Multi-source and multimodal data fusion for predicting academic performance in blended learning university courses

• URL: http://arxiv.org/abs/2403.05552v1
• Date: Thu, 8 Feb 2024 21:29:41 GMT
• Title: Multi-source and multimodal data fusion for predicting academic performance in blended learning university courses
• Authors: W. Chango, R. Cerezo, C. Romero,
• Abstract summary: We collected and preprocessed data about first-year university students from different sources. We carried out experiments by applying four different data fusion approaches and six classification algorithms. The best prediction models showed us that the level of attention in theory classes, scores in Moodle quizzes, and the level of activity in Moodle forums were the best set of attributes for predicting students' final performance in our courses.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we applied data fusion approaches for predicting the final academic performance of university students using multiple-source, multimodal data from blended learning environments. We collected and preprocessed data about first-year university students from different sources: theory classes, practical sessions, on-line Moodle sessions, and a final exam. Our objective was to discover which data fusion approach produced the best results using our data. We carried out experiments by applying four different data fusion approaches and six classification algorithms. The results showed that the best predictions were produced using ensembles and selecting the best attributes approach with discretized data. The best prediction models showed us that the level of attention in theory classes, scores in Moodle quizzes, and the level of activity in Moodle forums were the best set of attributes for predicting students' final performance in our courses.

Related papers

• A CLIP-Powered Framework for Robust and Generalizable Data Selection [51.46695086779598]
  Real-world datasets often contain redundant and noisy data, imposing a negative impact on training efficiency and model performance. Data selection has shown promise in identifying the most representative samples from the entire dataset. We propose a novel CLIP-powered data selection framework that leverages multimodal information for more robust and generalizable sample selection.
  arXiv  Detail & Related papers  (2024-10-15T03:00:58Z)
• Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance [55.872926690722714]
  We study the predictability of model performance regarding the mixture proportions in function forms. We propose nested use of the scaling laws of training steps, model sizes, and our data mixing law. Our method effectively optimize the training mixture of a 1B model trained for 100B tokens in RedPajama.
  arXiv  Detail & Related papers  (2024-03-25T17:14:00Z)
• How to Train Data-Efficient LLMs [56.41105687693619]
  We study data-efficient approaches for pre-training language models (LLMs) We find that Ask-LLM and Density sampling are the best methods in their respective categories. In our comparison of 19 samplers, involving hundreds of evaluation tasks and pre-training runs, we find that Ask-LLM and Density are the best methods in their respective categories.
  arXiv  Detail & Related papers  (2024-02-15T02:27:57Z)
• Improving prediction of students' performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources [0.0]
  The aim of this study was to predict university students' learning performance using different sources of data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources.
  arXiv  Detail & Related papers  (2024-02-10T09:31:39Z)
• 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)
• On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training [72.8087629914444]
  We study the impact of the trade-off between the intra-class diversity (the number of samples per class) and the inter-class diversity (the number of classes) of a supervised pre-training dataset. With the size of the pre-training dataset fixed, the best downstream performance comes with a balance on the intra-/inter-class diversity.
  arXiv  Detail & Related papers  (2023-05-20T16:23:50Z)
• Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP [43.7219097444333]
  We introduce a testbed of six publicly available data sources to investigate how pre-training distributions induce robustness in CLIP. We find that the performance of the pre-training data varies substantially across distribution shifts. We find that combining multiple sources does not necessarily yield better models, but rather dilutes the robustness of the best individual data source.
  arXiv  Detail & Related papers  (2022-08-10T18:24:23Z)
• Optimal Ensemble Construction for Multi-Study Prediction with Applications to COVID-19 Excess Mortality Estimation [7.02598981483736]
  Multi-study ensembling uses a two-stage strategy which fits study-specific models and estimates ensemble weights separately. This approach ignores the ensemble properties at the model-fitting stage, potentially resulting in a loss of efficiency. We show that when little data is available for a country before the onset of the pandemic, leveraging data from other countries can substantially improve prediction accuracy.
  arXiv  Detail & Related papers  (2021-09-19T16:52:41Z)
• Online Active Model Selection for Pre-trained Classifiers [72.84853880948894]
  We design an online selective sampling approach that actively selects informative examples to label and outputs the best model with high probability at any round. Our algorithm can be used for online prediction tasks for both adversarial and streams.
  arXiv  Detail & Related papers  (2020-10-19T19:53:15Z)
• Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining [8.26773636337474]
  This work analyzes two different undergraduate datasets at two different universities. It aims to predict the students' performance at two stages of course delivery (20% and 50% respectively)
  arXiv  Detail & Related papers  (2020-06-09T03:22:33Z)
• Multi-Center Federated Learning [62.57229809407692]
  This paper proposes a novel multi-center aggregation mechanism for federated learning. It learns multiple global models from the non-IID user data and simultaneously derives the optimal matching between users and centers. Our experimental results on benchmark datasets show that our method outperforms several popular federated learning methods.
  arXiv  Detail & Related papers  (2020-05-03T09:14:31Z)

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.