Do We Fully Understand Students' Knowledge States? Identifying and Mitigating Answer Bias in Knowledge Tracing

Knowledge tracing (KT) aims to monitor students' evolving knowledge states through their learning interactions with concept-related questions, and can be indirectly evaluated by predicting how students will perform on future questions. In this paper, we observe that there is a common phenomenon of answer bias, i.e., a highly unbalanced distribution of correct and incorrect answers for each question. Existing models tend to memorize the answer bias as a shortcut for achieving high prediction performance in KT, thereby failing to fully understand students' knowledge states. To address this issue, we approach the KT task from a causality perspective. A causal graph of KT is first established, from which we identify that the impact of answer bias lies in the direct causal effect of questions on students' responses. A novel COunterfactual REasoning (CORE) framework for KT is further proposed, which separately captures the total causal effect and direct causal effect during training, and mitigates answer bias by subtracting the latter from the former in testing. The CORE framework is applicable to various existing KT models, and we implement it based on the prevailing DKT, DKVMN, and AKT models, respectively. Extensive experiments on three benchmark datasets demonstrate the effectiveness of CORE in making the debiased inference for KT.Comment: 13 page

An Empathy-Based Sandbox Approach to Bridge Attitudes, Goals, Knowledge, and Behaviors in the Privacy Paradox

The "privacy paradox" describes the discrepancy between users' privacy attitudes and their actual behaviors. Mitigating this discrepancy requires solutions that account for both system opaqueness and users' hesitations in testing different privacy settings due to fears of unintended data exposure. We introduce an empathy-based approach that allows users to experience how privacy behaviors may alter system outcomes in a risk-free sandbox environment from the perspective of artificially generated personas. To generate realistic personas, we introduce a novel pipeline that augments the outputs of large language models using few-shot learning, contextualization, and chain of thoughts. Our empirical studies demonstrated the adequate quality of generated personas and highlighted the changes in privacy-related applications (e.g., online advertising) caused by different personas. Furthermore, users demonstrated cognitive and emotional empathy towards the personas when interacting with our sandbox. We offered design implications for downstream applications in improving user privacy literacy and promoting behavior changes

Long-rising Type II Supernovae in the Zwicky Transient Facility Census of the Local Universe

SN 1987A was an unusual hydrogen-rich core-collapse supernova originating from a blue supergiant star. Similar blue supergiant explosions remain a small family of events, and are broadly characterized by their long rises to peak. The Zwicky Transient Facility (ZTF) Census of the Local Universe (CLU) experiment aims to construct a spectroscopically complete sample of transients occurring in galaxies from the CLU galaxy catalog. We identify 13 long-rising (>40 days) Type II supernovae from the volume-limited CLU experiment during a 3.5 year period from June 2018 to December 2021, approximately doubling the previously known number of these events. We present photometric and spectroscopic data of these 13 events, finding peak r-band absolute magnitudes ranging from -15.6 to -17.5 mag and the tentative detection of Ba II lines in 9 events. Using our CLU sample of events, we derive a long-rising Type II supernova rate of $1.37^{+0.26}_{-0.30}\times10^{-6}$ Mpc$^{-3}$ yr$^{-1}$, $\approx$1.4% of the total core-collapse supernova rate. This is the first volumetric rate of these events estimated from a large, systematic, volume-limited experiment.Comment: 32 pages, 17 figures, 5 tables. Submitted to Ap

Analysis and Experiment of a Bioinspired Multimode Octopod Robot

Abstract Legged robots use isolated footholds to support, which have the merit of good terrain trafficability but lack speed ability. In contrast, wheeled robots have the advantages of high speed and efficiency but only run on flat roads. To improve the moving speed and terrain adaptability of the legged robot, this paper proposes a bioinspired multimode octopod robot with rolling, walking, and obstacle-surmounting modes. First, inspired by the multimode locomotion of the Cebrennus rechenbergi spider, the high-speed mobility of the legged robot is realized in involute kick-rolling mode through the extendable appendages. Then, the foot and appendage trajectories are analyzed by kinematic method and optimized for walking stability. Based on the static and the kinematic analyses, the terrain adaptability is improved by adhesive obstacle-surmounting mode with the assistance of the appendages affiliated to the main feet. The deformable trunk with one DoF is designed to switch between three modes. Finally, a series of dynamic simulations and experiments are carried out to verify the theoretical analyses of the adhesive obstacle-surmounting mode and the mobility of the involute kick-rolling mode. It is shown that the multimode octopod robot can integrate the advantages of high speed and good terrain trafficability from different types of robots and is suitable for performing tasks in unstructured terrains