Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable unlabeled samples for annotation and use as few-shot demonstrations, effectively reducing human annotation costs while promoting high performance.However, existing methods typically rely on output-level signals for the sample identification, such as predictive entropy or semantic similarities with test-time data based on external embeddings, which often overlook models' internal dynamics which could pinpoint specific knowledge gaps.To bridge this gap, we propose NEUFS, a Neuron-Aware Active Few-Shot Learning framework that shifts the selection paradigm from output-level proxies to models' internal dynamics.NEUFS utilizes neuron activation patterns to represent sample directly, and includes a dual-criteria selection strategy that: (1) ensures few-shot sample diversity with neuron patterns for broader example coverage, while (2) prioritizing on identifying informative and challenging few-shot samples LLMs tend to hallucinate by quantifying neuron consensus.Experiments on three datasets demonstrate that NEUFS excels in both reasoning and text classification tasks, outperforming existing AFSL baselines.Ablation studies further highlight that internal neuron activations provide a more principled and effective selection signal than external embeddings, validating the superiority of the proposed NEUFS.

Open Learner Models (OLM) show the learner model to users to assist their self-regulated learning by, for example, helping prompt reflection, facilitating planning and supporting navigation. OLMs can show different levels of detail of the underlying learner model, and can also structure the information differently. As a result, a trade-off may exist between the potential for better support for learning and the complexity of the information shown. This paper investigates students' perceptions about whether offering more and richer information in an OLM will result in more effective support for their self-regulated learning. In a first study, questionnaire responses relating to designs for six visualisations of varying complexity led to the implementation of three variations on one of the designs. A second controlled study involved students interacting with these variations. The study revealed that the most useful variation for searching for suitable learning material was a visualisation combining a basic coloured grid, an extended bar chart-like visualisation indicating related concepts, and a learning gauge.

Computational thinking describes key principles from computer science that are broadly generalizable. Robotics programs can be engaging learning environments for acquiring core computational thinking competencies. However, few empirical studies evaluate the effectiveness of a robotics programming curriculum for developing computational thinking knowledge and skills. This study measures pre/post gains with new computational thinking assessments given to middle school students who participated in a virtual robotics programming curriculum. Overall, participation in the virtual robotics curriculum was related to significant gains in pre- to posttest scores, with larger gains for students who made further progress through the curriculum. The success of this intervention suggests that participation in a scaffolded programming curriculum, within the context of virtual robotics, supports the development of generalizable computational thinking knowledge and skills that are associated with increased problem-solving performance on nonrobotics computing tasks. Furthermore, the particular units that students engage in may determine their level of growth in these competencies.

This study investigated the relationship between robotics team members' collaborative behaviors, communication and coordination in particular, during hypothetical challenges and their teams' actual performance during physical robotics challenges. Dataset included robot performance, robot design, research project, core values, and collaboration quality scores for 61 K-8 Robotics teams (N = 366) that participated in a FIRST LEGO League (FLL) Championship in 2015. Our analysis primarily focused on how well the collaboration quality scores predict team performance across different categories. Results indicated that the level of collaboration quality among team members is significantly associated with the team performance in the robotics tournament.

In complex systems with stochastic components, systems laws often emerge that describe higher level behavior regardless of lower level component configurations. In this paper, emergent laws for describing mechanochemical systems are investigated for processive myosin-actin motility systems. On the basis of prior experimental evidence that longer processive lifetimes are enabled by larger myosin ensembles, it is hypothesized that emergent scaling laws could coincide with myosin-actin contact probability or system energy consumption. Because processivity is difficult to predict analytically and measure experimentally, agent-based computational techniques are developed to simulate processive myosin ensembles and produce novel processive lifetime measurements. It is demonstrated that only systems energy relationships hold regardless of isoform configurations or ensemble size, and a unified expression for predicting processive lifetime is revealed. The finding of such laws provides insight for how patterns emerge in stochastic mechanochemical systems, while also informing understanding and engineering of complex biological systems.

An increasing focus in K-12 educational outreach is on the fields of science, technology, engineering, and mathematics (STEM). A challenge in educating students about STEM topics is the ability to communicate the key concepts on a level that engages the students. The student-directed learning approach allows the students to create educational content and the teacher to oversee the development. The student's peers serve as the audience, shifting the interface of students and teachers, as well as increasing student to student interaction. The proposed approach is based on the outcomes of a preliminary internship, which challenged students to develop a week-long module to communicate one of four sustainability concepts: green building, energy, water and green materials. The proposed study will focus on a larger sample of middle school students developing educational modules focused on sustainability and engineering. In-depth assessment of the student developers will provide information on audience awareness, self-efficacy, as well the effectiveness of the approach.

This paper describes a Web-based reciprocal peer review system called scaffolded writing and rewriting in the discipline (SWoRD), addressing its approaches to writing and rewriting as a sound function of peer reviews. It also provides an empirical evaluation showing that reciprocal peer reviews through SWoRD are very effective in improving writing quality in content classes.

This paper describes technology and tools for intelligent human-computer interaction (IHCI) in which human cognitive, perceptual, motor and affective factors are modeled and used to adapt the H-C interface. IHCI emphasizes that human behavior encompasses both apparent human behavior and the hidden mental state behind behavioral performance. IHCI expands on the interpretation of human activities, known as W4 (what, where, when, who). While W4 only addresses the apparent perceptual aspect of human behavior the W5+ technology for IHCI described in this paper addresses also the why and how questions, whose solution requires recognizing specific cognitive states. IHCI integrates parsing and interpretation of nonverbal information with a computational cognitive model of the user which, in turn, feeds into processes that adapt the interface to enhance operator performance and provide for rational decision-making. The technology proposed is based on a general four-stage interactive framework, which moves from parsing the raw sensory-motor input, to interpreting the user's motions and emotions, to building an understanding of the user's current cognitive state. It then diagnoses various problems in the situation and adapts the interface appropriately. The interactive component of the system improves processing at each stage. Examples of perceptual, behavioral, and cognitive tools are described throughout the paper Adaptive and intelligent HCI are important for novel applications of computing, including ubiquitous and human-centered computing.