The main objective of this study is to apply data mining techniques to predict and analyze students' academic performance based on their academic record and forum participation. Educational Data Mining (EDM) is an emerging tool for academic intervention. The educational institutions can use EDU for extensive analysis of students' characteristics. In this study, we have collected students' data from two undergraduate courses. Three different data mining classification algorithms (Naï ve Bayes, Neural Network, and Decision Tree) were used on the dataset. The prediction performance of three classifiers are measured and compared. It was observed that Naï ve Bayes classifier outperforms other two classifiers by achieving overall prediction accuracy of 86%. This study will help teachers to improve student academic performance.
The integration of new technologies in the classrooms opens new possibilities for the teaching and learning process. Technologies such as student response system (e.g. Clicker) are getting popularity among teachers due to its effects on student learning performance. In this study, our primary objective is to investigate the effect of Socrative with combination of smartphones on student learning performance. We also observed the benefits of interactivity between the teacher and the students and among classmates, which positively influences collaborative learning and engagement of students in the class. We test these relationships experimentally in a community college class environment using data from a survey answered by students in information technology associate degree. The results of our study reveal that collaborative learning and engagement of student in the class improves student learning performance. We highly recommend these tools in educational settings to support the learning process.
Due to rapid advancements in the field of information and communication technologies, mobile health (mHealth) has become a significant topic in the delivery of healthcare. Despite the perceived advantages and the large number of mHealth initiatives, the success of mHealth ultimately relies on whether these initiatives are used; their benefits will be diminished should people not use them. Previous literature has found that the adoption of mHealth by users is not yet widespread, and little research has been conducted on this problem. Therefore, this study identifies the antecedents of the intention to use mHealth and proposes a general model that might prove beneficial in explaining the acceptance of mHealth. The authors performed a quantitative meta-analysis of 49 journal papers published over the past 10 years and systematically reviewed the evidence regarding the most commonly identified factors that may affect the acceptance of mHealth. The findings indicate that the proposed model includes the seven most commonly used relationships in the selected articles. More specifically, the model assumes that perceived usefulness positively affects perceived ease of use and user behavioral intention to use mHealth is commonly influenced by five factors: perceived usefulness, perceived ease of use, attitude toward behavior, subjective norms, and facilitating conditions. The results of this work provide important insights into the predictors of mHealth acceptance for future researchers and practitioners.
The ability to predict students' academic performance is critical for any educational institution that aims to improve their students' learning process and achievement. Although students' performance prediction problem is studied widely, it still represents a challenge and complex issue for educational institutions due to the different features that affect students learning process and achievement in courses. Moreover, the utilization of web-based learning systems in education provides opportunities to study how students learning and what learning behavior leading them to success. The main objective of this research was to investigate the impact of assessment grades and online activity data in the Learning Management System (LMS) on students' academic performance. Based on one of the commonly used data mining techniques for prediction, called classification. Five classification algorithms were applied that decision tree, random forest, sequential minimal optimization, multilayer perceptron, and logistic regression. Experimental results revealed that assessment grades are the most important features affecting students' academic performance. Moreover, prediction models that included assessment grades alone or in combination with activity data perform better than models based on activity data alone. Also, random forest algorithm performs well for predicting student a cademic performance, followed by decision tree.
Power scheduling of domestic appliances is a vital preference for bridging the gap between demand and generation of electricity in a microgrid. For a stable microgrid, an acceptable mechanism must reduce the peak to average ratio (PAR) of power demand with supplementary benefits for consumers as reduced electricity charges. Recent studies have focused on PAR and cost reduction for a small consumer population. Furthermore, researchers have mainly considered homogeneous consumer loads. This study focuses on residential power scheduling for electricity cost reduction for consumers and load profile PAR curtailment for a relatively large consumer population with non-homogeneous loads. A sample population of 1000 consumers from various classes of society is considered. The proposed dynamic clustered community home energy management system (DCCHEMS) allows the clustering of appliances based on time overlap criteria. Comparatively flatter power demand is attained by utilizing the clustered appliances in conjunction with particle swarm optimization under the influence of user-defined constraints. Modified inclined block rates with real-time electricity pricing strategies are deployed to minimize the electricity costs. DCCHEMS achieved higher efficiency rates in contrast to the traditional non-clustering and static clustering optimization schemes. An improvement of 21% in peak to average ratio, 4% in cost reduction, and 19% in variance to mean ratio is obtained.
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