A Desktop VR-based HCI framework for programming instruction

Author(s):  
Magesh Chandramouli ◽  
Justin Heffron
2021 ◽  
pp. 073563312110015
Author(s):  
Ting-Ling Lai ◽  
You-Sheng Lin ◽  
Chi-Yin Chou ◽  
Hsiu-Ping Yueh

The study aims to evaluate the effectiveness of an inquiry-based virtual reality (VR) science lab used in junior high school science classes. The Scientific Investigation VR Lab (SIVRLAB) is designed for 9th-grade students to learn about electrochemical cells. It is situated in a guided problem-solving context, where learners need to review the concept of oxidation-reduction reactions and assemble a voltaic cell to save a robot. The SIVRLAB features several cognitive supports and guides for students to plan and record experiments and resolve the problem. It has both a head-mounted display (HMD) version and a desktop VR version. The study recruited 66 9th graders from three classes to evaluate the two versions of the SIVRLAB. The students were assigned to one of three conditions, namely, (1) using immersive HMD SIVRLAB individually, (2) using desktop SIVRLAB individually, and (3) observing one student use immersive HMD SIVRLAB. The students were briefly introduced to the concept of electrochemical cells in the first class and were instructed to use the SIVRLAB sessions in the next class. The results from knowledge pre- and post-tests, a user experience survey, and students’ reflections were collected and analyzed qualitatively. The findings revealed that students who used the desktop VR obtained the highest test scores among the three groups. However, in the follow-up physical laboratory test, the performance of the students in the original HMD VR experimental group was better than those in the desktop VR experimental group. The paper also discusses student feedback and teacher observations regarding the design and interaction with immersive VR. Lastly, the implications of the study and recommendations for future studies are presented.


2022 ◽  
pp. 073563312110622
Author(s):  
Sinan Hopcan ◽  
Elif Polat ◽  
Ebru Albayrak

The pair programming approach is used to overcome the difficulties of the programming process in education environments. In this study, the interaction sequences during the paired programming of preservice teachers was investigated. Lag sequential analysis were used to explore students’ behavioral patterns in pair programming. The participants of the study consist of 14 students, seven pairs enrolled in a Programming Languages course. The findings indicate that there are significant behavioral learning sequences. During the program development process, students hesitated to create an algorithm and to improve an existing one while proposing the next step. In addition, they constantly waited for approval. Collaborative behaviors such as giving and receiving feedback and helping other partners were less observed in females. In addition, significant sequential driver and navigator behaviors were presented. The findings of the study have important implications for instructors and designers when using a pair programming approach in teaching programming. In the future, programming instruction environments can be designed by considering the learner behaviors that are presented in this study.


Author(s):  
Serpil Meri-Yilan

Virtual reality (VR) technology has recently started shaping learning, especially language learning, with the aim of immersing learners into a VR learning environment. However, because of the high system cost of fully immersive VR, desktop VR has been implemented and preferred in educational settings. Based on a constructivist approach, desktop VR has drawn attention to the need for learner autonomy and an authentic VR learning environment. Therefore, this chapter describes empirical research on desktop VR-based learning using a constructivist approach. The research examined university students' interaction and perceptions of learning in this kind of learning environment. Based on the empirical findings gathered from observations and interviews, this chapter has aimed to discuss not only the issues observed both in previous studies and in this chapter, but also additional issues such as scaffolding, self-paced learning, collaboration, and learner differences in order for learning to occur in a well-designed desktop VR learning environment.


Author(s):  
Casper G. Wickman ◽  
Rikard So¨derberg

In the automotive industry today, virtual geometry verification activities are conducted with nominal models in the early design phases. Later in the design process when the first physical test series are made, are concepts verified in a non-nominal manner. Errors detected at this stage can result in expensive post-conceptual changes. By combining Computer Aided Tolerance (CAT) simulation tools with Virtual Reality (VR) tools, virtual environments for non-nominal geometry verification can be utilized. This paper presents the results from a study, conducted at Volvo Cars, that investigates the perceptional aspects that are related to verification of quality appearance, using non-nominal virtual models. Although a realistic non-nominal model is created, the interpretation, i.e. how the model is perceived, must be clarified. This would represent a validation of the model from a perceptional point of view. Since the effect of geometric variation is a specific application, with high demands on realistic and detailed representation, perceptional studies are needed to ensure that VR and other virtual representations can be used for this kind of application. The question is whether it is possible to evaluate aspects like flush, gap and see-through in virtual environments. In this paper, two environments are compared, one physical and one corresponding virtual environment. Three adjusted physical vehicles are mapped to the virtual environment and compared using non-immersive desktop VR in a visualization clinic with test subjects from the automotive industry. The study indicates that virtual objects are judged as less good looking compared with physical objects. There is also a higher degree of uncertainness when judging virtual objects.


Author(s):  
Leo C. Ureel II ◽  
Michelle Jarvie-Eggart ◽  
Melanie Kueber Watkins ◽  
Russell Looks ◽  
Briana Bettin

2019 ◽  
Vol 6 ◽  
Author(s):  
Priyanka Srivastava ◽  
Anurag Rimzhim ◽  
Palash Vijay ◽  
Shruti Singh ◽  
Sushil Chandra

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