KnowVis is a framework that converts linear video lectures into knowledge‑centric visual summaries. It first builds a detailed concept map from multimodal video content to identify key and challenging concepts, then organizes these into structured knowledge units before synthesizing engaging visual narratives. The authors also provide a dataset of 125 educational videos with 1,079 visual summaries and show through automated metrics and a human study that KnowVis outperforms existing methods in accuracy, clarity, and learning outcomes.
arXiv:2505.13520v2 Announce Type: replace-cross
Abstract: Textbook question answering (TQA) is a complex task, requiring the interpretation of complex multimodal context. Although recent advances hav...
By Hessa Alawwad, Usman Naseem, Areej Alhothali, Ali Alkhathlan, Amani Jamal
KnowVis is a framework that converts linear video lectures into knowledge‑centric visual narratives. It first extracts a detailed concept map from multimodal video content to identify key and challenging concepts, then builds structured knowledge units and synthesizes engaging visual summaries. The authors also provide a curated dataset of 125 educational videos across 10 disciplines, paired with 1,079 visual summaries, and show through automated evaluations and a human study that KnowVis produces more accurate, clear visuals that reduce cognitive load and improve learning effectiveness and knowledge retention.
By Yi Xu, Yifan Hou, Xiaoyu Zhang
arXiv:2604. 01280v2 Announce Type: replace-cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires Multimodal Large Language Models (MLLMs) to identify and combine fine-grained visual cues with retrieved textual evidence.
By Marco Morini, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
arXiv:2601. 02580v2 Announce Type: replace-cross Abstract: Traditional methods for determining assessment item parameters, such as difficulty and discrimination, rely heavily on expensive field testing to collect student performance data for Item Response Theory (IRT) calibration.
By Christopher Ormerod
arXiv:2607. 25294v1 Announce Type: cross Abstract: Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained knowledge.
By Lai Wei, Chengqi Li, Jiapeng Li, Ruina Hu, Yue Wang, Weiran Huang
The paper introduces Practical Integrated Cross-consistent Knowledge Tracing (PICKT), a model that incorporates multiple feature types to improve Knowledge Tracing robustness when new questions lack interaction history. It evaluates the impact of difficulty, textual, and knowledge‑map relational features, finding that difficulty is especially informative for hard questions, while fused text and map features help estimate unseen questions by leveraging similar ones seen during training. The study concludes that prioritizing feature annotation aligned with educational service characteristics is essential for maintaining robust diagnostics in Intelligent Tutoring Systems.
By Wonbeen Lee, Channyoung Lee, Junho Sohn, Hansam Cho
arXiv:2609.13158v1 Announce Type: new
Abstract: Large Vision--Language Models (LVLMs) are increasingly expected to perform visual question answering (VQA) over planar media. However, existing planar...
By Yongqi Yu, Yu Zhang
arXiv:2606. 08988v1 Announce Type: cross Abstract: Automatic Question Difficulty Estimation (AQDE) holds growing promise for educational assessment because it has the potential to yield difficulty estimates that are competitive with expert judgment, while helping reduce the time and financial burden associated with pilot administrations and scaling to digital testing contexts.
By Gabriel Ortega, Abelino Jim\'enez, S\'everin Lions, Pablo Dartnell
arXiv:2606. 13684v1 Announce Type: cross Abstract: Automatic Bloom's taxonomy classification of assessment questions can substantially reduce instructor workload, but labeling is subjective and teacher-dependent.
By Abdolali Faraji, Mohammadreza Molavi, Zohreh Rasoulkhani, Mohammadreza Tavakoli, G\'abor Kismih\'ok
arXiv:2608.30583v1 Announce Type: new
Abstract: Standard language proficiency tests rely on linguistic tasks such as vocabulary, grammar and reading comprehension quizzes. An alternative, cognitively...
By Shachar Frenkel, Ido Falah, Omer Shubi, Yevgeni Berzak
arXiv:2602. 00344v2 Announce Type: replace-cross Abstract: While Retrieval-Augmented Generation (RAG) is one of the dominant paradigms for enhancing Large Vision-Language Models (LVLMs) on knowledge-based VQA tasks, recent work attributes RAG failures to insufficient attention towards the retrieved context, proposing to reduce the attention allocated to image tokens.
By Beidi Zhao, Wenlong Deng, Xinting Liao, Yushu Li, Nazim Shaikh, Yao Nie, Xiaoxiao Li