We examine how strategies for representing student skill (𝜃) affect IRT-3PL item-parameter recovery from reconstructed item characteristic curves. We compare natural-language descriptors with signed decimal and scientific-notation anchors across anchor counts and placements. Our results show that dense numeric grids improve recovery, while non-uniform placements produce parameter-specific trade-offs, showing that 𝜃 representation matters.

We examine model design choices for encoder-based prediction of IRT-3PL item parameters in reading comprehension tasks with passage-based multiple-choice questions (MCQs). We compare three aspects: (1) encoding strategies for passage and question texts, including optional cross-attention, (2) per-parameter vs. joint-parameter prediction with MSE and CRPS losses, and (3) random vs. passage-grouped batching. Results show that separate-then-fuse encoding is substantially more efficient than concatenated-input encoding while maintaining comparable parameter recovery. Cross-attention improves recovery for discrimination and guessing but weakens difficulty recovery. Joint-parameter prediction achieves performance close to per-parameter prediction in the strongest configurations while reducing training time. CRPS is most useful for joint prediction, and passage-grouped batching consistently improves recovery of the guessing parameter. Overall, joint prediction with CRPS and passage-grouped batching provides the most balanced performance while maintaining the efficiency of separate-then-fuse encoding without cross-attention, showing that the proposed model design can support efficient IRT parameter prediction for passage-based MCQs.

When learning new vocabulary, both humans and machines acquire critical information about the meaning of an unfamiliar word through contextual information in a sentence or passage. However, not all contexts are equally helpful for learning an unfamiliar ‘target’ word. Some contexts provide a rich set of semantic clues to the target word’s meaning, while others are less supportive. We explore the task of finding educationally supportive contexts with respect to a given target word for vocabulary learning scenarios, particularly for improving student literacy skills. Because of their inherent context-based nature, attention-based deep learning methods provide an ideal starting point. We evaluate attention-based approaches for predicting the amount of educational support from contexts, ranging from a simple custom model using pre-trained embeddings with an additional attention layer, to a commercial Large Language Model (LLM). Using an existing major benchmark dataset for educational context support prediction, we found that a sophisticated but generic LLM had poor performance, while a simpler model using a custom attention-based approach achieved the best-known performance to date on this dataset.