Presentations An Academic Conference In Virtual Reality? – Evaluation Of A SocialVR ConferenceMiriam Mulders(1),
Raphael Zender(2)
1: University of Duisburg Essen, Germany; 2: University of Potsdam, Germany
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One of the first academic conferences in head-mounted display (HMD)-based Social Virtual Reality (SocialVR) was realized. The conference aims to support knowledge acquisition and informal exchange regarding the technology SocialVR itself and the use of Virtual and Augmented Reality technologies (VR/AR) in vocational education. The paper presents results of an explorative study of 75 conference participants. Results indicate that SocialVR is generally suited to host an academic conference. In some areas, it seems inferior or equivalent to other digital formats or face-to-face events. In other areas, it offers added value. Further research is needed to take advantage of these positive effects.
Evaluation Design Methodology for an AR App for English Literacy SkillsJennifer Tiede(1), Farzin Matin(2), Rita Treacy(3),
Silke Grafe(1), Eleni Mangina(2)
1: University of Würzburg, Germany; 2: University College Dublin, Ireland; 3: Wordsworthlearning, Ireland
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Augmented Reality (AR) is a powerful tool for supporting students’ learning processes, but sound research findings regarding the systematic evaluation of AR-enhanced teaching and learning processes are scarce especially with regards to literacy attainment. Hence, against the background of a systematic literature review the evaluation approach in the European H2020 ARETE project is introduced. The effects of Augmented Reality (AR) on fourth to sixth grade primary school students’ literacy skills acquisition are assessed. The evaluation approach has been designed systematically to respond to important research desiderata such as the development of multimethod and multi-perspective evaluation approaches combining different target groups and measurements. The aim of this paper is the design clarification and the provision of the research desideratum of evaluation design and metrics that are suitable for systematically assessing students’ literacy attainment when utilising AR.
Using Support Vector Machine on EEG Signals for College Students' Immersive Learning EvaluationBoxin Wan(1,2), Wenshan Huang(1), Ludi Bai(1,2), Junqi Guo(1,2)
1: School of Artificial Intelligence,Beijing Normal University, Beijing, China; 2: Center for Big Data Mining & Knowledge Engineering,Beijing Normal University, Beijing, China
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Conventional methods such as questionnaires and scales to evaluate learners’ learning immersion are influenced by individuals’ subjective factors. The non-synchronism between the learning state and after-learning investigation also reduces the accuracy. We propose a new method to evaluate learners’ learning immersion based on electroencephalogram (EEG) and support vector machine (SVM). We construct 2 learning scenarios to induce immersive senses: VR video learning for high-level immersion and online English word learning for low-level immersion. To distinguish two immersion levels, students' EEGs are collected. After entering their attention score, relaxation score, the synchronization rate between the 2 scores, high alpha and low beta wave into SVM model, the precision accuracy reaches 87.80%. Taken the classified results and the participants’ self-reports together, we find VR devices can create a more immersive environment which improves learners’ learning effect. Our findings provide evidence supporting the feasibility of predicting learning immersion levels by physiological recordings.
