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Personalized online systems have been developed
to make learning more effective. One of the ways to achieve
this personalization is to recommend the use of learning
materials according to learning styles. However,
information on learning materials and learning styles must
be formalized to make automatic processing by the
computer possible. The obje...
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Context 1
... represent learning styles, the structure proposed by [20] was adopted since it presented a more complete representation among the analyzed works. The following classes were used: user-model:LearningStyle, user-model:LearningStyleCategory and user-model:LearningStyleTheory. The subclasses used were: user-model:Active_Reflective, user-model:Inductive_Deductive, user-model:Sensing:Intuitive, user-model:Sequencial_Global and user-model:Visual-Verbal. These subclasses represent the dimensions of the Felder and Silverman model. The following properties were also used: user-model:basedOnTheory and user-model:hasCategory. The user-model:basedOnTheory property is used to represent to which learning style theory model a determined learning style category it belongs. To complement the learning styles representation, the orlm:LS_CE_AC and orlm:LS_AE_RO subclasses were added to represent the scales of the Kolb model. The addition of these subclasses was done since in [20] only subclasses for the Felder and Silverman model are proposed. Thus, as the user-model:LearningStyleTheory class allows the representation of other learning models and the Kolb Model, according to the revision described in Section 3, is also a model applied to learning styles, its representation scales were also included. V. E XPERIMENTS AND D ISCUSSION To evaluate the application of ORLM, an application scenario was created. The objective was to indicate, among the available materials, those that were more adequate to the learner, according to his or her learning style. The simulation was carried out in the Protégé , following four steps: (1) insert instances of people with specific learning styles, such as Andreia who has a Verbal learning style according to the Felder and Silverman Model, Andressa who has a Visual learning style according to the Felder and Silverman model and Joselaine who has the Visual learning style according to the Felder and Silverman model and an Assimilating learning style according to the Kolb model; (2) insert instances of types of materials and associate them with the learning styles available , such as Text type associated to the Verbal style, Image type associated with the Visual style and the Linear type associated with the Assimilating style; (3) insert instances of learning materials with their respective characteristics, as, for example, material001 with Linear and Text characteristics, material002 with Image and Linear characteristics and material 003 with Text characteristics; (4) carry out developed queries using the SPARQL [28] within the Protégé. The query presented in Fig. 2 is an example of the queries that can be developed using the proposed structure. The objective of this query was to find among the available materials which ones were being recommended according to the learning style of each individual. In Fig. 3, it is possible to visualize the results of the Fig. 2 query. The ontology considered all learning styles available, including the styles of different models. For each learning style, the correspondence with the characteristics of the learning material was verified before recommending it. The simulations showed that it is possible to use the ORLM to help material recommendation according to learning styles, and different objectives can be achieved according to the query formulation. Some objectives that can be reached through the structure presented are: find materials, considering only one determined learning model, find materials, considering only one category of a determined learning style model, or yet, find materials, considering a specific learning style. Through ontology it is also possible to find similarities between different learning model scales whenever the same learning material characteristic point out to styles from different models. The ontology proposed can also be used for other purposes which are not materials recommendation exactly. In this case, learning styles structure can be used and applied to other domains with other objectives. VI. C ONCLUSION The objective of this work was to present the ORLM ontology to recommend learning materials taking into consideration learning styles. The proposal was based on already established Standards such as FOAF and Dublin Core and fragments of ontology for the learning styles found in a systematic revision. A simulation was carried out and the results showed that it is possible to use the ORLM to recommend materials according to learning styles. The possibility of representing learning styles of different models offers flexibility in selecting which style is more adequate to the type of recommendation or customization being applied to a learning environment. The proposed ontology is being improved to include axioms that allow greater expressivity to classes, and the integration of the proposed ontology with a learning environment directed to software development teams. In this environment, Software Engineering ontology must also be used to classify learning materials by ...
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Citations
... These ontologies can be used in different e-learning systems, and therefore, increase interoperability of them [26]. Learning objects and learning style models have been modeled with ontologies in different studies [4,5,[15][16][17]22,[27][28][29][30]. Elearning systems that support both multi-agent and Semantic Web technologies include [15,16,22,31,32]. ...
... In addition to this, ontologies facilitate learning material description, sharing and search on the Web [61]. There are many studies that use ontologies for student and learning material modeling [16,23,27,28,30,59]. Thus, we decided to use ontological representation for student modeling in our prototype. ...
In this paper, a multi-agent based adaptive e-learning system that supports personalization based on learning styles is proposed. Considering that the importance of distance education has increased with the effect of the Covid-19 pandemic, it is aimed to propose an adaptive e-learning system solution that offers more effective learning experiences by taking into account the individual differences in the learning processes of the students. The Felder and Silverman learning style model was used to represent individual differences in students’ learning processes. In our system, it is aimed to recommend learning materials that are suitable for learning styles and previous knowledge levels of the students. With the multi-agent based structure, an effective control mechanism is devised to monitor the interaction of students with the system and to observe the learning levels of each student. The purpose of this control mechanism is to provide a higher efficiency in the subjects the students study compared to non-personalized e-learning systems. This study focuses on the proposed architecture and the development of the first prototype of it. In order to test the effectiveness of the system, personalized course materials should be prepared according to the learning styles of the students. In this context, it is planned to use the proposed system in future studies within the scope of a course in which the educational content is personalized.
... This increases the re-usability of the learner and domain models among different systems, and therefore, provides new solutions for interoperability of different e-learning systems [16]. Learning objects and learning style models have been modeled by using ontologies in various systems [17][18][19][20][21][22][23]. A review on ontology usage in e-learning systems that focuses on metadata modeling of learning objects, especially with the IEEE LOM standard is presented by [14]. ...
... The proposed learner model has a relatively simple design, yet the main contribution of it lies on its flexibility for combining different learning style models and examining the relationships between these learning style models. Most of the studies in the literature use a single learning style model [1,3,17,18,22,23,25] or a combination of two models [19,21]. Our approach provides a foundation to observe the interaction among the three learning style models currently modeled in the learner ontology, based on learner behaviors in the e-learning system. ...
... Ontology-based representations of learners and learning resources have been developed and used in different adaptive e-learning systems [14,16]. Students, teachers, institutions, learning materials and other components of adaptive e-learning systems are represented semantically by using ontologies [17][18][19][20][21][22][23]. Different learning style models are also modeled with ontologies as part of the student/learner models in adaptive e-learning systems [18,19,[21][22][23]. ...
Learning style models are used as indicators of individual differences of learners based on observations during learning processes. Numerous learning style models have been developed to model the individual differences of learners. Among these models, Felder-Silverman, Honey-Mumford and Kolb learning style models are the most-widely used ones in the literature. Learning style models are frequently used to provide personalization in adaptive e-learning systems. On the other hand, with the advancements on Semantic Web technologies in the last decade, ontologies have been used to represent domain knowledge and user information in the e-learning field, too. Ontological learner models have been developed and learners have been modeled based on their individual differences, usually based on their learning styles. In this regard, we examined how learning style models have been modeled with ontologies in different adaptive e-learning systems for personalization. Then, we proposed a learner modeling ontology based on three learning style models; Felder-Silverman, Honey-Mumford and Kolb; for personalized e-learning. Initial usage of the proposed learner ontology in a multi-agent based e-learning system is also discussed with current limitations and future work directions.
... Valasky [5] proposes the ORLM ontology that allows to recommend learning materials according to the students' learning styles. The main purpose is to personalize the learning material to fit the students' needs and preferences. ...
Learning contents creation supported on computer tools has triggered the scientific community for a couple of decades. However, teachers have been facing more and different challenges, namely the emergence of other delivery learning approaches besides the traditional educational settings, the diversification of the student target population, and the recognition of different ways of learning. In education domain, diverse recommender systems have been developed so far for recommending learning activities and more specifically, learning objects. This research work is focused on teaching-learning techniques recommendation to assist teachers by providing them recommendation about which teaching-learning techniques should scaffold teaching-learning activities to be carried out by students. This paper presents a recommender model sustained in diverse elements, namely, a hybrid recommender system, an association rules mechanism to infer possible combinations of teaching-learning techniques, and collaborative work among several actors in education. An evaluation is carried out and the preliminary results are very encouraging, revealing that teachers seem very enthusiastic and motivated to rethink their teaching-learning techniques when designing teaching-learning activities.
... In work [24] an ontology-based recommendation method is proposed. As the main point for recommendation, the personalization is considered. ...
p>Nowadays, intelligent e-learning systems which can adapt to learner's needs and preferences, became very popular. Many studies have demonstrated that such systems can increase the eects of learning. However, providing adaptability requires consideration of many factors. The main problems concern user modeling and personalization, collaborative learning, determining and modifying learning senarios, analyzing learner's learning styles. Determining the optimal learning scenario adapted to students' needs is very important part of an e-learning system. According to psychological research, learning path should follow the students' needs, such as (i.a.): content, level of diculty or presentation version. Optimal learning path can allow for easier and faster gaining of knowledge. In this paper an overview of methods for recommending learning material is presented. Moreover, a method for determining a learning scenario in Intelligent Tutoring Systems is proposed. For this purpose, an Analytic Hierarchy Process (AHP) method is used.</p
... To achieve this target authors used ontology as an appropriate tool that allows the recommendation of learning materials based on learning styles. They create simple ontology using three concepts: learning material, personal information and learners' learning styles (Valaski, Malucelli, & Reinehr, 2011). ...
... To achieve this target, authors use ontology as an appropriate tool that allows the recommendation of learning materials based on learning styles. They create simple Ontology based on three concepts: learning material, personal information and learners' learning styles (Valaski et al., 2011). ...
The current approaches of e-learning face challenges, in isolation of learners from learning process, and shortage of learning process quality. The researchers mentioned that the next generation of e-learning is e-learning ecosystem. E-learning ecosystem has many advantages, in which, learners form groups, collaborate with each other and with educators, and content designed for interaction. E-learning ecosystem faces some issues. It applies teacher-student model, in which, fixed learning pathway is considered suitable for all learners. Consequently, learners are presented with limited personalized materials. E-learning ecosystem needs to merge the personalization's concept. Semantic web ontology based personalization of learning environment plays a leading role to build smart e-learning ecosystem.
This paper previews a detailed study which addresses research papers that apply ontology within learning environment. Most of these studies focus on personalizing e-learning by providing learners with suitable learning objects, ignoring the other learning process components. This paper proposes and implements framework for smart e-learning ecosystem using ontology and SWRL. A new direction is proposed. This direction fosters the creation of a separate four ontologies for the personalized full learning package which is composed of learner model and all the learning process components (learning objects, learning activities and teaching methods).
... Valasky [5] proposes the ORLM ontology that allows to recommend learning materials according to the students' learning styles. The main purpose is to personalize the learning material to fit the students' needs and preferences. ...
The creation of computer-based design tools to help teachers in designing learning scenarios, more precisely teaching-learning activities, has great importance in education. Those tools are most valuable if enriched with special features as, for example, templates, scripts or wizards used to guide the teacher through the design process. Recommendation mechanisms anchored in solid theoretical achievements are currently a huge challenge in the scientific research. This paper presents a proposal for teaching-learning techniques recommendation supported by an ontological modeling approach. The recommendation aims to assist educators in designing of teaching-learning activities. The recommendation process is part of the ACEM model which integrates an authoring design tool. We propose that the recommendation mechanism will help teachers preparing those activities and improving the use of different learning techniques.
... Um ambiente para compartilhamento de materiais de aprendizagem relacionados a Engenharia de Software apoiado por ontologias foi desenvolvimento em trabalhos anteriores pelos pesquisadores [6][7][8]. ...
... Para o desenvolvimento da ontologia para a recomendação de materiais de aprendizagem da área de Engenharia de Software, de acordo com o estilo de aprendizagem (ORLM -Ontology Recommendation Learning Material) [22] foi utilizada a metodologia 101 [17] ...
... A ORLM é o resultado do desenvolvimento de uma ontologia para a representação de materiais de aprendizagem de acordo com os estilos de aprendizagem [22]. Na Figura 2 são apresentadas as classes, propriedades e relacionamentos da ORLM. ...
Human resources development is an important factor in software process improvement projects. A learning environment is proposed in order to provide a means so that software engineers may achieve needed skills. In such environment, learning occurs in an autonomous way through learning material sharing. The learning materials are recommended considering the learning style and ontologies are used in order to structure knowledge. An experiment was performed with 84 Software Engineering students and 80.95% of the participants considered the use of learning style in the proposed environment rather important.
Brazilian organizations must comply with the Brazilian General Data Protection Law (LGPD) and this need must be carried out in harmony with legacy systems and in the new systems developed and used by organizations. In this article we present an overview of the LGPD implementation process by public and private organizations in Brazil. We conducted a literature review and a survey with Information and Communication Technology (ICT) professionals to investigate and understand how organizations are adapting to LGPD. The results show that more than 46% of the organizations have a Data Protection Officer (DPO) and only 54% of the data holders have free access to the duration and form that their data is being treated, being able to consult this information for free and facilitated. However, 59% of the participants stated that the sharing of personal data stored by the organization is carried out only with partners of the organization, in accordance with the LGPD and when strictly necessary and 51% stated that the organization performs the logging of all accesses to the personal data. In addition, 96.7% of organizations have already suffered some sanction / notification from the National Data Protection Agency (ANPD). According to our findings, we can conclude that Brazilian organizations are not yet in full compliance with the LGPD.
