RealTema

In a time when educational institutions are adapting to the needs of individual learners, the need for individualized, data-informed student engagement is increasingly prevalent. Traditional nomothetic approaches that were looking at averages over the whole of the population often cloud the richly detailed realities of what makes up specific learner experiences. In contrast, idiographic approaches that focus on individual change over time have the potential to be transformational in understanding and improvement of student learning, affect, and engagement in contexts in their lives. This paper describes the UEFSurveyMobileApp (rename RealTema) : a mobile-first learning analytics system that combines moment-by-moment self-reporting, passive logging of behavior and feedback that leverages machine-intelligence. Building on Experience Sampling Method (ESM) and Ecological Momentary Assessment (EMA) principles in RealTime, the system captures moment-by-moment snapshots of students’ cognition, emotion, and behavior as they occur, creating a time-sensitive profile of engagement that changes as the learner does. Survey questions should be simple and accessible on mobile devices. Surveys must appear at the top level of navigation for easy access. Surveys should use proper survey elements (e.g., Likert scales) rather than radio buttons to measure mood and focus. Students should be able to respond to surveys using mood scales, short answers, and multiple-choice questions (e.g., "How focused are you right now?"). Teachers must be able to create survey questions. Due to time constraints, question deletion functionality may be limited or omitted. Admins/teachers must be able to: Create, edit, delete, and reuse survey templates. Schedule recurring surveys (daily, weekly, custom intervals). Send push/email notifications when new surveys are published. Send automated reminders for unanswered surveys. Surveys and templates must be stored and reusable. Users should be able to self-register through the app. Application should run as a mobile-first or web-based platform, and optionally integrate with Moodle. Periodic survey notifications must be supported through background scheduling. Behavioral data must be collected from the device, including: Time spent per screen App usage patterns Mood-screen behavior correlation Sentiment analysis and AI-based feedback should be applied to open-text responses. The system should support communication via REST APIs for survey responses and logging. Moodle course logs must be accessible and analyzed to support behavioral insights. Please see demos and https://youtube.com/shorts/E42rVycmh44?si=tizDBrcY5x_cO4lp andhttps://youtube.com/shorts/K3TAQAxqmMk?si=Y_OZ-ZpT1v_nWh1h