@inproceedings{hayat-etal-2025-three,
    title = "A Three-Tier {LLM} Framework for Forecasting Student Engagement from Qualitative Longitudinal Data",
    author = "Hayat, Ahatsham  and
      Martinez, Helen  and
      Khan, Bilal  and
      Hasan, Mohammad Rashedul",
    editor = "Boleda, Gemma  and
      Roth, Michael",
    booktitle = "Proceedings of the 29th Conference on Computational Natural Language Learning",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.conll-1.22/",
    pages = "334--347",
    ISBN = "979-8-89176-271-8",
    abstract = "Forecasting nuanced shifts in student engagement from longitudinal experiential (LE) data{---}multi-modal, qualitative trajectories of academic experiences over time{---}remains challenging due to high dimensionality and missingness. We propose a natural language processing (NLP)-driven framework using large language models (LLMs) to forecast binary engagement levels across four dimensions: Lecture Engagement Disposition, Academic Self-Efficacy, Performance Self-Evaluation, and Academic Identity and Value Perception. Evaluated on 960 trajectories from 96 first-year STEM students, our three-tier approach{---}LLM-informed imputation to generate textual descriptors for missing-not-at-random (MNAR) patterns, zero-shot feature selection via ensemble voting, and fine-tuned LLMs{---}processes textual non-cognitive responses. LLMs substantially outperform numeric baselines (e.g., Random Forest, LSTM) by capturing contextual nuances in student responses. Encoder-only LLMs surpass decoder-only variants, highlighting architectural strengths for sparse, qualitative LE data. Our framework advances NLP solutions for modeling student engagement from complex LE data, excelling where traditional methods struggle."
}