WIP: Model of Self-Regulated Smart Learning Environment

Abstract

The use of smart and mobile technologies provides a smart learning environment that can support diverse learning needs. The self-regulated learning process has been identified as one of the strategies that can support students in the online learning environment. Metacognitive skills such as goal setting, task strategy, self-reaction, help-seeking, time management can be developed in a smart learning environment to support the learning process. However, despite the increasing research in the smart learning environment, there is a scarcity of welldocumented work on how the metacognitive components in a smart learning environment could be modeled to support the development of a self-regulated smart learning environment. This paper explored the metacognitive components in the smart learning environment to propose the hybrid model, referred to as MSLEM: Metacognitive Smart Learning Environment Model which can be beneficial to researchers for providing a personalized learning environment for supporting the online learning process

An Artificial Neural Network (ANN)-Based Learning Agent for Classifying Learning Styles in Self-Regulated Smart Learning Environment

“…The proposed approach to provide personalized learning content in a self-regulated smart learning environment is to develop a learning agent that can be integrated into the inference engine module of the MSLEM [17]. However, the reliability of adaptive depends on the precision of the learning style classification.…”

“…Previously, this research proposed a model of a self-regulated smart learning environment called the MSLEM-metacognitive smart learning environment model [17]. The model consists of six connected modules, i.e., cognitive detection, learning contents management, adaptive assessment, inference engine, metacognitive and intervention engine; it identified five metacognitive skills-goal settings (GS), help-seeking (HS), task strategies (TS), time-management (TM), and self-evaluation (SE) as the critical success skills for a self-regulated smart learning environment.…”

“…The model consists of six connected modules, i.e., cognitive detection, learning contents management, adaptive assessment, inference engine, metacognitive and intervention engine; it identified five metacognitive skills-goal settings (GS), help-seeking (HS), task strategies (TS), time-management (TM), and self-evaluation (SE) as the critical success skills for a self-regulated smart learning environment. A learning agent needs to be developed and integrated into the model's inference engine module to provide a system capable of personalizing learning contents to students based on their behavior in a learning environment [17].…”

Data science in education: interdisciplinary contributions

“…Uma massiva quantidade de dados tem sido utilizada em ambientes inteligentes de aprendizagem autorregulados (Gambo & Shakir, 2021), na avaliação de alunos, professores, métodos etc. (Rodrigues et al, 2018), em predições de desempenho acadêmico (Costa et al, 2017;Karlos et al, 2020;Tomasevic et al, 2020), em projetos de universidades inteligentes (Artífice et al, 2021), dentre outras aplicações.…”

Review of Genetic Algorithm to improve Students Academic Performance by applying Smart Learning

“…Gambo & Shakir (2021c) explore the metacognitive components in the smart learning environment to propose the hybrid model, called MSLEM: Metacognitive Smart Learning Environment Model, which can help researchers to provide a personalized learning environment to support the online learning process Ilkou & Signer (2020). propose the innovative combination of a knowledge graph that represents what one has to learn and a learning path that defines in which order things are to be learned.…”