Evaluating neural networks as a method for identifying students in need of assistance

© 2017 ACM. Course instructors need to be able to identify students in need of assistance as early in the course as possible. Recent work has suggested that machine learning approaches applied to snapshots of small programming exercises may be an effective solution to this problem. However, these results have been obtained using data from a single institution, and prior work using features extracted from student code has been highly sensitive to differences in context. This work provides two contributions: first, a partial reproduction of previously published results, but in a different context, and second, an exploration of the efficacy of neural networks in solving this problem. Our findings confirm the importance of two features (the number of steps required to solve a problem and the correctness of key problems), indicate that machine learning techniques are relatively stable across contexts (both across terms in a single course and across courses), and suggest that neural network based approaches are as effective as the best Bayesian and decision tree methods. Furthermore, neural networks can be tuned to be reliably pessimistic, so they may serve a complementary role in solving the problem of identifying students who need assistance

Rapid collision detection for deformable objects using inclusion-fields applied to cloth simulation

We introduce an inclusion-field technique for fast detection of collisions between a highly deformable object and another object with limited deformations. We mainly target the cloth simulation application where cloth (highly deformable) collides with deforming skin of a moving human model (has limited deformation as skin stretches and compacts within finite spacial and temporal limits specified by the bending angle and speed). Our technique intermixes concepts from space voxelization and distance fields to make use of the limited deformation nature of human skin. The technique works by discretizing the space containing the object into cells, and giving each cell an inclusion property. This property specifies whether this cell lies inside, outside, or on the surface of the deforming object. As the object deforms, the cells’ inclusion properties are updated to maintain the correctness of the collision detection process. We tested our technique on a generally deforming Bezier surface, and on cloth simulation to detect collisions between cloth and several articulated and deforming human body parts. Results showed that the inclusion field allows real-time collision detection between cloth and limited deformable objects on a standard PC. The technique is simple and easy to implement

A Very Rare Basidiobolomycosis Case Presented with Cecal Perforation and Concomitant Hepatic Involvement in an Elderly Male Patient: A Case Study

This is a case report of Basidiobolomycosis in a 65-year-old male patient from Jizan presenting with colonic perforation and concomitant liver involvement from February 2021 to July 2021. To control the infection, the patient underwent colonic resection and segmental liver resection, as well as three antifungal drugs. The treatment was successful, and the condition was completely resolved

Explainable Artificial Intelligence for Human-Centric Data Analysis in Virtual Learning Environments

The amount of data to analyze in virtual learning environments (VLEs) grows exponentially everyday. The daily interaction of students with VLE platforms represents a digital foot print of the students' engagement with the learning materials and activities. This big and worth source of information needs to be managed and processed to be useful. Educational Data Mining and Learning Analytics are two research branches that have been recently emerged to analyze educational data. Artificial Intelligence techniques are commonly used to extract hidden knowledge from data and to construct models that could be used, for example, to predict students' outcomes. However, in the educational field, where the interaction between humans and AI systems is a main concern, there is a need of developing new Explainable AI (XAI) systems, that are able to communicate, in a human understandable way, the data analysis results. In this paper, we use an XAI tool, called ExpliClas, with the aim of facilitating data analysis in the context of the decision-making processes to be carried out by all the stakeholders involved in the educational process. The Open University Learning Analytics Dataset (OULAD) has been used to predict students' outcome, and both graphical and textual explanations of the predictions have shown the need and the effectiveness of using XAI in the educational field