Learner to learner fuzzy profiles similarity using a hybrid interaction
analysis grid
- URL: http://arxiv.org/abs/2110.00247v1
- Date: Fri, 1 Oct 2021 08:01:41 GMT
- Title: Learner to learner fuzzy profiles similarity using a hybrid interaction
analysis grid
- Authors: Chabane Khentout, Khadidja Harbouche, Mahieddine Djoudi (TECHN\'E - EA
6316)
- Abstract summary: The paper aims to establish a suitable environment of interaction and collaboration among learners by using the speech acts via a semi structured synchronous communication tool.
By applying the fuzzy logic, we formalize human reasoning and, thus, giving very appreciable flexibility to the reasoning that use it.
The educational data mining techniques are used to optimize the mapping of behaviors to learner's profile, with similarity-based clustering, using Eros and PCA measures.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The analysis of remote discussions is not yet at the same level as the
face-to-face ones. The present paper aspires twofold. On the one hand, it
attempts to establish a suitable environment of interaction and collaboration
among learners by using the speech acts via a semi structured synchronous
communication tool. On the other, it aims to define behavioral profiles and
interpersonal skills hybrid grid by matching the BALES' IPA and PLETY's
analysis system. By applying the fuzzy logic, we formalize human reasoning and,
thus, giving very appreciable flexibility to the reasoning that use it, which
makes it possible to take into account imprecisions and uncertainties. In
addition, the educational data mining techniques are used to optimize the
mapping of behaviors to learner's profile, with similarity-based clustering,
using Eros and PCA measures. In order to show the validity of our system, we
performed an experiment on real-world data. The results show, among others: (1)
the usefulness of fuzzy logic to properly translate the profile text
descriptions into a mathematical format, (2) an irregularity in the behavior of
the learners, (3) the correlation between the profiles, (4) the superiority of
Eros method to the PCA factor in precision.
Related papers
- CauSkelNet: Causal Representation Learning for Human Behaviour Analysis [6.880536510094897]
This study introduces a novel representation learning method based on causal inference to better understand human joint dynamics and complex behaviors.
Our approach advances human motion analysis and paves the way for more adaptive intelligent healthcare solutions.
arXiv Detail & Related papers (2024-09-23T21:38:49Z) - Interpretable Data Fusion for Distributed Learning: A Representative Approach via Gradient Matching [19.193379036629167]
This paper introduces a representative-based approach for distributed learning that transforms multiple raw data points into a virtual representation.
It achieves this by condensing extensive datasets into digestible formats, thus fostering intuitive human-machine interactions.
arXiv Detail & Related papers (2024-05-06T18:21:41Z) - RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning.
Our proposed method uses reinforcement learning with user intervention signals themselves as rewards.
This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
arXiv Detail & Related papers (2023-11-21T21:05:21Z) - Improving (Dis)agreement Detection with Inductive Social Relation
Information From Comment-Reply Interactions [49.305189190372765]
Social relation information can play an assistant role in the (dis)agreement task besides textual information.
We propose a novel method to extract such relation information from (dis)agreement data into an inductive social relation graph.
We find social relations can boost the performance of the (dis)agreement detection model, especially for the long-token comment-reply pairs.
arXiv Detail & Related papers (2023-02-08T09:09:47Z) - Semantic Interactive Learning for Text Classification: A Constructive
Approach for Contextual Interactions [0.0]
We propose a novel interaction framework called Semantic Interactive Learning for the text domain.
We frame the problem of incorporating constructive and contextual feedback into the learner as a task to find an architecture that enables more semantic alignment between humans and machines.
We introduce a technique called SemanticPush that is effective for translating conceptual corrections of humans to non-extrapolating training examples.
arXiv Detail & Related papers (2022-09-07T08:13:45Z) - RLIP: Relational Language-Image Pre-training for Human-Object
Interaction Detection [32.20132357830726]
Language-Image Pre-training (LIPR) is a strategy for contrastive pre-training that leverages both entity and relation descriptions.
We show the benefits of these contributions, collectively termed RLIP-ParSe, for improved zero-shot, few-shot and fine-tuning HOI detection as well as increased robustness from noisy annotations.
arXiv Detail & Related papers (2022-09-05T07:50:54Z) - Active Learning of Ordinal Embeddings: A User Study on Football Data [4.856635699699126]
Humans innately measure distance between instances in an unlabeled dataset using an unknown similarity function.
This work uses deep metric learning to learn these user-defined similarity functions from few annotations for a large football trajectory dataset.
arXiv Detail & Related papers (2022-07-26T07:55:23Z) - DRFLM: Distributionally Robust Federated Learning with Inter-client
Noise via Local Mixup [58.894901088797376]
federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data.
We propose a general framework to solve the above two challenges simultaneously.
We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
arXiv Detail & Related papers (2022-04-16T08:08:29Z) - Symbiotic Adversarial Learning for Attribute-based Person Search [86.7506832053208]
We present a symbiotic adversarial learning framework, called SAL.Two GANs sit at the base of the framework in a symbiotic learning scheme.
Specifically, two different types of generative adversarial networks learn collaboratively throughout the training process.
arXiv Detail & Related papers (2020-07-19T07:24:45Z) - Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
We present an in-depth analysis of existing deep learning-based methods for modelling social interactions.
We propose two knowledge-based data-driven methods to effectively capture these social interactions.
We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
arXiv Detail & Related papers (2020-07-07T17:19:56Z) - Temporal Embeddings and Transformer Models for Narrative Text
Understanding [72.88083067388155]
We present two approaches to narrative text understanding for character relationship modelling.
The temporal evolution of these relations is described by dynamic word embeddings, that are designed to learn semantic changes over time.
A supervised learning approach based on the state-of-the-art transformer model BERT is used instead to detect static relations between characters.
arXiv Detail & Related papers (2020-03-19T14:23:12Z)
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.