403 research outputs found
Students´ language in computer-assisted tutoring of mathematical proofs
Truth and proof are central to mathematics. Proving (or disproving) seemingly simple statements often turns out to be one of the hardest mathematical tasks. Yet, doing proofs is rarely taught in the classroom. Studies on cognitive difficulties in learning to do proofs have shown that pupils and students not only often do not understand or cannot apply basic formal reasoning techniques and do not know how to use formal mathematical language, but, at a far more fundamental level, they also do not understand what it means to prove a statement or even do not see the purpose of proof at all. Since insight into the importance of proof and doing proofs as such cannot be learnt other than by practice, learning support through individualised tutoring is in demand.
This volume presents a part of an interdisciplinary project, set at the intersection of pedagogical science, artificial intelligence, and (computational) linguistics, which investigated issues involved in provisioning computer-based tutoring of mathematical proofs through dialogue in natural language. The ultimate goal in this context, addressing the above-mentioned need for learning support, is to build intelligent automated tutoring systems for mathematical proofs. The research presented here has been focused on the language that students use while interacting with such a system: its linguistic propeties and computational modelling. Contribution is made at three levels: first, an analysis of language phenomena found in students´ input to a (simulated) proof tutoring system is conducted and the variety of students´ verbalisations is quantitatively assessed, second, a general computational processing strategy for informal mathematical language and methods of modelling prominent language phenomena are proposed, and third, the prospects for natural language as an input modality for proof tutoring systems is evaluated based on collected corpora
MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention
Problematic smartphone use negatively affects physical and mental health.
Despite the wide range of prior research, existing persuasive techniques are
not flexible enough to provide dynamic persuasion content based on users'
physical contexts and mental states. We first conduct a Wizard-of-Oz study
(N=12) and an interview study (N=10) to summarize the mental states behind
problematic smartphone use: boredom, stress, and inertia. This informs our
design of four persuasion strategies: understanding, comforting, evoking, and
scaffolding habits. We leverage large language models (LLMs) to enable the
automatic and dynamic generation of effective persuasion content. We develop
MindShift, a novel LLM-powered problematic smartphone use intervention
technique. MindShift takes users' in-the-moment physical contexts, mental
states, app usage behaviors, users' goals & habits as input, and generates
high-quality and flexible persuasive content with appropriate persuasion
strategies. We conduct a 5-week field experiment (N=25) to compare MindShift
with baseline techniques. The results show that MindShift significantly
improves intervention acceptance rates by 17.8-22.5% and reduces smartphone use
frequency by 12.1-14.4%. Moreover, users have a significant drop in smartphone
addiction scale scores and a rise in self-efficacy. Our study sheds light on
the potential of leveraging LLMs for context-aware persuasion in other behavior
change domains
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