LECTOR: Summarizing E-book Reading Content for Personalized Student Support

• URL: http://arxiv.org/abs/2505.07898v1
• Date: Mon, 12 May 2025 06:40:55 GMT
• Title: LECTOR: Summarizing E-book Reading Content for Personalized Student Support
• Abstract summary: This study proposes LECTOR, a model that summarizes information from reading content in a format that can be easily integrated with reading activity data.<n>Our first experiment compared LECTOR to representative Natural Language Processing (NLP) models in extracting key information from 2,255 lecture slides.<n>Our second experiment compared reading preferences extracted by LECTOR with traditional reading activity data in predicting low-performing students.
• Score: 3.6908139322709466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Educational e-book platforms provide valuable information to teachers and researchers through two main sources: reading activity data and reading content data. While reading activity data is commonly used to analyze learning strategies and predict low-performing students, reading content data is often overlooked in these analyses. To address this gap, this study proposes LECTOR (Lecture slides and Topic Relationships), a model that summarizes information from reading content in a format that can be easily integrated with reading activity data. Our first experiment compared LECTOR to representative Natural Language Processing (NLP) models in extracting key information from 2,255 lecture slides, showing an average improvement of 5% in F1-score. These results were further validated through a human evaluation involving 28 students, which showed an average improvement of 21% in F1-score over a model predominantly used in current educational tools. Our second experiment compared reading preferences extracted by LECTOR with traditional reading activity data in predicting low-performing students using 600,712 logs from 218 students. The results showed a tendency to improve the predictive performance by integrating LECTOR. Finally, we proposed examples showing the potential application of the reading preferences extracted by LECTOR in designing personalized interventions for students.

Related papers

• Scalable Early Childhood Reading Performance Prediction [5.413138072912236]
  There are no suitable publicly available educational datasets for modeling and predicting future reading performance.<n>In this work, we introduce the Enhanced Core Reading Instruction ECRI dataset.<n>We leverage the dataset to empirically evaluate the ability of state-of-the-art machine learning models to recognize early childhood educational patterns.
  arXiv  Detail & Related papers  (2024-12-05T18:59:50Z)
• From Words to Worth: Newborn Article Impact Prediction with LLM [69.41680520058418]
  This paper introduces a promising approach, leveraging the capabilities of LLMs to predict the future impact of newborn articles.<n>The proposed method employs LLM to discern the shared semantic features of highly impactful papers from a large collection of title-abstract pairs.<n>The quantitative results, with an MAE of 0.216 and an NDCG@20 of 0.901, demonstrate that the proposed approach achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-08-07T17:52:02Z)
• Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs [70.15262704746378]
  We propose a systematically created human-annotated dataset consisting of coherent summaries for five publicly available datasets and natural language user feedback. Preliminary experiments with Falcon-40B and Llama-2-13B show significant performance improvements (10% Rouge-L) in terms of producing coherent summaries.
  arXiv  Detail & Related papers  (2024-07-05T20:25:04Z)
• LLM-Select: Feature Selection with Large Language Models [64.5099482021597]
  Large language models (LLMs) are capable of selecting the most predictive features, with performance rivaling the standard tools of data science.<n>Our findings suggest that LLMs may be useful not only for selecting the best features for training but also for deciding which features to collect in the first place.
  arXiv  Detail & Related papers  (2024-07-02T22:23:40Z)
• Enriched BERT Embeddings for Scholarly Publication Classification [0.13654846342364302]
  The NSLP 2024 FoRC Task I addresses this challenge organized as a competition. The goal is to develop a classifier capable of predicting one of 123 predefined classes from the Open Research Knowledge Graph (ORKG) taxonomy of research fields for a given article.
  arXiv  Detail & Related papers  (2024-05-07T09:05:20Z)
• Influence Scores at Scale for Efficient Language Data Sampling [3.072340427031969]
  "influence scores" are used to identify important subsets of data. In this paper, we explore the applicability of influence scores in language classification tasks.
  arXiv  Detail & Related papers  (2023-11-27T20:19:22Z)
• Modeling Student Performance in Game-Based Learning Environments [0.0]
  This study investigates game-based learning in the context of the educational game "Jo Wilder and the Capitol Case" The research aims to identify the features most predictive of student performance and correct question answering.
  arXiv  Detail & Related papers  (2023-09-23T16:53:07Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Responsible Active Learning via Human-in-the-loop Peer Study [88.01358655203441]
  We propose a responsible active learning method, namely Peer Study Learning (PSL), to simultaneously preserve data privacy and improve model stability. We first introduce a human-in-the-loop teacher-student architecture to isolate unlabelled data from the task learner (teacher) on the cloud-side. During training, the task learner instructs the light-weight active learner which then provides feedback on the active sampling criterion.
  arXiv  Detail & Related papers  (2022-11-24T13:18:27Z)
• A Large Scale Search Dataset for Unbiased Learning to Rank [51.97967284268577]
  We introduce the Baidu-ULTR dataset for unbiased learning to rank. It involves randomly sampled 1.2 billion searching sessions and 7,008 expert annotated queries. It provides: (1) the original semantic feature and a pre-trained language model for easy usage; (2) sufficient display information such as position, displayed height, and displayed abstract; and (3) rich user feedback on search result pages (SERPs) like dwelling time.
  arXiv  Detail & Related papers  (2022-07-07T02:37:25Z)
• Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments [21.12546768556595]
  VLE is a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures. Our experimental results indicate that the newly proposed VLE dataset leads to building context-agnostic engagement prediction models. Experiments in combining the built model with a personalising algorithm show promising improvements in addressing the cold-start problem encountered in educational recommenders.
  arXiv  Detail & Related papers  (2022-06-22T15:53:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.