Competence analytics

A smart learning environment is characterized by the key provision of personalized learning experiences. To approach different degrees of personalization in online learning, this article introduces a framework called Smart Competence Analytics in LEarning (SCALE) that tracks finer-level learning experiences and translates them into opportunities for customized feedback, reflection, and regulation. The SCALE framework is implemented in four layers: the sensing layer, the analysis layer, the competence layer, and the visualization layer. The sensing layer provides the datasets to support context-awareness through state-of-the-art sensing technologies. The analysis layer, by the means of powerful code analysis tools, derives performance metrics (e.g., learner coding metrics) which serve as input to the competence layer to identify proficiency levels of learners. Finally, a learning analytics dashboard called MI-DASH (visualization layer) allows interaction with performance and competence metrics. The current SCALE system has been used in a study to track the habits, performances, and competences of novice programmers. Growth of coding competences of first-year engineering students has been captured in a continuous manner. Students have been provided with customized feedback to optimize their learning paths. The article describes the analytics-based approach pursued in the study and highlights key findings.