Resolving Learners Misunderstandings Using an Adaptive Divide and Correct Technique

The education system is one of the main government sectors that has been affected by COVID-19 pandemic. Most governments around the world have temporarily closed educational institutions and distance learning imposed a massive impact on students' learning processes. In an online teaching and learning environment, handling students' misunderstandings is a challenging and time-consuming task. Many of the proposed solutions for handling students' misunderstandings are highly demanding on teachers, and suffer from lack of descriptively. In this paper, we propose a novel adaptive divide and correct technique to assist teachers in providing formative feedback to students. Additionally, we lower teachers' cognitive load in comprehending misunderstandings by measuring their semantical commonality. Our experiment results showed that our approach could significantly augment teachers in providing formative feedback to a large number of students. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Using explainable deep learning in da Vinci Xi robot for tumor detection

Deep learning has proved successful in computer-aided detection in interpreting ultrasound images, COVID infections, identifying tumors from computed tomography (CT) scans for humans and animals. This paper proposes applications of deep learning in detecting cancerous cells inside patients via laparoscopic camera on da Vinci Xi surgical robots. The paper presents method for detecting tumor via object detection and classification/localizing using GRAD-CAM. Localization means heat map is drawn on the image highlighting the classified class. Analyzing images collected from publicly available partial robotic nephrectomy videos, for object detection, the final mAP was 0.974 and for classification the accuracy was 0.84.