This study investigates learner’s reading behaviors in a critical reading task in humanities course using learning analytics techniques. A Critical Analysis of Literature and Cinema course was selected as a context. The course activities evolved over 10 years, and for this instance, some face-to-face classroom critical reading activities were migrated to online mode by using BookRoll, a learning analytics enhanced eBook platform. Students (n=22 out of the 50 registered) accessed Hayavadana, an Indian play uploaded on BookRoll, and attempted to identify performative elements and cultural references in the text and highlight them. In this study, we analyze learner’s reading logs gathered in the learning record store linked to BookRoll during that activity. We extend our previous work where we identify four online reading profiles: effortful, strategic, wanderers, and check-out, based on learner’s clickstream interactions and time spent with the content. We validate the profiles with qualitative interview data collected from the learners and illustrate the quantified learning behaviors of each of those profiles based on an engagement metric. Our work aims to initiate further discussion related to the application of learning analytics in humanities courses both to probe into the learning behaviors of the students and thereby enhance the experiences with the use of interactive learning environments and data-driven services.
Deep Learning (DL)-based image classification models are essential for autonomous vehicle (AV) perception modules since incorrect categorization might have severe repercussions. Adversarial attacks are widely studied cyberattacks that can lead DL models to predict inaccurate output, such as incorrectly classified traffic signs by the perception module of an autonomous vehicle. In this study, we create and compare Hybrid Classical-Quantum Deep Learning (HCQ-DL) models with Classical Deep Learning (C-DL) models to demonstrate robustness against adversarial attacks for perception modules. Before feeding them into the quantum system, we used transfer learning models like AlexNet and VGG-16 as feature extractors. We tested over 1000 quantum circuits in our HCQ-DL models for Projected Gradient Descent (PGD), Fast Gradient Sign Attack (FGSA), and Gradient Attack (GA), which are three well-known untargeted adversarial approaches. We evaluated the performance of all models during adversarial attack and no-attack scenarios. Our HCQ-DL models maintain accuracy above 95% during a no-attack scenario and above 91% for GA and FGSA attacks, which is higher than C-DL models. During the PGD attack, our AlexNet-based HCQ-DL model maintained an accuracy of 85% compared to C-DL models that achieved accuracies below 21%.
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