recommendation systems to The socio-affective space is being considered from the concepts of
perform the appropriate combination between expectations of [18], when he establishes the sociogram structure based on social
users and the elements to be recommended. interactions. The Sociogram, is a graphical representation of
sociometry, and allows the identification of group interactions, or
formation of social networks, the establishment of groups and the
3.1 Recommendation skills highlights or reference elements as well as the marginalized
The skills-based recommendation takes into account the need to
elements within the social structure.
assist the user in the search and selection of focused content to the
profile [6]. This process is not characterized as a filtering system,
but a guideline for interaction of the recommendation system. 4.2 Motivational Factors
The motivational factors are developed from Bercht model [4]
In this context it is relevant to understand the great challenge of with influence from [9] who considers the independence, the
the recommendation is to recognize the combination of elements effort and the student's confidence in execution of tasks and
that make possible an appropriate result to the expectations of the activities in a virtual system. The three elements to be combined
users. subsidize the inference of motivation, being a hint for the
The choice of filtering process gains importance as it identifies the recognition of the State of mind.
characteristics of the recommendation and the needs of This work was used entirely in [15] when considering evaluation
individuals involved in the process. The modeling of the system of motivational factors a persistent set of actions adopted by the
becomes critical to contemplate the most reliable results possible student in the Virtual learning environment (VLE).
to offer or need incorporated.
3.2 Filtering systems 4.3 State of mind
Within the context of recommendation seven types of filtration The mood is based on definitions of [22]: 1) be excited, implies a
systems are described: 1) collaborative filtering; 2) content-based joyful behavior demonstrate good mood, motivation, interest,
filtering; 3 demographic filtration); 4) knowledge-based filtering; satisfaction to meet the challenges of learning, and collaborates
5) utility-based filtering; 6) based filtering in other contexts; 7) and cooperates with partners; 2) be discouraged, implies
hybrid filtering. demonstrate a discontent, sad behaviour, unwilling, disinterest,
without motivation, dissatisfaction, frustration (or feel penalized)
The first two systems are observed in the texts of [11] and [21]; to continue learning, or even feel coerced, by believing that the
the third has highlighted in the text of [17]; the fourth and fifth will of others prevails; 3) be indifferent, implies demonstrate
are found in [5]; the sixth is approached [12] and [19]; the apathy, carelessness, negligence, neglect and lack of motivation
seventh is a result of the above found in [1] and [5]. for learning content.
5. SYSTEMS TO BE USED IN THE MSA- RECoaComp template Macro vision
RECOACOMP
Aspects of categorization for the socio-affective recommendation
of competency learning objects will be recognized and made
available by systems validated by the core of Digital Technology
applied to education (NUTED): 1) Map, affective ROODA
functionality, and the 2) RECoaComp [3]; and [7].
5.1 ROODA
The ROODA, institutionally recognized by UFRGS in 2003 as E-
LEARNING environment. It is the AVA in this work as a
platform chosen for the implementation of framework of
recognition and validation of socio-affective States of the students
during the OAs recommendation processes.
The ROODA aims the main educational paradigm shift from the
Figure 2. Cazella et ali, 2012.
interaction and cooperation of users in AVA. User-centered and In general the basic operation of RECoaComp happens in three
value-driven process of cooperation. For [2], the goal of this steps: 1) the teacher selects OAs from a repository, aiming at the
platform is to offer possibilities through resources on the web. construction of specific skills, recognizing that it can supply more
Users (teachers, counselors and students) can build a cooperative than one jurisdiction; 2) the student responds to a questionnaire
work through virtual and social interactions, turning your way of which traces a profile about the competencies relevant to the
thinking from the coexistence and exchange between students and subject (these previously defined by professor); 3) is triggered the
teachers. search through the information filtering by selecting the default
repository, using the registered metadata, the OA with the student
5.1.1 Affective Map profile, regarding competences [8].
The Affective Map [15] is a feature of ROODA which considers The idea of RECoaComp is to provide the student the content that
the moods of students and was developed in four phases as shown best meet the needs of building skills based filtering at the
in Figure 1, inspired by [13]: a) acquisition and identification; b) intersection of information relating to the student's profile and
interpretation; c) selection and d) inference of the moods of the skills that make it possible to develop OA.
student. The acquisition and identification determine the means
and methods by which the system will recognize characteristics
relating to affective States under review. 6. MSA-RECoaComp And Prospects
The MSA will be developed in the form of a RECoaComp
General scheme of recognition of moods module. Its implementation happens with the identification of the
State of mind and motivational factors obtained through the
affective and social relations maps presented on the sociogram. It
is of growing interest the development of technological tools
directed to educational systems that deal with the recognition of
social and affective phenomena.
It discusses, in this work, the introduction of affective aspects and
sociometry, based on mood, motivational factors and sociogram,
in virtual learning environments, to facilitate the provision of OAs
by competencies.
It is intended to apply the experiments during the semester of
2015/I in students of the Business Course and later, in students in
the Post- graduation Program in Informatics in education. The
application will check the student's perception about the
appropriateness of the OAs when recommended within the socio-
affective aspects.
Figure 1. Longhi et ali (2007) The study is relevant to determine whether the variables chosen
for the delineation of the categories of State of mind and social
5.2 RECoaComp and its perspectives environment should be considered (or reassessed) when
developing tool MSA-RECOACOMP..
The RECoaComp [3] allows the filtering of OAs considering the
competences to be established by the user. Using a collaborative 7. REFERENCES
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