Teaching with virtual reality (VR) has transformed the traditional educational environment by providing immersive and engaging learning experiences. Students may explore realistic simulations, control items, and study hands-on in a virtual reality classroom. Using virtual reality technology Mathematics education may be taken to the next level by utilizing this immersive technology, which improves how students learn and engage with mathematical topics. 3D geometry and trigonometry issues are typically difficult for students to grasp when presented in 2D planes. Using VR Geometry students, in particular, may enter a three-dimensional mathematical environment where abstract concepts come to life. They are able to visualize geometric forms, manipulate things, and investigate mathematical concepts in ways that typical schools do not allow. In this article, a Geometrical classroom was created utilizing a virtual reality environment. There are two sections to the classroom. In the first section, students may study about various 3D geometrical forms such as cubes, cuboids, cones, cylinders, spheres, and pyramids. In the second section, a game-based quiz app was designed in which students could engage and earn reward points and also able to understand the answer through 3D visualization. For the scientific evaluation of the VR platform, a three-phase research study was conducted. A total of 24 students took part in the study, and they were separated into two groups based on their understanding of the themes presented on the platform. In the last step, technical evaluation of the system as well as learning improvement were measured using a questionnaire after going through the VR game-based platform. The study yields excellent findings in that students’ understanding increases, and 83% of students found that they can readily perceive diverse geometrical forms using the VR game-based platform.

Enabling multiple-purpose robots to follow textual instructions is an important challenge on the path to automating skill acquisition. In order to contribute to this goal, we work with physical exercise instructions as an everyday activity domain where textual descriptions are usually focused on body movements. Body movements are a common element across a broad range of activities that are of interest for robotic automation. Developing a text-to-animation system, as a first step towards understanding language for machines, is an important task. The process requires natural language understanding (NLU) including non-declarative sentences and the extraction of semantic information from complex syntactic structures with a large number of potential interpretations. Despite a comparatively high density of semantic references to body movements, exercise instructions still contain a large amount of underspecified information. Detecting and bridging or filling such underspecified elements is extremely challenging when relying on methods from NLU alone. Humans, however, can often add such implicit information with ease, due to its embodied nature. We present a process that contains a combination of a semantic parser and a Bayesian network. It explicates the information that is contained in textual movement instructions so that an animation execution of the motion-sequences performed by a virtual humanoid character can be rendered. Human computation is then employed to determine best candidates and to further inform the models in order to increase performance adequacy.

Language analysis is very important for the native speaker to connect with the digital world. Assamese is a relatively unexplored language. In this report, we analyze different aspects of speech-to-text processing, starting from building a speech corpus, defining syllable rules, and finally developing a speech search engine of Assamese. We have collected about 20 hours of speech in three (viz., read, extempore, and conversation) modes and transcribed it. We also discuss some issues and challenges faced during development of the corpus. We have developed an automatic syllabification model with 11 rules for the Assamese language and found an accuracy of more than 95% in our result. We found 12 different syllable patterns where 5 are found most frequent. The maximum length of a syllable found is four letters. With the help of Hidden Markov Model Toolkit (HTK) 3.5, we used deep learning based neural network for our speech recognition model, where we obtained 78.05% accuracy for automatic transcription of Assamese speech.