We use the process and findings from a case study of design educators' practices of assessment and feedback to fuel theorizing about how to make AI useful in service of human experience. We build on Suchman's theory of situated actions. We perform a qualitative study of 11 educators in 5 fields, who teach design processes situated in project-based learning contexts. Through qualitative data gathering and analysis, we derive codes: design process; assessment and feedback challenges; and computational support. We twice invoke creative cognition's family resemblance principle. First, to explain how design instructors already use assessment rubrics and second, to explain the analogous role for design creativity analytics: no particular trait is necessary or sufficient; each only tends to indicate good design work. Human teachers remain essential. We develop a set of situated design creativity analytics--Fluency, Flexibility, Visual Consistency, Multiscale Organization, and Legible Contrast--to support instructors' efforts, by providing on-demand, learning objectives-based assessment and feedback to students. We theorize a methodology, which we call situating analytics, firstly because making AI support living human activity depends on aligning what analytics measure with situated practices. Further, we realize that analytics can become most significant to users by situating them through interfaces that integrate them into the material contexts of their use. Here, this means situating design creativity analytics into actual design environments. Through the case study, we identify situating analytics as a methodology for explaining analytics to users, because the iterative process of alignment with practice has the potential to enable data scientists to derive analytics that make sense as part of and support situated human experiences.

Planning for both immediate and long-term benefits becomes increasingly important in recommendation. Existing methods apply Reinforcement Learning (RL) to learn planning capacity by maximizing cumulative reward for long-term recommendation. However, the scarcity of recommendation data presents challenges such as instability and susceptibility to overfitting when training RL models from scratch, resulting in sub-optimal performance. In this light, we propose to leverage the remarkable planning capabilities over sparse data of Large Language Models (LLMs) for long-term recommendation. The key to achieving the target lies in formulating a guidance plan following principles of enhancing long-term engagement and grounding the plan to effective and executable actions in a personalized manner. To this end, we propose a Bi-level Learnable LLM Planner framework, which consists of a set of LLM instances and breaks down the learning process into macro-learning and micro-learning to learn macro-level guidance and micro-level personalized recommendation policies, respectively. Extensive experiments validate that the framework facilitates the planning ability of LLMs for long-term recommendation. Our code and data can be found at https://github.com/jizhi-zhang/BiLLP.

In programming education, providing manual feedback is essential but labour-intensive, posing challenges in consistency and timeliness. We introduce ECHO, a machine learning method to automate the reuse of feedback in educational code reviews by analysing patterns in abstract syntax trees. This study investigates two primary questions: whether ECHO can predict feedback annotations to specific lines of student code based on previously added annotations by human reviewers (RQ1), and whether its training and prediction speeds are suitable for using ECHO for real-time feedback during live code reviews by human reviewers (RQ2). Our results, based on annotations from both automated linting tools and human reviewers, show that ECHO can accurately and quickly predict appropriate feedback annotations. Its efficiency in processing and its flexibility in adapting to feedback patterns can significantly reduce the time and effort required for manual feedback provisioning in educational settings.

The accurate detection of Mesoscale Convective Systems (MCS) is crucial for meteorological monitoring due to their potential to cause significant destruction through severe weather phenomena such as hail, thunderstorms, and heavy rainfall. However, the existing methods for MCS detection mostly targets on single-frame detection, which just considers the static characteristics and ignores the temporal evolution in the life cycle of MCS. In this paper, we propose a novel encoder-decoder neural network for MCS detection(MCSDNet). MCSDNet has a simple architecture and is easy to expand. Different from the previous models, MCSDNet targets on multi-frames detection and leverages multi-scale spatiotemporal information for the detection of MCS regions in remote sensing imagery(RSI). As far as we know, it is the first work to utilize multi-scale spatiotemporal information to detect MCS regions. Firstly, we design a multi-scale spatiotemporal information module to extract multi-level semantic from different encoder levels, which makes our models can extract more detail spatiotemporal features. Secondly, a Spatiotemporal Mix Unit(STMU) is introduced to MCSDNet to capture both intra-frame features and inter-frame correlations, which is a scalable module and can be replaced by other spatiotemporal module, e.g., CNN, RNN, Transformer and our proposed Dual Spatiotemporal Attention(DSTA). This means that the future works about spatiotemporal modules can be easily integrated to our model. Finally, we present MCSRSI, the first publicly available dataset for multi-frames MCS detection based on visible channel images from the FY-4A satellite. We also conduct several experiments on MCSRSI and find that our proposed MCSDNet achieve the best performance on MCS detection task when comparing to other baseline methods.

Reinforcement Learning (RL) provides a framework in which agents can be trained, via trial and error, to solve complex decision-making problems. Learning with little supervision causes RL methods to require large amounts of data, which renders them too expensive for many applications (e.g. robotics). By reusing knowledge from a different task, knowledge transfer methods present an alternative to reduce the training time in RL. Given how severe data scarcity can be, there has been a growing interest for methods capable of transferring knowledge across different domains (i.e. problems with different representation) due to the flexibility they offer. This review presents a unifying analysis of methods focused on transferring knowledge across different domains. Through a taxonomy based on a transfer-approach categorization, and a characterization of works based on their data-assumption requirements, the objectives of this article are to 1) provide a comprehensive and systematic revision of knowledge transfer methods for the cross-domain RL setting, 2) categorize and characterize these methods to provide an analysis based on relevant features such as their transfer approach and data requirements, and 3) discuss the main challenges regarding cross-domain knowledge transfer, as well as ideas of future directions worth exploring to address these problems.

The fields of effective resistance and optimal transport on graphs are filled with rich connections to combinatorics, geometry, machine learning, and beyond. In this article we put forth a bold claim: that the two fields should be understood as one and the same, up to a choice of $p$. We make this claim precise by introducing the parameterized family of $p$-Beckmann distances for probability measures on graphs and relate them sharply to certain Wasserstein distances. Then, we break open a suite of results including explicit connections to optimal stopping times and random walks on graphs, graph Sobolev spaces, and a Benamou-Brenier type formula for $2$-Beckmann distance. We further explore empirical implications in the world of unsupervised learning for graph data and propose further study of the usage of these metrics where Wasserstein distance may produce computational bottlenecks.

Conversational tutoring systems (CTSs) offer learning experiences through interactions based on natural language. They are recognized for promoting cognitive engagement and improving learning outcomes, especially in reasoning tasks. Nonetheless, the cost associated with authoring CTS content is a major obstacle to widespread adoption and to research on effective instructional design. In this paper, we discuss and evaluate a novel type of CTS that leverages recent advances in large language models (LLMs) in two ways: First, the system enables AI-assisted content authoring by inducing an easily editable tutoring script automatically from a lesson text. Second, the system automates the script orchestration in a learning-by-teaching format via two LLM-based agents (Ruffle&Riley) acting as a student and a professor. The system allows for free-form conversations that follow the ITS-typical inner and outer loop structure. We evaluate Ruffle&Riley's ability to support biology lessons in two between-subject online user studies (N = 200) comparing the system to simpler QA chatbots and reading activity. Analyzing system usage patterns, pre/post-test scores and user experience surveys, we find that Ruffle&Riley users report high levels of engagement, understanding and perceive the offered support as helpful. Even though Ruffle&Riley users require more time to complete the activity, we did not find significant differences in short-term learning gains over the reading activity. Our system architecture and user study provide various insights for designers of future CTSs. We further open-source our system to support ongoing research on effective instructional design of LLM-based learning technologies.

Machines that can replicate human intelligence with type 2 reasoning capabilities should be able to reason at multiple levels of spatio-temporal abstractions and scales using internal world models. Devising formalisms to develop such internal world models, which accurately reflect the causal hierarchies inherent in the dynamics of the real world, is a critical research challenge in the domains of artificial intelligence and machine learning. This thesis identifies several limitations with the prevalent use of state space models (SSMs) as internal world models and propose two new probabilistic formalisms namely Hidden-Parameter SSMs and Multi-Time Scale SSMs to address these drawbacks. The structure of graphical models in both formalisms facilitates scalable exact probabilistic inference using belief propagation, as well as end-to-end learning via backpropagation through time. This approach permits the development of scalable, adaptive hierarchical world models capable of representing nonstationary dynamics across multiple temporal abstractions and scales. Moreover, these probabilistic formalisms integrate the concept of uncertainty in world states, thus improving the system's capacity to emulate the stochastic nature of the real world and quantify the confidence in its predictions. The thesis also discuss how these formalisms are in line with related neuroscience literature on Bayesian brain hypothesis and predicitive processing. Our experiments on various real and simulated robots demonstrate that our formalisms can match and in many cases exceed the performance of contemporary transformer variants in making long-range future predictions. We conclude the thesis by reflecting on the limitations of our current models and suggesting directions for future research.

Furniture assembly remains an unsolved problem in robotic manipulation due to its long task horizon and nongeneralizable operations plan. This paper presents the Tactile Ensemble Skill Transfer (TEST) framework, a pioneering offline reinforcement learning (RL) approach that incorporates tactile feedback in the control loop. TEST's core design is to learn a skill transition model for high-level planning, along with a set of adaptive intra-skill goal-reaching policies. Such design aims to solve the robotic furniture assembly problem in a more generalizable way, facilitating seamless chaining of skills for this long-horizon task. We first sample demonstration from a set of heuristic policies and trajectories consisting of a set of randomized sub-skill segments, enabling the acquisition of rich robot trajectories that capture skill stages, robot states, visual indicators, and crucially, tactile signals. Leveraging these trajectories, our offline RL method discerns skill termination conditions and coordinates skill transitions. Our evaluations highlight the proficiency of TEST on the in-distribution furniture assemblies, its adaptability to unseen furniture configurations, and its robustness against visual disturbances. Ablation studies further accentuate the pivotal role of two algorithmic components: the skill transition model and tactile ensemble policies. Results indicate that TEST can achieve a success rate of 90\% and is over 4 times more efficient than the heuristic policy in both in-distribution and generalization settings, suggesting a scalable skill transfer approach for contact-rich manipulation.

Large Language Models (LLMs) like GPT and Bard are capable of producing code based on textual descriptions, with remarkable efficacy. Such technology will have profound implications for computing education, raising concerns about cheating, excessive dependence, and a decline in computational thinking skills, among others. There has been extensive research on how teachers should handle this challenge but it is also important to understand how students feel about this paradigm shift. In this research, 52 first-year CS students were surveyed in order to assess their views on technologies with code-generation capabilities, both from academic and professional perspectives. Our findings indicate that while students generally favor the academic use of GPT, they don't over rely on it, only mildly asking for its help. Although most students benefit from GPT, some struggle to use it effectively, urging the need for specific GPT training. Opinions on GPT's impact on their professional lives vary, but there is a consensus on its importance in academic practice.