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.
Human emotions are strongly coupled with physical and mental health of any individual. While emotions exbibit complex physiological and biological phenomenon, yet studies reveal that physiological signals can be used as an indirect measure of emotions. In unprecedented circumstances alike coronavirus (Covid-19) outbreak, a remote IoT enabled solution, coupled with AI can interpret and communicate emotions to serve substantially in healthcare and related fields. This work proposes an integrated IoT framework which enables wireless communication of physiological signals to data processing hub where Long Short-Term Memory (LSTM) based emotion recognition is performed. The proposed framework offers real-time communication and recognition of emotions which enables health monitoring and distance learning support amidst pandemics. In this study, the achieved results are very promising. In proposed IoT protocols (TS-MAC and R-MAC) ultra-low latency of 1 millisecond is achieved. R-MAC also offers improved reliability in comparison to state-of-the-art. In addition, the proposed deep learning scheme offers high performance (f-score) of 95%. The achieved results in communications and AI match the interdependency requirements of deep learning and IoT frameworks, thus ensuring the suitability of proposed work in distance learning, student engagement, healthcare, emotion support and general wellbeing.
The extraction of spatial semantics is important in many real-world applications such as geographical information systems, robotics and navigation, semantic search, etc. Moreover, spatial semantics are the most relevant semantics related to the visualization of language. The goal of multimodal spatial role labeling task is to extract spatial information from free text while exploiting accompanying images. This task is a multimodal extension of spatial role labeling task which has been previously introduced as a semantic evaluation task in the SemEval series. The multimodal aspect of the task makes it appropriate for the CLEF lab series. In this paper, we provide an overview of the task of multimodal spatial role labeling. We describe the task, sub-tasks, corpora, annotations, evaluation metrics, and the results of the baseline and the task participant.
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