This paper reflects on a project-based curriculum employing constructed languages to teach linguistics, with a focus on phonology. In a special topics linguistics course, nine students were led through the construction of a language. While students in introductory linguistics courses sometimes struggle with phonology, active engagement with a semester-long language construction project endowed these students with the practical motivation to understand (1) what phonology is, (2) how phonological rules work, and (3) why rules surface in the first place. They readily captured generalizations based on natural classes of sounds, recognizing the systematicity of their constructed phonology. Student performance and engagement in this course support the use of constructed languages as a pedagogical tool in linguistics. Because an ongoing project builds in problem-solving opportunities and processual thinking, highlighting relationships among key concepts, students achieve a more comprehensive understanding of core areas in the broader linguistic picture.

This study creates a virtual space for language learning using a user-customizable metaverse platform and explores its potential for EFL learning. To this end, a virtual learning space, grounded in constructivist learning principles – contextualized learning, active learning, and collaborative learning – was created on a 2D metaverse platform. The metaverse was designed as a simulated deserted island for enjoyable and playful learning, allowing the students to actively explore, discover, and interact as they look for clues to escape the island. For educational application, 29 Korean middle school students participated in a two-hour activity. Data included screen recordings of student activities, student surveys, and interviews with the students and teachers. The findings showed that, as an EFL learning space of playful constructivism, the metaverse had great potential to embed contextualized learning and served as a medium for active learning that positively affected student interest and motivation. The results confirmed that the team-based approach combined with a game-like metaverse fostered student collaboration. Overall, the study showcased how language instructors can make use of a customizable metaverse for L2 learning and how a virtual space may serve as an arena for learner-centered instruction.

Motion planning for high-DOF multi-arm systems operating in complex environments remains a challenging problem, with many motion planning algorithms requiring evaluation of the minimum collision distance and its derivative. Because of the computational complexity of calculating the collision distance, recent methods have attempted to leverage data-driven machine learning methods to learn the collision distance. Because of the significant training dataset requirements for high-DOF robots, existing kernel-based methods, which require $O(N^2)$ memory and computation resources, where $N$ denotes the number of dataset points, often perform poorly. This paper proposes a new active learning method for learning the collision distance function that overcomes the limitations of existing methods: (i) the size of the training dataset remains fixed, with the dataset containing more points near the collision boundary as learning proceeds, and (ii) calculating collision distances in the higher-dimensional link $SE(3)^n$ configuration space – here $n$ denotes the number of links – leads to more accurate and robust collision distance function learning. Performance evaluations with high-DOF multi-arm robot systems demonstrate the advantages of the proposed active learning-based strategy vis-$\grave{\text{a}}$-vis existing learning-based methods.

In this article, we showcase the pilot scenario of The Trojan War, an educational self-directed game that combines text inspired by ancient Greek (as well as Roman) literature with graphics based on the ‘Geometric style’, an authentic Greek style of painting contemporary with the composition of the Homeric epics. Our game uses interactive scenarios to support active learning strategies of students interested in Classical Studies in both tertiary and secondary education. Players can take on the role of key characters, making choices that can prevent, start, or stop the Trojan War, as well as determine their own personal outcomes. The learners are thus presented with the opportunity to explore alternative pathways to rewrite the history of the War. In the process, they can apply their subject knowledge and develop their intellectual and critical skills. They also become familiar with a distinctive and expressive early Greek artistic style, the so-called Geometric. Rather than focusing on winning, the game aims to give students the opportunity to engage with important ideas and values of ancient Greek culture by exploring multiple perspectives on the topic. It also provides a valuable lesson on the potentially wide-ranging consequences of individual choices, which is a core element of responsible citizenship.

Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. Gaussian process (GP) models are widely used for this purpose, but they struggle with capturing complex non-stationary behaviour and scaling to large datasets. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues. A ConvGNP uses neural networks to parameterise a joint Gaussian distribution at arbitrary target locations, enabling flexibility and scalability. Using simulated surface air temperature anomaly over Antarctica as training data, the ConvGNP learns spatial and seasonal non-stationarities, outperforming a non-stationary GP baseline. In a simulated sensor placement experiment, the ConvGNP better predicts the performance boost obtained from new observations than GP baselines, leading to more informative sensor placements. We contrast our approach with physics-based sensor placement methods and propose future steps towards an operational sensor placement recommendation system. Our work could help to realise environmental digital twins that actively direct measurement sampling to improve the digital representation of reality.

Named entity recognition (NER) aims to identify mentions of named entities in an unstructured text and classify them into predefined named entity classes. While deep learning-based pre-trained language models help to achieve good predictive performances in NER, many domain-specific NER applications still call for a substantial amount of labeled data. Active learning (AL), a general framework for the label acquisition problem, has been used for NER tasks to minimize the annotation cost without sacrificing model performance. However, the heavily imbalanced class distribution of tokens introduces challenges in designing effective AL querying methods for NER. We propose several AL sentence query evaluation functions that pay more attention to potential positive tokens and evaluate these proposed functions with both sentence-based and token-based cost evaluation strategies. We also propose a better data-driven normalization approach to penalize sentences that are too long or too short. Our experiments on three datasets from different domains reveal that the proposed approach reduces the number of annotated tokens while achieving better or comparable prediction performance with conventional methods.

Model order reduction (MOR) can provide low-dimensional numerical models for fast simulation. Unlike intrusive methods, nonintrusive methods are attractive because they can be applied even without access to full order models (FOMs). Since nonintrusive MOR methods strongly rely on snapshots of the FOMs, constructing good snapshot sets becomes crucial. In this work, we propose a novel active-learning-based approach for use in conjunction with nonintrusive MOR methods. It is based on two crucial novelties. First, our approach uses joint space sampling to prepare a data pool of the training data. The training data are selected from the data pool using a greedy strategy supported by an error estimator based on Gaussian process regression. Second, we introduce a case-independent validation strategy based on probably approximately correct learning. While the methods proposed here can be applied to different MOR methods, we test them here with artificial neural networks and operator inference.

Although an accurate reliability assessment is essential to build a resilient infrastructure, it usually requires time-consuming computation. To reduce the computational burden, machine learning-based surrogate models have been used extensively to predict the probability of failure for structural designs. Nevertheless, the surrogate model still needs to compute and assess a certain number of training samples to achieve sufficient prediction accuracy. This paper proposes a new surrogate method for reliability analysis called Adaptive Hyperball Kriging Reliability Analysis (AHKRA). The AHKRA method revolves around using a hyperball-based sampling region. The radius of the hyperball represents the precision of reliability analysis. It is iteratively adjusted based on the number of samples required to evaluate the probability of failure with a target coefficient of variation. AHKRA adopts samples in a hyperball instead of an n-sigma rule-based sampling region to avoid the curse of dimensionality. The application of AHKRA in ten mathematical and two practical cases verifies its accuracy, efficiency, and robustness as it outperforms previous Kriging-based methods.