A significant amount of research has indicated that students’ procrastination tendencies are an important factor influencing the performance of students in online learning. It is, therefore, vital for educators to be aware of the presence of such behavior trends as students with lower procrastination tendencies usually achieve better than those with higher procrastination. In the present study, we propose a novel algorithm—using student’s assignment submission behavior—to predict the performance of students with learning difficulties through procrastination behavior (called PPP). Unlike many existing works, PPP not only considers late or non-submissions, but also investigates students’ behavioral patterns before the due date of assignments. PPP firstly builds feature vectors representing the submission behavior of students for each assignment, then applies a clustering method to the feature vectors for labelling students as a procrastinator, procrastination candidate, or non-procrastinator, and finally employs and compares several classification methods to best classify students. To evaluate the effectiveness of PPP, we use a course including 242 students from the University of Tartu in Estonia. The results reveal that PPP could successfully predict students’ performance through their procrastination behaviors with an accuracy of 96%. Linear support vector machine appears to be the best classifier among others in terms of continuous features, and neural network in categorical features, where categorical features tend to perform slightly better than continuous. Finally, we found that the predictive power of all classification methods is lowered by an increment in class numbers formed by clustering.
Dementia is the most prevalent degenerative disease in seniors in which progression can be prevented or delayed by early diagnosis. In this study, we proposed a two-layer model inspired by the method used in dementia support centers for the early diagnosis of dementia and using machine learning techniques. Data were collected from patients who received dementia screening from 2008 to 2013 at the Gangbuk-Gu center for dementia in the Republic of Korea. The data consisted of the patient's gender, age, education, the Mini-Mental State Examination in the Korean version of the CERAD Assessment Packet (MMSE-KC) for dementia screening test, and the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD-K) for the dementia precise test. In the proposed model, MMSE-KC data are initially classified into normal and abnormal. In the second stage, CERAD-K data are used to classify dementia and mild cognitive impairment. The performance of each algorithm is compared with that of Naive Bayes, Bayes Network, Begging, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) using Precision, Recall and F-measure. Comparing the F-measure values of normal, mild cognitive impairment (MCI), and dementia, the MLP was the highest in the F-measure values of normal with 0.97, while the SVM appear to be the highest in MCI and dementia with 0.739. Using the proposed early diagnosis model for dementia reduces the time and economic burden and can help simplify the diagnosis method for dementia.
Educational games have been increasingly used to improve students’ computational thinking. However, most existing games have focused on the theoretical knowledge of computational thinking, ignoring the development of computational thinking skills. Moreover, there is a lack of integration of adaptivity into educational computer games for computational thinking, which is crucial to addressing individual needs in developing computational thinking skills. In this study, we present an adaptive educational computer game, called AutoThinking, for developing students’ computational thinking skills in addition to their conceptual knowledge. To evaluate the effects of the game, we conducted an experimental study with 79 elementary school students in Estonia, where the experimental group learned with AutoThinking, while the control group used a traditional technology-enhanced learning approach. Our findings show that learning with the adaptive educational computer game significantly improved students’ computational thinking related to both conceptual knowledge and skills. Moreover, students using the adaptive educational computer game showed a significantly higher level of interest, satisfaction, flow state, and technology acceptance in learning computational thinking. Implications of the findings are also discussed.
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