Design Tasks Research Articles

Task design, L1 literacy, and second language oracy

Abstract This paper reports a replication of part of Tavakoli and Foster’s (2008) investigation into the influence of narrative task design on second language (L2) oral performance. The initial study found in part that narratives with both foreground and background information elicited significantly greater syntactic complexity than those with only foreground information. This close replication adds the variable of literacy, conducting the study with adult refugees to New Zealand with low first language (L1) literacy. Participants narrated two of the four cartoon strips in Tavakoli and Foster (2008). In contrast to the initial study, background information in the narrative tasks had no impact on the syntactic complexity, lexical diversity, or fluency of performances. However, given the tendency of participants to omit background events, this outcome is discussed in terms of visual literacy, and aptness to describe rather than connect the cartoon frames. The implications for the use of narrative tasks with such learners are explored.

Developing TPACK through task design: exploring the use of multiple modes of representation and the promotion of mathematical processes

ABSTRACT This research considers the need to train teachers who can effectively integrate technology for teaching and learning mathematics. The primary goal of this research is to showcase a successful training experience by identifying and describing some indicators of the technological pedagogical content knowledge acquired by pre-service early childhood education teachers during their participation in this initial training programme. Specifically, we focus on the different ways of representing mathematical concepts and the mathematical processes that can be activated by including such modes of representation. The participants in this research were 28 PECTs, who were organised into groups that planned a set of tasks using technology to teach geometric concepts. The results show that the groups employed a variety of modes of representation, translations and transformations (connections), fostering the emergence of different mathematical processes. Thus, our proposal to integrate technology was satisfactory in this sense, suggesting its potential applicability across diverse subjects, particularly those in which the ways of representing concepts play a relevant role. Also, the analysis conducted may be extrapolated to subjects others than mathematics.

Experimental examination of the incentive and sorting effects of pay-for-performance on creative performance.

There is long-standing debate over whether pay-for-performance (PFP) enhances or undermines creative performance. Traditional motivation and revised creativity theories suggest that PFP and intrinsic task interest combine additively to enhance creative performance, whereas cognitive evaluation theory and self-determination theory posit that PFP undermines task interest and thus intrinsic motivation and creative performance. To help resolve these conflicting predictions and provide a more comprehensive understanding of how and when PFP influences creative performance, this study incorporates both the incentive and sorting mechanisms of PFP, varying strengths of PFP, and task autonomy as a key moderator. A novel laboratory experiment was designed to capture key elements of workplace contexts, including in the design of the creative tasks, PFP strengths based on benchmarking of U.S. companies' practices, and allowing subjects to sort into different pay conditions, consistent with the opportunity for mobility in the labor market. The results showed that, through both incentive and sorting mechanisms, high PFP intensity enhanced creative performance more so than low PFP intensity, and both were superior to fixed pay. Importantly, task autonomy positively moderated the PFP-creative performance relationship, such that creative performance under PFP increased much more under higher task autonomy. Finally, the difference in creative performance under PFP versus fixed pay was greater when subjects were allowed to sort into their preferred pay conditions than when they worked only under randomly assigned pay conditions. Theoretical and practical implications and future research directions are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Understanding university English instructors’ assessment literacy: a formative assessment perspective

Formative assessment is a powerful approach to inform, direct, and enhance teaching and learning. To deploy it in classrooms, teachers are expected to be assessment-capable. However, few studies have explored the extent to which teachers are literate in formative assessment applications and their levels of literacy, especially in Chinese tertiary settings. This study examined Chinese university English instructors’ assessment literacy by investigating what three participants knew, thought about, and how they applied formative assessment. A knowledge-conception-practice framework was applied to explore assessment literacy in the four formative assessment processes (i.e., design, use, interpretation, and communication) to engage student learning. Individual interviews, classroom observations, and supplementary documents were adopted as instruments for data collection. Through iterative and comparative thematic analysis, findings revealed that notwithstanding variations, all three instructors possessed basic formative assessment knowledge and were willing to design and conduct formative assessment-related activities for students’ learning engagement and enhancement. However, they held product-oriented rather than process-oriented mindsets when designing, using, interpreting, and communicating formative assessment. They also lacked sufficient assessment knowledge and strategies to effectively engage students in self-regulated learning through learner-centered task design, success criteria, and process-oriented feedback. Lastly, the study discussed theoretical and pedagogical implications regarding how to conceptualize and develop English instructors’ formative assessment literacy in mainland China and beyond.

Machine learning-driven discovery of high-performance MEMS disk resonator gyroscope structural topologies

The design of the microelectromechanical system (MEMS) disc resonator gyroscope (DRG) structural topology is crucial for its physical properties and performance. However, creating novel high-performance MEMS DRGs has long been viewed as a formidable challenge owing to their enormous design space, the complexity of microscale physical effects, and time-consuming finite element analysis (FEA). Here, we introduce a new machine learning-driven approach to discover high-performance DRG topologies. We represent the DRG topology as pixelated binary matrices and formulate the design task as a path-planning problem. This path-planning problem is solved via deep reinforcement learning (DRL). In addition, we develop a convolutional neural network-based surrogate model to replace the expensive FEA to provide reward signals for DRL training. Benefiting from the computational efficiency of neural networks, our approach achieves a significant acceleration ratio of 4.03 × 105 compared with FEA, reducing each DRL training run to only 426.5 s. Through 8000 training runs, we discovered 7120 novel structural topologies that achieve navigation-grade precision. Many of these surpass traditional designs in performance by several orders of magnitude, revealing innovative solutions previously unconceived by humans.

Pre-service teachers’ understandings of exploratory task design in mathematics: from GeoGebra task design to the 8 th grade student

Pre-service teachers’ understandings of exploratory task design in mathematics: from GeoGebra task design to the 8 ^th grade student

Cross-species translational paradigms for assessing positive valence system as defined by the RDoC matrix.

Functions associated with processing reward-related information are fundamental drivers of motivation, learning, and goal-directed behavior. Such functions have been classified as the positive valence system under the Research Domain and Criteria (RDoC) criteria and are negatively impacted across a range of psychiatric disorders and mental illnesses. The positive valence system is composed of three comprehensive categories containing related but dissociable functions that are organized into either Reward Responsiveness, Reward Learning, or Reward Valuation. The presence of overlapping behavioral dysfunction across diagnostic mental disorders is in-part what motivated the RDoC initiative, which emphasized that the study of mental illness focus on investigating relevant behavior and cognitive functions and their underlying mechanisms, rather than separating efforts on diagnostic categories (i.e., transdiagnostic). Moreover, the RDoC approach is well-suited for preclinical neuroscience research, as the rise in genetic toolboxes and associated neurotechnologies enables researchers to probe specific cellular targets with high specificity. Thus, there is an opportunity to dissect whether behaviors and cognitive functions are supported by shared or distinct neural mechanisms. For preclinical research to effectively inform our understandings of human behavior however, the cognitive and behavioral paradigms should have predictive, neurobiological, and pharmacological predictive validity to the human test. Touchscreen-based testing systems provide a further advantage for this endeavor enabling tasks to be presented to animals using the same media and task design as in humans. Here, we outline the primary categories of the positive valence system and review the work that has been done cross-species to investigate the neurobiology and neurochemistry underlying reward-related functioning. Additionally, we provide clinical tasks outlined by RDoC, along with validity and/or need for further validation for analogous rodent paradigms with a focus on implementing the touchscreen-based cognitive testing systems.

Differentiable design of freeform diffractive optical elements for beam shaping by representing phase distribution using multi-level B-splines

The generation of a specific laser beam profile on the work surface is key to various laser beam shaping tasks, relying heavily on diffractive optical elements (DOEs). Most beam-shaping DOEs are designed using iterative Fourier transform algorithms (IFTAs), which generally have slow convergence and prone to stagnate at local minima. Moreover, the microreliefs generated by IFTAs tend to be irregular, complicating manufacturing and causing uncontrolled scattering of light. We propose a differentiable DOE design method that applies a phase-smoothness constraint using multi-level B-splines. A multi-scale gradient-descent optimization strategy, naturally linked with the multi-level B-splines, is employed to robustly determine the optimized phase distribution that is fully continuous. This, in turn, can lead to more regular DOE microreliefs, which can simplify the fabrication process and be less sensitive to changes in wavelength and working distance. Furthermore, our method can also design a fully continuous freeform lens, distinguished from most freeform lens design approaches by its foundation in physical optics rather than geometrical optics. Simulation and experimental results of several design tasks demonstrate the effectiveness of the proposed method.

Simultaneous Remote Monitoring of Multiple Automated Vehicles

As vehicle automation continues to become a more prevalent part of everyday life, remote operators may be able to contribute to making this technology safer and more efficient. Such operators could be expected to monitor vehicles until it is necessary for them to intervene. In recent years several countries have passed legislation mandating the implementation of remote operation, making it a plausible scenario. For many practical reasons, operators may monitor more than one vehicle. This study provides an initial look into how remote operators might perform when asked to attend to multiple concurrent displays using simulated automated vehicles. The initial results indicate that participants retained a comparable level of accuracy in detecting failures in automation as the number of vehicles they monitored rose from one to two, albeit with a higher workload. The results and the methodology provide insights into potential task designs for remote operators of automated vehicles.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Addressing high-performance data sparsity in metasurface inverse design using multi-objective optimization and diffusion probabilistic models

Recent advancements in deep learning, particularly generative networks capable of producing high-freedom structures, have significantly enhanced the precise generation of meta-atoms. However, these methodologies typically rely on an abundance of high-performance data, which remains scarce in many practical design scenarios. To bridge this gap, our study introduces what we believe to be a novel approach that synergistically combines multi-objective optimization algorithms with an enhanced diffusion model featuring an attention mechanism, termed MetaDiffusion-Att. Using the complex design task of dual-polarized, wide-angle incidence, and broadband low-emissivity electromagnetic glass as an application example, we demonstrate the effectiveness of our method through qualitative and quantitative experiments. The introduced multi-objective optimization method significantly captures more high-performance samples while ensuring high degrees of freedom, compared to currently widely used generic dataset construction methods. The MetaDiffusion-Att model, improved by the introduced attention mechanism, significantly outperforms conventional WGAN-GP and conditional VAE methods in generation accuracy and quality under small datasets. Furthermore, the proposed method exhibits extrapolation capabilities, generating new structures with performance surpassing that of the dataset, further enriching the design space. This framework provides a promising solution for the inverse design of metasurfaces in challenging scenarios with sparse high-performance samples.

GeoLLM: A specialized large language model framework for intelligent geotechnical design

Large language models (LLMs) have achieved remarkable success in various industrial and research fields, enhancing work efficiency by assisting machines in comprehending human language. In geotechnical design where extensive repetitive cross-checking of design codes consumes considerable time and labour, the utilization of LLMs to enhance design procedures has not been explored before. The challenge is to ensure that LLMs accurately comprehend professional geotechnical information from text and execute mathematical calculations correctly. This study makes the first attempt at developing a specialized LLM framework, GeoLLM, integrated with an innovative prompt engineering strategy to extract professional information from text and enable accurate mathematical calculations. GeoLLM is applied to the design of single piles involving bearing capacity and settlement calculations. The results reveal that GeoLLM exhibits excellent performance in single pile cases. Additionally, compared with LLMs of varying architectures and sizes, commercial LLMs with over 100 billion parameters presented outstanding comprehensive capacities, while those with 1.8 ∼ 72 billion parameters degraded relatively. These findings indicate the promising capacity of GeoLLM to address professional tasks in geotechnical design.

Communicative AI Agents in Mathematical Task Design: A Qualitative Study of GPT Network Acting as a Multi-professional Team

Communicative AI Agents in Mathematical Task Design: A Qualitative Study of GPT Network Acting as a Multi-professional Team

Teacher-led strategies for maximizing learning outcomes in higher education / Estrategias dirigidas por el profesorado para maximizar los resultados de aprendizaje en Educación Superior

A number of challenges have arisen in educational policies for higher education (HE) contexts in the last decades, leading to a shift from teacher- to learner-oriented methodologies. However, there is a dearth of studies revising effective teacher-led strategies aimed at maximizing learning outcomes in HE, since learners may still need teachers’ support in organizing their acquisition process. Consequently, adopting this instructional perspective, this paper has examined how teachers’ methodology can be (re)designed to promote successful learning in HE by means of strategies aimed at scaffolding content, language, design of tasks and output. A systematic list of effective instructional strategies is developed in this paper, overlooking those techniques that, despite their frequent use, have not proved to be highly beneficial for promoting learning outcomes. When students become expert learners, a shift from instructional to discovery or learner-based methodologies may occur.

FSscore: A Personalized Machine Learning‐Based Synthetic Feasibility Score

AbstractDetermining whether a molecule can be synthesized is crucial in chemistry and drug discovery, as it guides experimental prioritization and molecule ranking in de novo design tasks. Existing scoring approaches to assess synthetic feasibility struggle to extrapolate to new chemical spaces or fail to discriminate based on subtle differences such as chirality. This work addresses these limitations by introducing the Focused Synthesizability score (FSscore), which uses machine learning to rank structures based on their relative ease of synthesis. First, a baseline trained on an extensive set of reactant‐product pairs is established, which is then refined with expert human feedback tailored to specific chemical spaces. This targeted fine‐tuning improves performance on these chemical scopes, enabling more accurate differentiation between molecules that are hard and easy to synthesize. The FSscore showcases how a human‐in‐the‐loop framework can be utilized to optimize the assessment of synthetic feasibility for various chemical applications.

Route Planning Method of UAV Patrol Based on High Precision Tower Model

The high-precision positioning technology of industrial Unmanned Aerial Vehicle (UAV) plays a vital role in intelligent trajectory planning in power inspection. First, the information on power facilities is collected and processed to form a planning grid to optimize the flight path and correct the trajectory of UAV. However, the preparatory work is more complex, and the algorithm model which needs more detailed and complex terrain database data is not universal enough. To improve the safety of UAV and the efficiency of path planning, we establish a three-dimensional model of the power tower, calculate the coordinate position of the inspection work according to the information of the high-voltage transmission tower, and then determine the corresponding relationship between the inspection point and the flight trajectory combined with the safe distance. Based on the set of random interference semaphores and model rasterization, the discrete error strategy and Euler method are introduced to improve the performance. In each iteration, the best solution is first updated using the execution strategy. We continue to calculate the spatial coordinates of all reference points and provide the coordinate position distribution for the flight mission. The solution of equipment inspection trajectory optimization is realized by using dynamic obstacle perception. The proposed discrete measurement error self-correction algorithm is deployed on the flight platform to guide UAV inspection tasks according to the point cloud coordinate model of transmission equipment in the power system and to correct the flight route in time. To verify the research results, the Algorithm Artemisinin optimization and Whale Optimization Algorithm test verify that the ability of the proposed algorithm to get rid of local optimization is improved by 8.94% and, 12.42% respectively compared with other optimization algorithms. The convergence accuracy is 12.4% and 7.9% higher than other schemes, and it has a better effect in solving numerical problems. The research results using the embedded system to complete the task deployment and unloading provide a reference for trajectory planning and task design in different application scenarios.

Exploring collaborative writing among large groups in online distance learning through a public forum and private chat tool

Abstract This study explored how collaborative writing, an often-used instructional strategy in second language (L2) learning, intersects with large-group dynamics, and investigated their potential impact on the quality of writing outcomes in an online distance learning course. Using a mixed-methods approach, the research scrutinized intra-group interaction processes in two large groups undertaking a computer-assisted language learning writing assignment and evaluated the impact of these interaction processes on their writing products. Data from discussions in both a public online forum and a private social communication platform (WeChat) were collected, systematically coded, and analysed quantitatively and qualitatively based on language functions. Data collection also included an assessment of the written products and follow-up group interviews. The findings indicate distinct interaction patterns between high-performing and low-performing groups, characterised by an expert/participant pattern and a dominant/passive pattern, respectively. Additionally, insights from the interviews shed light on these interaction patterns and the potential impact on student learning outcomes. The study suggests practical implications, highlighting the importance of task design in promoting high levels of collaborative knowledge construction to enhance students’ writing skills and L2 language learning in large-group settings.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics.

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Evaluating the Performance of Topic Modeling Techniques with Human Validation to Support Qualitative Analysis

Examining the effectiveness of machine learning techniques in analyzing engineering students’ decision-making processes through topic modeling during simulation-based design tasks is crucial for advancing educational methods and tools. Thus, this study presents a comparative analysis of different supervised and unsupervised machine learning techniques for topic modeling, along with human validation. Hence, this manuscript contributes by evaluating the effectiveness of these techniques in identifying nuanced topics within the argumentation framework and improving computational methods for assessing students’ abilities and performance levels based on their informed decisions. This study examined the decision-making processes of engineering students as they participated in a simulation-based design challenge. During this task, students were prompted to use an argumentation framework to articulate their claims, evidence, and reasoning, by recording their informed design decisions in a design journal. This study combined qualitative and computational methods to analyze the students’ design journals and ensured the accuracy of the findings through the researchers’ review and interpretations of the results. Different machine learning models, including random forest, SVM, and K-nearest neighbors (KNNs), were tested for multilabel regression, using preprocessing techniques such as TF-IDF, GloVe, and BERT embeddings. Additionally, hyperparameter optimization and model interpretability were explored, along with models like RNNs with LSTM, XGBoost, and LightGBM. The results demonstrate that both supervised and unsupervised machine learning models effectively identified nuanced topics within the argumentation framework used during the design challenge of designing a zero-energy home for a Midwestern city using a CAD/CAE simulation platform. Notably, XGBoost exhibited superior predictive accuracy in estimating topic proportions, highlighting its potential for broader application in engineering education.

An exploratory study on second language learner engagement in different types of interactive tasks in video-chat and text-chat communication

Abstract This study investigated 48 Hong Kong English as a second language (ESL) learners’ engagement in three types of interactive tasks conducted via video chat or text chat. The learners, paired with their university friends, completed a descriptive task, a narrative task, and a decision-making task. They were randomly assigned to either the video-chat or text-chat group and were interviewed about their emotional engagement in each task immediately after task performance. The findings revealed that both groups of learners were behaviourally the least engaged but cognitively the most engaged in the decision-making task. They experienced positive emotions during the descriptive task, but fewer reported positive emotions in the other two tasks. The text-chat group was the most socially engaged in the decision-making task. Furthermore, the video-chat group was behaviourally and emotionally more engaged than the text-chat group. These findings have implications for task design and selection in computer-mediated communication.