Application of reflective journals to assess self-directed learning in a blended learning setting: a case study in Hong Kong

This paper presents the case study of a blended learning subject offered to a group of engineering students, in which a reflective journal was adopted as part of the assessment. It also presents an innovative attempt to explore the relationship between the reflections of students and their performance in the subject. The feedbacks from students were collected and analysed using text-mining techniques, and a machine-learning algorithm was used to identify the association between the reflective feedback text and the corresponding final grades of the students. The supervised machine-learning algorithm produces an inferred function from the training data so as to make predictions about the output values of the testing data. The results prove that reflective journals can be a valuable means of assessing student learning in a blended learning environment, and also offers a good reference for educators to have a better understanding regarding the performance of students.