This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text‐based self‐evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information from student self‐evaluations yields a significant improvement in early‐stage prediction quality. The results also indicate the limited early‐stage predictive value of structured data, such as homework completion, attendance, and exam grades, due to data sparseness at the beginning of the course. Thus, applying sentiment analysis to unstructured data (e.g., self‐evaluation comments) can play an important role in improving the accuracy of early‐stage predictions. The findings present educators with an opportunity to provide students with real‐time feedback and support to help students become self‐regulated learners. Using the exploring results for improvement in teaching and learning initiatives is important to maintain students' performances and the effectiveness of the learning process.
A competency‐based curriculum involves an outcome‐based approach for cultivating graduates’ core competencies required for specific professions. A curriculum committee defines graduates’ outcomes of core competencies, designs a curriculum to cultivate core competencies, evaluates graduates’ outcomes, and reflects on and regulates the curriculum. However, curriculum committees lack systematic evaluative information for reflection. Learning analytics, an emerging data‐driven analytics of educational data, could be applied to assist curriculum committees in reflection. This study proposes competency‐based learning analytics, including seven analytic tools, to analyze curricula and graduates’ academic records to assist curriculum committees in reflecting (1) objectives, competencies, and curriculum design of competency‐based curricula and (2) faculty teaching and student learning. The proposed learning analytics were conducted on 14 departments of a university. This study reports the curriculum committees’ eight practical reflections of curricula, faculty teaching, and student learning. This study illustrates potential applications and impact of competency‐based learning analytics on competency‐based curricula.
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