arXiv subjects

Xiaoyi Tian

Publications and source records attributed to Xiaoyi Tian.

26 records · Page 2Linked to original sources

arXiv2025

Combining Log Data and Collaborative Dialogue Features to Predict Project Quality in Middle School AI Education

Project-based learning plays a crucial role in computing education. However, its open-ended nature makes tracking project development and assessing success challenging. We investigate how dialogue and system interaction logs predict project quality during collaborative, project-based AI learning of 94 middle school students working in pairs. We used linguistic features from dialogue transcripts and behavioral features from system logs to predict three project quality outcomes: productivity (number of training phrases), content richness (word density), and lexical variation (word diversity) of chatbot training phrases. We compared the predictive accuracy of each modality and a fusion of the modalities. Results indicate log data better predicts productivity, while dialogue data is more effective for content richness. Both modalities modestly predict lexical variation. Multimodal fusion improved predictions for productivity and lexical variation of training phrases but not content richness. These findings suggest that the value of multimodal fusion depends on the specific learning outcome. The study contributes to multimodal learning analytics by demonstrating the nuanced interplay between behavioral and linguistic data in assessing student learning progress in open-ended AI learning environments.

cs.HC↗

arXiv2025

Investigating the Impact and Student Perceptions of Guided Parsons Problems for Learning Logic with Subgoals

Parsons problems (PPs) have shown promise in structured problem solving by providing scaffolding that decomposes the problem and requires learners to reconstruct the solution. However, some students face difficulties when first learning with PPs or solving more complex Parsons problems. This study introduces Guided Parsons problems (GPPs) designed to provide step-specific hints and improve learning outcomes in an intelligent logic tutor. In a controlled experiment with 76 participants, GPP students achieved significantly higher accuracy of rule application in both level-end tests and post-tests, with the strongest gains among students with lower prior knowledge. GPP students initially spent more time in training (1.52 vs. 0.81 hours) but required less time for post-tests, indicating improved problem solving efficiency. Our thematic analysis of GPP student self-explanations revealed task decomposition, better rule understanding, and reduced difficulty as key themes, while some students felt the structured nature of GPPs restricted their own way of reasoning. These findings reinforce that GPPs can effectively combine the benefits of worked examples and problem solving practice, but could be further improved by individual adaptation.

cs.HC↗

arXiv2024

Some aspects of semi-harmonious quasi-projection pairs

A term called the quasi-projection pair $(P,Q)$ was introduced recently by the authors, where $P$ is a projection and $Q$ is an idempotent on a Hilbert $C^*$-module $H$ satisfying $Q^*=(2P-I)Q(2P-I)$, in which $Q^*$ is the adjoint operator of the idempotent $Q$ and $I$ is the identity operator on $H$. Some fundamental issues on quasi-projection pairs, such as the block matrix representations for quasi-projection pairs and the $C^*$-morphisms associated with quasi-projection pairs, are worthwhile to be investigated. This paper aims to make some preparations. One object called the semi-harmonious quasi-projection pair is introduced in the general setting of the adjointable operators on Hilbert $C^*$-modules. Some related operator theories on the common similarity of operators and a norm equation associated with the Friedrichs angle are dealt with.

math.FA↗

arXiv2024

The generalized polar decomposition, the weak complementarity and the parallel sum for adjointable operators on Hilbert $C^*$-modules

This paper deals mainly with some aspects of the adjointable operators on Hilbert $C^*$-modules. A new tool called the generalized polar decomposition for each adjointable operator is introduced and clarified. As an application, the general theory of the weakly complementable operators is set up in the framework of Hilbert $C^*$-modules. It is proved that there exists an operator equation which has a unique solution, whereas this unique solution fails to be the reduced solution. Some investigations are also carried out in the Hilbert space case. It is proved that there exist a closed subspace $M$ of certain Hilbert space $K$ and an operator $T\in \mathbb{B}(K)$ such that $T$ is $(M,M)$-weakly complementable, whereas $T$ fails to be $(M,M)$-complementable. The solvability of the equation $$A:B=X^*AX+(I-X)^*B(I-X) \quad (X\in\mathbb{B}(H))$$ is also dealt with in the Hilbert space case, where $A,B\in \mathbb{B}(H)$ are two general positive operators, and $A:B$ denotes their parallel sum. Among other things, it is shown that there exist certain positive operators $A$ and $B$ on the Hilbert space $\ell^2(\mathbb{N})\oplus \ell^2(\mathbb{N})$ such that the above equation has no solution.

math.FA↗

arXiv2024

Can Similarity-Based Domain-Ordering Reduce Catastrophic Forgetting for Intent Recognition?

Task-oriented dialogue systems are expected to handle a constantly expanding set of intents and domains even after they have been deployed to support more and more functionalities. To live up to this expectation, it becomes critical to mitigate the catastrophic forgetting problem (CF) that occurs in continual learning (CL) settings for a task such as intent recognition. While existing dialogue systems research has explored replay-based and regularization-based methods to this end, the effect of domain ordering on the CL performance of intent recognition models remains unexplored. If understood well, domain ordering has the potential to be an orthogonal technique that can be leveraged alongside existing techniques such as experience replay. Our work fills this gap by comparing the impact of three domain-ordering strategies (min-sum path, max-sum path, random) on the CL performance of a generative intent recognition model. Our findings reveal that the min-sum path strategy outperforms the others in reducing catastrophic forgetting when training on the 220M T5-Base model. However, this advantage diminishes with the larger 770M T5-Large model. These results underscores the potential of domain ordering as a complementary strategy for mitigating catastrophic forgetting in continually learning intent recognition models, particularly in resource-constrained scenarios.

cs.CL↗

arXiv2023

The Frobenious distances from projections to an idempotent matrix

For each pair of matrices $A$ and $B$ with the same order, let $\|A-B\|_F$ denote their Frobenius distance. This paper deals mainly with the Frobenius distances from projections to an idempotent matrix. For every idempotent $Q\in \mathbb{C}^{n\times n}$, a projection $m(Q)$ called the matched projection can be induced. It is proved that $m(Q)$ is the unique projection whose Frobenius distance away from $Q$ takes the minimum value among all the Frobenius distances from projections to $Q$, while $I_n-m(Q)$ is the unique projection whose Frobenius distance away from $Q$ takes the maximum value. Furthermore, it is proved that for every number $α$ between the minimum value and the maximum value, there exists a projection $P$ whose Frobenius distance away from $Q$ takes the value $α$. Based on the above characterization of the minimum distance, some Frobenius norm upper bounds and lower bounds of $\|P-Q\|_F$ are derived under the condition of $PQ=Q$ on a projection $P$ and an idempotent $Q$.

math.FA↗

arXiv2023

A Review of Digital Learning Environments for Teaching Natural Language Processing in K-12 Education

Natural Language Processing (NLP) plays a significant role in our daily lives and has become an essential part of Artificial Intelligence (AI) education in K-12. As children grow up with NLP-powered applications, it is crucial to introduce NLP concepts to them, fostering their understanding of language processing, language generation, and ethical implications of AI and NLP. This paper presents a comprehensive review of digital learning environments for teaching NLP in K-12. Specifically, it explores existing digital learning tools, discusses how they support specific NLP tasks and procedures, and investigates their explainability and evaluation results in educational contexts. By examining the strengths and limitations of these tools, this literature review sheds light on the current state of NLP learning tools in K-12 education. It aims to guide future research efforts to refine existing tools, develop new ones, and explore more effective and inclusive strategies for integrating NLP into K-12 educational contexts.

cs.CL↗

arXiv2022

Dominance as an Indicator of Rapport and Learning in Human-Agent Communication

Power dynamics in human-human communication can impact rapport-building and learning gains, but little is known about how power impacts human-agent communication. In this paper, we examine dominance behavior in utterances between middle-school students and a teachable robot as they work through math problems, as coded by Rogers and Farace's Relational Communication Control Coding Scheme (RCCCS). We hypothesize that relatively dominant students will show increased learning gains, as will students with greater dominance agreement with the robot. We also hypothesize that gender could be an indicator of difference in dominance behavior. We present a preliminary analysis of dominance characteristics in some of the transactions between robot and student. Ultimately, we hope to determine if manipulating the dominance behavior of a learning robot could support learning.

cs.HC↗