EdTech and Emerging Technologies
Permanent URI for this collectionhttps://hdl.handle.net/10125/107533
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Item type: Item , Lexical Analysis of Automatic Transcriptions Using Speech-to-Text Services: A Statistically Evaluated Case Study(2024-01-03) Falvojr, Venilton; Marcolino, Anderson; Bruno, Diego; Martins Falvo, Catherine; Osório, Fernando Santos; Barbosa, EllenThis paper introduces Speech2Learning, an innovative architecture designed to leverage Speech-To-Text (STT) technology to enhance the accessibility of Learning Objects (LOs). Stemming from a recognized gap in prior Systematic Mapping, the primary objective of this architecture is to simplify the development of flexible educational solutions. In a collaborative endeavor with Brazilian EdTech DIO, we instantiated Speech2Learning as a Proof of Concept (PoC) to subtitle video lessons on their e-learning platform. This PoC was essential to obtain valuable insights for a more comprehensive Case Study. Therefore, we performed a lexical similarity analysis on the automatic transcriptions generated by leading STT providers in Portuguese, English and Spanish. Finally, we carried out a rigorous Statistical Analysis to evaluate the quantitative data from the Case Study. Our findings highlight the potential of Speech2Learning to promote the accessibility of LOs, as well as the relevance of continued research to increase the accuracy of STT services.Item type: Item , Exploring Immersive VR-based Emphatic Learning Using Interpersonal Reactivity Index(2024-01-03) Radianti, Jaziar; Wass, Sofie; Steig, Marianne Hovet; Safari, Mugula ChrisRecent research shows a knowledge gap concerning how VR-based material can be used in higher education, especially students that will work with people with intellectual disabilities and can support empathy skills relating to emotion and attitudes. In this study we explore the use of VR-based material as a tool for facilitating the empathy skill among bachelor students. We conducted a set of systematic experiments of 360-degree immersive materials using head-mounted Virtual Reality devices. Data was gathered from the second semester students through two experiment survey of Interpersonal Reactivity Index (IRI), covering pre- and post-tests before and after using VR. The results suggest some empathy improvement in some empathy subscales of IRI, although the amount of improvement is not so significant. The students show positive and supportive attitudes towards the use of immersive material for improving empathy skills.Item type: Item , Utilizing Convolutional Neural Networks and Eye-Tracking Data for Classroom Attention Tracking(2024-01-03) Boswell, Bradley; Sanders, Andrew; Walia, Gursimran; Allen, AndrewInstructors often use facial cues of their students as key indicators of student attention levels. However, this method can pose a problem in online and computer-based learning environments. While other research has shown computer vision and eye-tracking could be used with machine learning techniques to predict attentiveness, they have shown only moderate success in terms of accuracy. In this work, we improve upon existing techniques for student attention tracking. We employed our previously developed Non-Intrusive Classroom Attention Tracking System (NiCATS) to collect facial images and eye-tracking data of students during three controlled experiments that represent common academic scenarios. Our first contribution is using convolutional neural networks to predict student attentiveness with an F1-Score of 0.91. Our second contribution is the validation of using eye-tracking metrics in conjunction with machine learning models to predict the attentiveness of students with up to 0.78 F1-Score, which could be useful when webcam privacy is a concern.Item type: Item , Introduction to the Minitrack on EdTech and Emerging Technologies(2024-01-03) Shekhar, Gaurav; Khazanchi, Deepak