Deep learning for a smart e-Iearning system

Learning analytics and collaborative groups of learners in distance education: a systematic mapping study

“…With this, the necessary academic interventions can be given to students to increase the percentage of approval in the course. Chanaa and Faddouli (2018) proposed a customized model to improve the course completion rate and provide appropriate content that meets the individual needs and preferences of the student. The personalized model aimed to analyze the cognitive acquisition of students throughout the learning period, combining cognition analysis with sentiment analysis and the learning style of each student.…”

“…Table 7 List of articles that contain analysis methods using records and log history References/Authors Analysis Methods/Techniques Altaf et al (2019) Neural networks to predict student performance based on log data. Chanaa and Faddouli (2018) Customized model with three main components, sentimental analysis, cognitive analysis and learning style. Dimopoulos et al (2013) Evaluation tool, called Enriched Heading of Learning Analytics (LAe-R).…”

“…Various methods or techniques were used. The methods or techniques most used by the authors were: data analysis with 37% (Islam et al, 2019;Hussain et al, 2018;Altaf et al, 2019;Omar and Abdesselam, 2017), followed by learning systems with 27% (Lagman and Mansul, 2017;Chanaa and Faddouli, 2018;Kolekar et al, 2018), Assessment tool with 18% (Graf et al, 2011;Dimopoulos et al, 2013), Recommendation System (Florian et al, 2011) and Data mining Systems with 9% (Lara et al, 2014). Group work is an important resource for teachers to promote collaborative learning, but there are many difficulties in identifying students with similar profiles in the formation of solid groups, in the case of distance learning courses, these difficulties increase and research on systems for the analysis of student profiles to recommend collaborative study groups are still scarce.…”

“…The benefits categorized as intelligent services are: learning management system (Lavoie and Proulx, 2019), semantic recommendation using ontology (Sharma and Ahuja, 2016); hybrid recommendation based on student profile (Kapembe and Quenum, 2019); deep reinforcement learning structure (Huang et al, 2019); decision-making system (El Fouki et al, 2017), content-based recommendation system (Venugopalan et al, 2016), domain-specific language (Balderas et al, 2013), WAVE architecture (Manhães et al, 2014), intelligent teaching assistant system (Wang et al, 2019), profile analysis system (El Moustamid et al, 2017), algorithm based on the technique of optimizing ant colonies (Kozierkiewicz-Hetmańska and Zyśk, 2013), prototype indicators (Florian et al, 2011), online learning systems based on big data technologies (Dahdouh et al, 2018), agentbased recommendation system, Java2D technology-based e-learning system (Hamada, 2012), ID based recommendation system (Zakrzewska, 2012;Anaya et al, 2013), capture system (Lagman and Mansul, 2017), custom model (Chanaa and Faddouli, 2018), ontology model (Joy et al, 2019), evaluation tool (Dimopoulos et al, 2013), adaptive recommendation method (Chen et al, 2020), Kernel Context Recommendender System algorithm (Iqbal et al, 2019), distributed course recommendation systems (Dahdouh et al, 2019), custom user interface (Kolekar et al, 2018), recommendation system techniques for educational data mining (Thai-Nghe et al, 2010), individualized artificial intelligence tutor and LBA model (Kim and Kim, 2020) based on a system called SBAN (Zaoudi and Belhadaoui, 2020). The application of methods and techniques of data analysis provide student grade prediction, behavior pattern detection, academic progress forecasting, modeling, course dropout risk prediction, also providing student performance feedback to teachers.…”

Análise de Dados e Serviços Inteligentes Aplicados na Educação à Distância: um mapeamento sistemático

“…As recomendações são baseadas em três coleções de dados: dos atributos dos membros dos grupos, dos materiais de ensino equipados com bandeiras indicando os alunos-alvo e o de um novo recurso do aluno. Chanaa e Faddouli (2018) propuseram um modelo personalizado com 3 componentes principais, análise sentimental, análise cognitiva e estilo de aprendizagem, que utiliza as trilhas e a pegada dos usuários quando utilizam um sistema de gerenciamento de aprendizado a fim de encontrar informações convenientes sobre os alunos e explorá-las com sucesso, além de melhorar a taxa de conclusão do curso e fornecer a eles conteúdos adequados que atendam às suas necessidades e preferências individuais.…”