A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick
Nishant Balepur, Matthew Shu, Alexander Hoyle, Alison Robey, Shi Feng,, Seraphina Goldfarb-Tarrant, Jordan Boyd-Graber

TL;DR
This paper introduces SMART, a mnemonic generator trained on real student feedback, which improves mnemonic learning by aligning LLM outputs with student preferences and expert assessments, leading to more effective educational tools.
Contribution
The paper presents a novel approach to training mnemonic generators using student feedback and Bayesian models, enhancing LLM alignment for educational applications.
Findings
Expressed and observed preferences often disagree.
Bayesian models effectively synthesize multiple preference types.
SMART matches GPT-4 in effectiveness at lower costs.
Abstract
Keyword mnemonics are memorable explanations that link new terms to simpler keywords. Prior work generates mnemonics for students, but they do not train models using mnemonics students prefer and aid learning. We build SMART, a mnemonic generator trained on feedback from real students learning new terms. To train SMART, we first fine-tune LLaMA-2 on a curated set of user-written mnemonics. We then use LLM alignment to enhance SMART: we deploy mnemonics generated by SMART in a flashcard app to find preferences on mnemonics students favor. We gather 2684 preferences from 45 students across two types: expressed (inferred from ratings) and observed (inferred from student learning), yielding three key findings. First, expressed and observed preferences disagree; what students think is helpful does not always capture what is truly helpful. Second, Bayesian models can synthesize complementary…
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Code & Models
Videos
Taxonomy
TopicsEducation and Technology Integration · Experimental and Theoretical Physics Studies
MethodsAttention Is All You Need · Sparse Evolutionary Training · Softmax · Layer Normalization · Absolute Position Encodings · Byte Pair Encoding · Label Smoothing · Position-Wise Feed-Forward Layer · Dropout · Adam
