Extractive summarization of long documents is bottlenecked by quadratic complexity, often forcing truncation and limiting deployment in resource-constrained settings. We introduce the first Mamba-Transformer hybrid for extractive summarization, combining the semantic strength of pre-trained transformers with the linear-time processing of state space models. Leveraging Mamba's ability to process full documents without truncation, our approach preserves context while maintaining strong summarization quality. The architecture includes: (1) a transformer encoder for sentence-level semantics, (2) a Mamba state space model to capture inter-sentence dependencies efficiently, and (3) a linear classifier for sentence relevance prediction. Across news, argumentative, and scientific domains under low-resource conditions, our method achieves: (1) large gains over BERTSUM and MATCHSUM, including +0.23 ROUGE-1 on ArXiv and statistically significant improvements on all datasets (p < 0.001); (2) consistent advantages across domains, strongest on the longest documents; (3) robust performance with limited training data; and (4) 24-27

Assessing self-explanations of code is a critical task in educational Natural Language Processing (NLP), essential for providing automated feedback in programming education. While recent studies demonstrate that LLMs exhibit analogical reasoning capabilities, their application to educational code explanation assessment remains underexplored. To address this gap, we propose a novel three-stage approach: (1) prompting the LLM to extract Open Information Extraction (OIE) units from student and expert explanations, (2) prompting the LLM to construct semantic graphs from these units, and (3) employing LLM-based analogical reasoning to assess explanation similarity. We evaluate our approach on the Self-code corpus using Pearson and Spearman correlations. Our method achieves correlations of 0.8 and 0.76, respectively, significantly outperforming supervised models like BERT (0.74/0.75) and unsupervised approaches like text-embedding-ada-002 (0.67/0.64). These results demonstrate the effectiveness of structured semantic representation combined with analogical reasoning for educational assessment tasks.

The Automated Essay Scoring (AES) task is an important NLP research problem given its significance for the education ecosystem. Recently, researchers started to apply a hybrid approach to this task. This hybrid approach incorporates into a deep learning model expert features that assess a particular dimension of the essay. Motivated by these successes, we propose to automatically assess essays using a hybrid approach that relies on external discourse knowledge. Our proposed model consists of using transformer-based embeddings to generate semantic representations of essays. Then, we incorporate several discourse features into these representations. Finally, we apply a linear classifier to generate the final score. To evaluate the effectiveness of this approach, we have conducted extensive experiments using the Automated Student Assessment Prize dataset (ASAP). The performance of the proposed model has been evaluated using the Quadratic Weighted Kappa (QWK) metric. The experimental results demonstrate the effectiveness of this approach in comparison with several existing solutions in literature.

We present in this paper the results of a randomized control trial experiment that compared the effectiveness of two instructional strategies that scaffold learners' code comprehension processes: eliciting Free Self-Explanation and a Socratic Method. Code comprehension, i.e., understanding source code, is a critical skill for both learners and professionals. Improving learners' code comprehension skills should result in improved learning which in turn should help with retention in intro-to-programming courses which are notorious for suffering from very high attrition rates due to the complexity of programming topics. To this end, the reported experiment is meant to explore the effectiveness of various strategies to elicit self-explanation as a way to improve comprehension and learning during complex code comprehension and learning activities in intro-to-programming courses. The experiment showed pre-/post-test learning gains of 30% (M = 0.30, SD = 0.47) for the Free Self-Explanation condition and learning gains of 59% (M = 0.59,SD = 0.39) for the Socratic method. Furthermore, we investigated the behavior of the two strategies as a function of students' prior knowledge which was measured using learners' pretest score. For the Free Self-Explanation condition, there was no significant difference in mean learning gains for low vs. high knowledge students. The magnitude of the difference in performance (mean difference= 0.02,95% CI: -0.34 to 0.39) was very small (eta squared = 0.006). Likewise, the Socratic method showed no significant difference in mean learning gains between low vs. high performing students. The magnitude of the performance difference (mean difference =-0.24,95% CI: -0.534 to 0.03) was large (eta squared = 0.10). These findings suggest that eliciting self-explanations can be used as an effective strategy and that guided self-explanations as in the Socratic method condition is more effective at inducing learning gains.

The transfer learning pretraining-finetuning paradigm has revolutionized the natural language processing field yielding state-of the art results in several subfields such as text classification and question answering. However, little work has been done investigating pretrained language models for the open student answer assessment task. In this paper, we fine tune pretrained T5, BERT, RoBERTa, DistilBERT, ALBERT and XLNet models on the DT-Grade dataset which contains freely generated (or open) student answers together with judgment of their correctness. The experimental results demonstrated the effectiveness of these models based on the transfer learning pretraining-finetuning paradigm for open student answer assessment. An improvement of 8%-15% in accuracy was obtained over previous methods. Particularly, a T5 based method led to state-of-the-art results with an accuracy and F1 score of 0.88.

This paper reports the findings of an empirical study on the effects and nature of self explanation during source code comprehension learning activities in the context of learning computer programming language Java. Our study shows that self explanation helps learning and there is a strong posi- tive correlation between the volume of self-explanation students produce and how much they learn. Furthermore, self- explanations as an instructional strategy has no discrepancy based on student’s prior knowledge. We found that partici- pants explain target code examples using a combination of language, code references, and mathematical expressions. This is not surprising given the nature of the target item, a computer program, but this indicates that automatically eval- uating such self-explanations may require novel techniques compared to self-explanations of narrative or scientific texts.

Assessing the correctness of student answers in a dialog-based intelligent tutoring system (ITS) is a well-defined Natural Language Processing (NLP) task that has attracted the attention of many researchers in the field. Inspired by Vaswani’s transformer, we propose in this paper an attention-based transformer neural network with a multi-head attention mechanism for the task of student answer assessment. Results show the competitiveness of our proposed model. A highest accuracy of 71.5% was achieved when using ELMo embeddings, 10 heads of attention, and 2 layers. This is very competitive and rivals the highest accuracy achieved by a previously proposed BI-GRU-Capsnet deep network (72.5%) on the same dataset. The main advantages of using transformers over BI-GRU-Capsnet is reducing the training time and giving more space for parallelization.

Graph Convolutional Networks have achieved impressive results in multiple NLP tasks such as text classification. However, this approach has not been explored yet for the student answer assessment task. In this work, we propose to use Graph Convolutional Networks to automatically assess freely generated student answers within the context of dialogue-based intelligent tutoring systems. We convert this task to a node classification task. First, we build a DTGrade graph where each node represents the concatenation of the student answer and its corresponding reference answer whereas the edges represent the relatedness between nodes. Second, the DTGrade graph is fed to two layers of Graph Convolutional Networks. Finally, the output of the second layer is fed to a softmax layer. The empirical results showed that our model reached the state-of-the-art results by obtaining an accuracy of 73%.

This paper initiates language in LIDA by using the Learning Intelligent Decision' Agent's (LIDA) perceptual learning mechanism to suggest how an infant vervet, Chlorocebus pygerthrus, learns the meanings of vervet monkey alarm calls. We consider a multiple meanings approach which includes a feeling-based meaning, an action-based meaning, and a referential meaning. Simulations first test the learning of the meanings of these alarm calls while the infant is physically attached to the mother. The second simulations study the infant's understanding of these alarm calls while the infant is detached physically from the mother. Our results confirm that a LIDA based agent simulating a vervet infant is capable of learning such multiple meanings. The agent learned in sequence the feeling-based meaning, the action-based meaning, and the referential meaning. The LIDA agent achieved a good understanding of each of these meanings. This work can be seen as a starting step toward modeling the learning of human language in the LIDA cognitive architecture. Such modeling of language use would be a significant addition to the LIDA model.