NeoAVA: A virtual learning environment for Self-Regulated Learning to be used by students and teachers
Year: 2024
Type: conference-paper
Abstract: Many students face difficulties in self-managing their studies and making efficient choices about which resources to use, resulting in lower academic performance when using Virtual Learning Environments (VLE). The study proposed a web application integrated with Google Classroom, aimed at enhancing student performance through personalized educational recommendations based on self-regulated learning (SRL) strategies and Big Five (BF) personality traits. The research employs a Design Science Research methodology, involving problem identification, solution design, and evaluation using the Technology Acceptance Model (TAM) to assess the system’s usability and effectiveness. The methodology involved experiments with a small group of participants who provided feedback via a TAM survey. The results indicate positive acceptance of the system, with participants reporting that NeoAVA is useful, easy to use, and enhances their learning experience. The system leverages SRL and BF profiles to generate personalized recommendations that guide students toward better academic outcomes, showing promise in improving student performance through tailored interventions. The findings suggest the potential for broader application of NeoAVA across different educational platforms.
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Award: G29316538
OpenAlex ID: W4404839813