In recent years, machine learning has been increasingly applied to the area of mental health diagnosis, treatment, support, research, and clinical administration. In particular, using less-invasive wearables combined with the artificial intelligence to monitor, or diagnose the mental diseases has tremendous needs in real practice. To this end, we propose a novel approach for automatic detection of major depressive disorder. Firstly, spontaneous activity physical data are recorded by a watch-type device equipped with an activity monitor. Subsequently, a bag-of-behaviour-words approach is applied to extract higher representations from the raw sensor data in an unsupervised scenario. Finally, a support vector machine is selected as the classifier to make the predictions on screening the major depressive disorder. There are 69 healthy control subjects, and 14 major depressive disorder patients involved in this study. The experimental results demonstrate the effectiveness of the proposed method in a rigorous subject-independent test, which achieves an unweighted average recall at 59.3 % (an accuracy of 66.0 %). This unweighted average recall significantly ( < .05, onetailed -test) outperforms human hand-crafted features with an unweighted average recall at 53.6 % (an accuracy of 61.7 %).
A less-invasive method for the diagnosis of the major depressive disorder can be useful for both the psychiatrists and the patients. We propose a machine learning framework for automatically discriminating patients suffering from the major depressive disorder (n n n = = = 1 1 14 4 4) and healthy subjects (n n n = = = 1 1 17 7 7). To this end, spontaneous physical activity data were recorded via a watch-type computer device equipped by the participants in their daily lives. Two machine learning models are investigated and compared, i. e., support vector machines, and deep recurrent neural networks. Experimental results show that, both of the two methods, i. e., the static model fed with human hand-crafted features, and the sequential model fed with raw data can reach a promising performance with an unweighted average recall at 76.0 % and 56.3 %, respectively.
Blended learning technique has adapted many new digitized tools to facilitate students with flexible and self-phased learning opportunities. The flipped classroom strategy, one of the blended learning models has been limited by low engagement of students in the online component. In the present study, we augment a Flip and Pair (F&P), an active-learning strategy into the blended learning course. Following the AB type single group quasi-experimental design, we evaluated the effects of F&P strategy on the student’s engagement and learning while orchestrating it for an undergraduate engineering physics course. Our results highlighted that there is a positive correlation between the engagement (computed based on learning logs of TEEL (Technology-enhanced and Evidence-based Education and Learning) platform in the F&P activities with that of the performance score (knowledge quizzes and final exam). F&P strategy had a better contribution compared to Flip and Traditional Teaching (F&TT) strategy with respect to both engagement and performance. Also, students exhibited a positive perception of learning and engagement. Based on our findings, we identified the key instructional measures that an instructor can follow to increase student engagement while using the F&P strategy.
Recent spread of the COVID-19 forces governments around the world to temporarily close educational institutions. In this paper, we evaluated learning engagement, level of satisfaction and anxiety of e-book based remote teaching strategy on an online learning platform. The research involves 358 students at an urban junior-high school in Japan. Learning logs were analyzed to measure student engagement, whereas survey responses indicated their perception regarding the remote learning experience. Log analysis revealed that the average completion rate over 267 learning materials was 67%. We also observed a significant decrease in engagement 3 weeks after remote learning and different subjects and grades. Survey analysis showed students felt both satisfaction and anxiety about remote learning. However, there were significant differences in the level of satisfaction between different grades. The results indicated that (1) maintaining students’ motivation is a challenge to remote learning in secondary schools, and (2) we need to relieve students’ anxiety about their own progress in the class and their classes after the break. This study is the first to report trends in actual teaching–learning engagement, which were recorded during sessions of emergency remote teaching in Japanese schools. The results can inform the future implementation of remote learning in junior-high schools.
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