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Found 21 papers, 1 papers with code
Mastery Guided Non-parametric Clustering to Scale-up Strategy Prediction
Predicting the strategy (sequence of concepts) that a student is likely to use in problem-solving helps Adaptive Instructional Systems (AISs) better adapt themselves to different types of learners based on their learning abilities.
Automated Assessment of Students' Code Comprehension using LLMs
Assessing student's answers and in particular natural language answers is a crucial challenge in the field of education.
The Behavior of Large Language Models When Prompted to Generate Code Explanations
This paper systematically investigates the generation of code explanations by Large Language Models (LLMs) for code examples commonly encountered in introductory programming courses.
Scalable and Equitable Math Problem Solving Strategy Prediction in Big Educational Data
The strategy prediction model is trained on instances sampled from these clusters.
A Tutorial Markov Analysis of Effective Human Tutorial Sessions
This paper investigates what differentiates effective tutorial sessions from less effective sessions.
DT\_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output
We describe our system (DT Team) submitted at SemEval-2017 Task 1, Semantic Textual Similarity (STS) challenge for English (Track 5).
Joint Inference for Mode Identification in Tutorial Dialogues
Our results show that the joint inference system is far more effective than the pipeline system in mode detection, and improves over the performance of the pipeline system by about 6 points in F1 score.
DT-Neg: Tutorial Dialogues Annotated for Negation Scope and Focus in Context
Existing negation datasets have focused on non-dialogue texts such as literary texts where the scope and focus of negation is normally present within the same sentence where the negation is located and therefore are not the most appropriate to inform the development of negation handling algorithms for dialogue-based systems.
SemAligner: A Method and Tool for Aligning Chunks with Semantic Relation Types and Semantic Similarity Scores
This paper introduces a ruled-based method and software tool, called SemAligner, for aligning chunks across texts in a given pair of short English texts.
On Paraphrase Identification Corpora
We analyze in this paper a number of data sets proposed over the last decade or so for the task of paraphrase identification.
Latent Semantic Analysis Models on Wikipedia and TASA
This paper introduces a collection of freely available Latent Semantic Analysis models built on the entire English Wikipedia and the TASA corpus.
The DARE Corpus: A Resource for Anaphora Resolution in Dialogue Based Intelligent Tutoring Systems
To the best of our knowledge, no data set is currently available for pronoun resolution in dialogue based intelligent tutoring systems.
