Designing self-regulated smart learning environment for online education

Over a decade, there has been exponential growth in online learning, providingeducational opportunities for people with little or no access to education. Itis changing how people learn through the affordance of smart and wirelesstechnologies, allowing flexibility of time, place, and approaches to learning.Educational institutions and related organizations are taking up these opportunitiesto complement in-class learning with the online learning process toincrease access to education, enrollment and provide skilled-based courses,especially with the current challenges of the Covid-19 pandemic. However,online learning is challenging and needs learners to be self-motivated, active,and focused on achieving a learning goal. Furthermore, the increasing developmentsin smart and mobile technologies transform learning environmentsinto smart learning systems supporting online learning. Self-regulated learninghas been identified as one of the strategies that can support a learning processand be integrated into a smart learning environment to provide personalizedlearning experiences. However, despite the increasing research in the smarteducation system, there is a scarcity of well-documented theoretical modelson how metacognitive components of the smart learning environment andself-regulated learning processes could be integrated to support online learningexperiences. Besides, there is a scarcity of evaluation models of self-regulatedsmart learning environments to understand users’ experiences for effectiveimplementation. This thesis addressed the gaps in knowledge by extendingthe metacognitive components in the smart learning environment to develop ametacognitive smart learning environment model that provided a self-regulatedsmart learning environment for supporting online education. Moreover, thisresearch developed an artificial neural network-based learning agent to personalizelearning in the self-regulated smart learning environment. This researchfurther developed a model to evaluate factors influencing students’ satisfactionin the self-regulated smart learning environment. This thesis is based onthe pragmatism research paradigm that emphasizes participants’ voices. Itjustified the need for students’ participation in developing and implementinga self-regulated smart learning environment. This research was based ondesign-based research, highlighting the importance of cyclical evaluation ofa learning environment as part of the research and design processes. Datawere collected in three stages using a mixed-method design that addressedthe research questions among the final year students in the Department ofComputer Science, Adamawa State University Mubi-Nigeria. The first datacollection stage explored the students’ background, experiences, and learningneeds for implementing a self-regulated smart learning environment. Thefindings revealed that students understood the self-regulated learning processand its importance in their learning process. Besides, students have experiencesand technology resources for designing and implementing a self-regulatedlearning environment. The second and the third stages of the data collectionwere cyclical evaluations of the post-implementation that explored students’experiences, challenges, and factors influencing the students’ satisfaction. Thefindings show that students could follow the self-regulated learning process,and the experiences impacted their skills and knowledge contents. However,Students noted some implementation issues that needed improvement to supportlearning experiences, such as offline functionalities, coding facilities, andinteraction. Besides, system quality, information quality, and service qualityinfluenced students’ satisfaction moderated by the personal and environmentalcharacteristics. The findings also suggested that mobile networks, subsidizeddata, and smartphones can support effective implementation strategies. Thisresearch proposed pedagogical guidelines for designing and integrating a selfregulatedsmart learning environment to support online education. The findingsof this research were useful for stakeholders managing, developing, and designinglearning environments to support online learning for inclusive learningexperiences.