Dfam: A Distributed Feedback Analysis Mechanism for Knowledge Based Educational Big Data

Abstract

There is almost digitization of the entire educational system, as there is an abundant of digital materials available. The educational system is not left out from the global standardization as well as authorities are imposing certain standard at local and global level. As a result, these data which are getting generated in the context of educational also complies all the basic characteristics of the Big Data such as volume in terms of size, and others velocity, variety etc. In order to store, search and process an open source project, Apache Hadoop has been conceptualized, whereas it lags the application specific needs especially in the field of education to enhance the teaching and learning processes. In this paper, an architectural model is illustrated to demonstrate the existing eco-system and a proposed model for provisioning the enhanced teaching -learning mechanism, so that it can be adopted to enhance the intelligence into mechanism of educational framework.

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

“…The authors concluded that when considering performance indicators, the neural network is an excellent classifier and can be used to predict whether the student needs academic help. Olanrewaju et al (2016) introduced a new approach called Feedback Analysis Mechanism distributed (DFAM) for Big Data based on knowledge that can be applicable to the improvement of the concepts associated with the e-learning mechanisms on the cloud platform. The proposed system design perspective also included the analysis of feedback of different types of feelings provided by the respective users of the proposed system.…”

“…Qu et al (2018) Student performance prediction framework, which includes data processing using data stored in the warehouse. Olanrewaju et al (2016) Approach called Feedback Analysis Mechanism distributed for Big Data.…”

“…The benefits can be categorized into two aspects: 1) methods and techniques and 2) intelligent services. The benefits categorized as methods and techniques are: software frameworks for developing prediction models (Olivé et al, 2018), educational data mining system (Lara et al, 2014), pipeline infrastructure (Chaffai et al, 2017), student performance prediction structure (Qu et al, 2018), system oriented to analysis of results (Zorrilla et al, 2010), algorithms (Ros et al, 2017), methods based on a latent statistical class (Sorour et al, 2015), application of data mining using computational techniques (Peñafiel et al, 2018), mixed chart of terms (Clarizia et al, 2018), opinion mining platform (Spatiotis et al, 2018), data mining techniques such as grouping and association (Islam et al, 2019), statistical machine learning technique (Hussain et al, 2018), distributed feedback analysis engine for Big Data (Altaf et al, 2019), neural networks to predict performance (Olanrewaju et al, 2016), learning style prediction method, system that uses (Yang et al, 2014), voting methodology (Kotsiantis et al, 2010), based learning behavioral model (Chang and Chu, 2010), clustering algorithms (Omar and Abdesselam, 2017), method for predicting students' final grades by an RNR (Okubo et al, 2017), academic analysis tool (Graf et al, 2011), complementary techniques -agglomerate hierarchical cluster, epistemic network analysis and process mining (Rayón et al, 2014), learning analysis methodology (Uzir et al, 2020), method based on Learning Analytics (Guo et al, 2020) and explanatory model of student behavior (Jovanović et al, 2020).…”

“…The identification and presentation of clusters point to the main terms explored in the final corpus comprising 51 articles. After analyzing the 48 grouping terms, 6 terms were identified that stand out as a search trend, being: recommendation system (Thai-Nghe et al, 2010;Anaya et al, 2013;Venugopalan et al, 2016;Kapembe and Quenum, 2019;Iqbal et al, 2019;Dahdouh et al, 2019), educational data mining (Thai-Nghe et al, 2010;Lara et al, 2014;Olivé et al, 2018), analysis (Zorrilla et al, 2010;Graf et al, 2011;Olanrewaju et al, 2016;Ros et al, 2017;Peñafiel et al, 2018;Uzir et al, 2020), learning management system (Okubo et al, 2017;Olivé et al, 2018;Chanaa and Faddouli, 2018;Lavoie and Proulx, 2019), e-learning and platform e-learning (Chang and Chu, 2010;Zorrilla et al, 2010;Hamada, 2012;Anaya et al, 2013;Olanrewaju et al, 2016;Omar and Abdesselam, 2017;El Fouki et al, 2017;Lagman and Mansul, 2017;Clarizia et al, 2018;Joy et al, 2019;Dahdouh et al, 2019). These terms are within the red, blue and green clusters, and they have connections with each other and connections to the other clusters.…”

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

“…Sorour, Goda e Mine (2015) propuseram novos métodos baseados em uma classe estatística latente "Tópicos" para a tarefa de previsão da nota do aluno; os métodos convertem os comentários dos alunos usando análise semântica latente, análise semântica latente probabilística e geram modelos de previsão usando máquina de vetor de suporte e Rede Neural Artificial para prever as notas finais dos alunos. Rashidah et al (2016) introduziram uma nova abordagem denominada Mecanismo de Análise de Feedback distribuído para Big Data baseado em conhecimento que pode ser aplicável ao aprimoramento dos conceitos associados aos mecanismos de e-learning na plataforma de nuvem.…”