Combining EEG and NLP Features for Predicting Students' Lecture Comprehension using Ensemble Classification

• URL: http://arxiv.org/abs/2311.11088v1
• Date: Sat, 18 Nov 2023 14:35:26 GMT
• Title: Combining EEG and NLP Features for Predicting Students' Lecture Comprehension using Ensemble Classification
• Authors: Phantharach Natnithikarat, Theerawit Wilaiprasitporn, Supavit Kongwudhikunakorn
• Abstract summary: The proposed framework includes EEG and NLP feature extraction, processing, and classification. EEG and NLP features are extracted to construct integrated features obtained from recorded EEG signals and sentence-level syntactic analysis. Experiment results show that this framework performs better than the baselines.
• Score: 0.7964328411060118
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electroencephalography (EEG) and Natural Language Processing (NLP) can be applied for education to measure students' comprehension in classroom lectures; currently, the two measures have been used separately. In this work, we propose a classification framework for predicting students' lecture comprehension in two tasks: (i) students' confusion after listening to the simulated lecture and (ii) the correctness of students' responses to the post-lecture assessment. The proposed framework includes EEG and NLP feature extraction, processing, and classification. EEG and NLP features are extracted to construct integrated features obtained from recorded EEG signals and sentence-level syntactic analysis, which provide information about specific biomarkers and sentence structures. An ensemble stacking classification method -- a combination of multiple individual models that produces an enhanced predictive model -- is studied to learn from the features to make predictions accurately. Furthermore, we also utilized subjective confusion ratings as another integrated feature to enhance classification performance. By doing so, experiment results show that this framework performs better than the baselines, which achieved F1 up to 0.65 for predicting confusion and 0.78 for predicting correctness, highlighting that utilizing this has helped improve the classification performance.

Related papers

• Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers [20.46918103875102]
  This study presents a systematic approach to enrich datasets with features derived from large language model embeddings. We assess the impact of RoBERTa and GPT-2 embeddings on ensemble classifiers, including Random Forest, XGBoost, and CatBoost. Results indicate that integrating embeddings with traditional numerical and categorical features often enhances predictive performance.
  arXiv  Detail & Related papers  (2024-11-03T17:45:00Z)
• Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton Sequence [56.092059713922744]
  We show that using high-level contextualized features as prediction targets can achieve superior performance. Specifically, we propose Skeleton2vec, a simple and efficient self-supervised 3D action representation learning framework. Our proposed Skeleton2vec outperforms previous methods and achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2024-01-01T12:08:35Z)
• A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training [4.722922834127293]
  This paper proposes a visual interpretation-based self-improving classification model with a combination of virtual adversarial training (VAT) and BERT models to address the problems. Specifically, a fine-tuned BERT model is used as a classifier to classify the sentiment of the text. The predicted sentiment classification labels are used as part of the input of another BERT for spam classification via a semi-supervised training manner.
  arXiv  Detail & Related papers  (2023-09-03T15:07:24Z)
• Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning [76.43827771613127]
  In this paper, we investigate task-specific preferences between pairs of input texts as a new alternative way for such auxiliary data annotation. We propose a novel multi-task learning framework, called prefer-to-classify (P2C), which can enjoy the cooperative effect of learning both the given classification task and the auxiliary preferences.
  arXiv  Detail & Related papers  (2023-06-08T04:04:47Z)
• Learning What Not to Segment: A New Perspective on Few-Shot Segmentation [63.910211095033596]
  Recently few-shot segmentation (FSS) has been extensively developed. This paper proposes a fresh and straightforward insight to alleviate the problem. In light of the unique nature of the proposed approach, we also extend it to a more realistic but challenging setting.
  arXiv  Detail & Related papers  (2022-03-15T03:08:27Z)
• Automated Speech Scoring System Under The Lens: Evaluating and interpreting the linguistic cues for language proficiency [26.70127591966917]
  We utilize classical machine learning models to formulate a speech scoring task as both a classification and a regression problem. First, we extract linguist features under five categories (fluency, pronunciation, content, grammar and vocabulary, and acoustic) and train models to grade responses. In comparison, we find that the regression-based models perform equivalent to or better than the classification approach.
  arXiv  Detail & Related papers  (2021-11-30T06:28:58Z)
• Diversity Enhanced Active Learning with Strictly Proper Scoring Rules [4.81450893955064]
  We study acquisition functions for active learning (AL) for text classification. We convert the Expected Loss Reduction (ELR) method to estimate the increase in (strictly proper) scores like log probability or negative mean square error. We show that the use of mean square error and log probability with BEMPS yields robust acquisition functions.
  arXiv  Detail & Related papers  (2021-10-27T05:02:11Z)
• Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing [72.4445825335561]
  We propose a simple method to derive 2D representation from detection scores produced by an arbitrary set of binary classifiers. Based upon rank correlations, our method facilitates a visual comparison of classifiers with arbitrary scores. While the approach is fully versatile and can be applied to any detection task, we demonstrate the method using scores produced by automatic speaker verification and voice anti-spoofing systems.
  arXiv  Detail & Related papers  (2021-06-11T13:03:33Z)
• Improving Deep Learning Sound Events Classifiers using Gram Matrix Feature-wise Correlations [1.2891210250935146]
  In our method, we analyse all the activations of a generic CNN in order to produce feature representations using Gram Matrices. The proposed approach can be applied to any CNN and our experimental evaluation of four different architectures on two datasets demonstrated that our method consistently improves the baseline models.
  arXiv  Detail & Related papers  (2021-02-23T16:08:02Z)
• Attention-based Neural Bag-of-Features Learning for Sequence Data [143.62294358378128]
  2D-Attention (2DA) is a generic attention formulation for sequence data. The proposed attention module is incorporated into the recently proposed Neural Bag of Feature (NBoF) model to enhance its learning capacity. Our empirical analysis shows that the proposed attention formulations can not only improve performances of NBoF models but also make them resilient to noisy data.
  arXiv  Detail & Related papers  (2020-05-25T17:51:54Z)
• Self-Supervised Tuning for Few-Shot Segmentation [82.32143982269892]
  Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples. Existing meta-learning method tends to fail in generating category-specifically discriminative descriptor when the visual features extracted from support images are marginalized in embedding space. This paper presents an adaptive framework tuning, in which the distribution of latent features across different episodes is dynamically adjusted based on a self-segmentation scheme.
  arXiv  Detail & Related papers  (2020-04-12T03:53:53Z)

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.