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Creating and delivering a presentation for Project, Seminar, and Course work is an important academic activity included in the curricula of undergraduate engineering studies. The presentation should be graded based on the presentation skills, accuracy, and authenticity of the contents covered in the presentation. Educational institutes use rubrics to assess the presentation skills on different grounds, which is a cumbersome task for the teacher when the strength of students is significant. Our main objective is to automatically grade the students' presentation skills in terms of PowerPoint presentations and the student's confidence. The proposed system describes a method and dataset designed to automate grading students' presentation skills. Our research study is divided into two parts. In the first part, the PowerPoint presentation features corresponding to text appearance, tables, charts, images, footer, and hyperlinks are extracted to grade PowerPoint presentations. At the same time, Mel-frequency Cepstral Coefficients, Mel Spectrogram, and Chroma features are extracted from the students' audio to identify confidence in the second part of the study. The audio is recorded at the presentation time. Feature extraction programs are implemented in python using Python-pptx and Librosa library. The tree-based feature selection method is used to remove the irrelevant features. Random Forest Ensemble model gives 100 % accuracy while predicting the grade of PowerPoint presentations. Multilayer Perceptron model gives 88% accuracy while predicting the confidence level of the students. The output of both models is combined to grade the students' presentation skills. The quadratic Weighted Kappa (QWK) score is 0.82, which indicates a significant similarity between automated and human-rated scores.
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