Machine Learning Approach for Predicting Students Academic Performance and Study Strategies based on their Motivation

• URL: http://arxiv.org/abs/2210.08186v1
• Date: Sat, 15 Oct 2022 04:09:05 GMT
• Title: Machine Learning Approach for Predicting Students Academic Performance and Study Strategies based on their Motivation
• Authors: Fidelia A. Orji and Julita Vassileva
• Abstract summary: This research aims to develop machine learning models for students academic performance and study strategies prediction. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) essential for students learning process were used in building the models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This research aims to develop machine learning models for students academic performance and study strategies prediction which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) essential for students learning process were used in building the models. Determining the broad effect of these attributes on students' academic performance and study strategy is the center of our interest. To investigate this, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Related papers

• Toward In-Context Teaching: Adapting Examples to Students' Misconceptions [54.82965010592045]
  We introduce a suite of models and evaluation methods we call AdapT. AToM is a new probabilistic model for adaptive teaching that jointly infers students' past beliefs and optimize for the correctness of future beliefs. Our results highlight both the difficulty of the adaptive teaching task and the potential of learned adaptive models for solving it.
  arXiv  Detail & Related papers  (2024-05-07T17:05:27Z)
• Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach [50.36650300087987]
  This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism. We have found that integrating the forgetting mechanisms significantly enhances the models' performance in acquiring new knowledge.
  arXiv  Detail & Related papers  (2024-03-27T05:10:38Z)
• A Predictive Model using Machine Learning Algorithm in Identifying Students Probability on Passing Semestral Course [0.0]
  This study employs classification for data mining techniques, and decision tree for algorithm. With the utilization of the newly discovered predictive model, the prediction of students probabilities to pass the current courses they take gives 0.7619 accuracy, 0.8333 precision, 0.8823 recall, and 0.8571 f1 score.
  arXiv  Detail & Related papers  (2023-04-12T01:57:08Z)
• Augmenting Interpretable Knowledge Tracing by Ability Attribute and Attention Mechanism [0.0]
  Knowledge tracing aims to model students' past answer sequences to track the change in their knowledge acquisition during exercise activities. Most existing approaches ignore the fact that students' abilities are constantly changing or vary between individuals. We propose a novel model based on ability attributes and attention mechanism.
  arXiv  Detail & Related papers  (2023-02-04T11:19:55Z)
• Predicting student performance using sequence classification with time-based windows [1.5836913530330787]
  We show that accurate predictive models can be built based on sequential patterns derived from students' behavioral data. We present a methodology for capturing temporal aspects in behavioral data and analyze its influence on the predictive performance of the models. The results of our improved sequence classification technique are capable of predicting student performance with high levels of accuracy, reaching 90 percent for course-specific models.
  arXiv  Detail & Related papers  (2022-08-16T13:46:39Z)
• Latent Properties of Lifelong Learning Systems [59.50307752165016]
  We introduce an algorithm-agnostic explainable surrogate-modeling approach to estimate latent properties of lifelong learning algorithms. We validate the approach for estimating these properties via experiments on synthetic data.
  arXiv  Detail & Related papers  (2022-07-28T20:58:13Z)
• An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs [67.23285413610243]
  Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models. We study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
  arXiv  Detail & Related papers  (2022-05-21T19:49:04Z)
• What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks? [59.51457877578138]
  We study contrastive learning on the wearable-based activity recognition task. This paper presents an open-source PyTorch library textttCL-HAR, which can serve as a practical tool for researchers.
  arXiv  Detail & Related papers  (2022-02-12T06:10:15Z)
• Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations [21.74631969428855]
  Interpretable Knowledge Tracing (IKT) is a simple model that relies on three meaningful latent features. IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes (TAN) IKT has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems.
  arXiv  Detail & Related papers  (2021-12-15T19:05:48Z)
• Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms [91.3755431537592]
  We analyze four broad meta-learning strategies which rely on plug-in estimation and pseudo-outcome regression. We highlight how this theoretical reasoning can be used to guide principled algorithm design and translate our analyses into practice.
  arXiv  Detail & Related papers  (2021-01-26T17:11:40Z)
• Do we need to go Deep? Knowledge Tracing with Big Data [5.218882272051637]
  We use EdNet, the largest student interaction dataset publicly available in the education domain, to understand how accurately both deep and traditional models predict future student performances. Our work observes that logistic regression models with carefully engineered features outperformed deep models from extensive experimentation.
  arXiv  Detail & Related papers  (2021-01-20T22:40:38Z)

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.