Two paths towards the future of remote studies using social VR

Over the last decade, remote experiments have become a widely used and integral method for many human-computer interaction domains. Nonetheless, extended reality (XR) researchers have been slow to adopt remote research methods. This can largely be attributed to standard remote experimentation techniques being ill-suited for the unique XR domain constraints. Existing research, albeit limited, has aimed to overcome these constraints and demonstrate the viability of traditional remote research methods for XR studies, yet most XR experiments have remained in-lab. This gap in XR methodology has never been more evident or detrimental than during the ongoing global COVID-19 pandemic. During the pandemic, safe and ethical co-present in-lab experimentation has become increasingly difficult, if not impossible. Many researchers struggled to transition to remote research methods resulting in delayed, canceled, or unsatisfactory experiments. Beyond this current crisis, remote research methods present several advantages, such as obtaining a larger sample and accessing specific user populations that have not been leveraged in XR research — leading to missed opportunities and potentially less rigorous results.Our previous research demonstrated the efficacy of using existing social virtual reality (VR) platforms to implement and conduct remote VR experiments. Social VR platforms provide an experienced and VR-equipped user base to recruit from and customizable distributed synchronous virtual environments to implement experiments, which makes them a natural fit for VR experiments. They allow researchers to be co-present in the same virtual environment as participants to proctor experiments, similar to how they would during a co-present in-lab study. However, existing social VR platforms were not built with this use-case in mind, resulting in several limitations, such as the inability to easily save data externally. These limitations prevent existing social VR platforms from being a viable long-term XR research method. Our previous work identified two potential paths towards establishing long-term social VR remote research methods. The first potential path is to partner with existing social VR platforms to create official channels for remote studies. The second potential path is to build a bespoke social VR platform specifically for conducting XR remote experiments. We believe both of these paths have their respective strengths and weakness and are viable long-term solutions for remote XR studies. In this position paper, we aim to contribute a detailed discussion of both of these paths, their benefits, limitations, and potential architecture. In so doing, we hope to provide the XR community our insights into how social VR research methods can be expanded and inspiration for the potential future of remote XR research.

Ontology Reasoning for Explanatory Feedback Generation to Teach How Algorithms Work

Developing algorithms using control structures and understanding their building blocks are essential skills in mastering programming. Ontologies and software reasoning is a promising method for developing intelligent tutoring systems in well-defined domains (like programming languages and algorithms); it can be used for many kinds of teaching tasks. In this work, we used a formal model consisting of production rules for Apache Jena reasoner as a basis for developing a constraint-based tutor for introductory programming domain. The tutor can determine fault reasons for any incorrect answer that a student can enter. The problem the student should solve is building an execution trace for the given algorithm. The problem is a closed-ended question that requires arranging given actions in the (unique) correct order; some actions can be used several times, while others can be omitted. Using formal reasoning to check domain constraints allowed us to provide explanatory feedback for all kinds of errors students can make.

Schema Compliant Consistency Management via Triple Graph Grammars and Integer Linear Programming

In the field of Model-Driven Engineering, Triple Graph Grammars (TGGs) play an important role as a rule-based means of implementing consistency management. From a declarative specification of a consistency relation, several operations including forward and backward transformations, (concurrent) synchronisation, and consistency checks can be automatically derived. For TGGs to be applicable in realistic application scenarios, expressiveness in terms of supported language features is very important. A TGG tool is schema compliant if it can take domain constraints, such as multiplicity constraints in a meta-model, into account when performing consistency management tasks. To guarantee schema compliance, most TGG tools allow application conditions to be attached as necessary to relevant rules. This strategy is problematic for at least two reasons: First, ensuring compliance to a sufficiently expressive schema for all previously mentioned derived operations is still an open challenge; to the best of our knowledge, all existing TGG tools only support a very restricted subset of application conditions. Second, it is conceptually demanding for the user to indirectly specify domain constraints as application conditions, especially because this has to be completely revisited every time the TGG or domain constraint is changed. While domain constraints can in theory be automatically transformed to obtain the required set of application conditions, this has only been successfully transferred to TGGs for a very limited subset of domain constraints. To address these limitations, this paper proposes a search-based strategy for achieving schema compliance. We show that all correctness and completeness properties, previously proven in a setting without domain constraints, still hold when schema compliance is to be additionally guaranteed. An implementation and experimental evaluation are provided to support our claim of practical applicability.