UniNet: Next Term Course Recommendation using Deep Learning

• URL: http://arxiv.org/abs/2009.09326v1
• Date: Sun, 20 Sep 2020 00:07:45 GMT
• Title: UniNet: Next Term Course Recommendation using Deep Learning
• Authors: Nicolas Araque, Germano Rojas, Maria Vitali
• Abstract summary: We propose a deep learning approach to represent how chronological order of course grades affects the probability of success. We have shown that it is possible to obtain a performance of 81.10% on AUC metric using only grade information. This is shown to be meaningful across different student GPA levels and course difficulties.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Course enrollment recommendation is a relevant task that helps university students decide what is the best combination of courses to enroll in the next term. In particular, recommender system techniques like matrix factorization and collaborative filtering have been developed to try to solve this problem. As these techniques fail to represent the time-dependent nature of academic performance datasets we propose a deep learning approach using recurrent neural networks that aims to better represent how chronological order of course grades affects the probability of success. We have shown that it is possible to obtain a performance of 81.10% on AUC metric using only grade information and that it is possible to develop a recommender system with academic student performance prediction. This is shown to be meaningful across different student GPA levels and course difficulties

Related papers

• CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence [55.21518669075263]
  CURE4Rec is the first comprehensive benchmark for recommendation unlearning evaluation. We consider the deeper influence of unlearning on recommendation fairness and robustness towards data with varying impact levels.
  arXiv  Detail & Related papers  (2024-08-26T16:21:50Z)
• Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization [0.0]
  This paper presents a hybrid RS that combines Collaborative Filtering (CF) and Content-based Filtering (CBF) A Genetic Algorithm (GA) has been developed to automatically discover the optimal RS configuration. Experimental results show a study of the most relevant criteria for the course recommendation.
  arXiv  Detail & Related papers  (2024-02-13T11:02:12Z)
• Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks [58.469818546042696]
  We study the sample efficiency of OPE with human preference and establish a statistical guarantee for it. By appropriately selecting the size of a ReLU network, we show that one can leverage any low-dimensional manifold structure in the Markov decision process.
  arXiv  Detail & Related papers  (2023-10-16T16:27:06Z)
• Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation [49.85548436111153]
  We propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC) SRC formulates the recommendation task under a set-to-sequence paradigm. We conduct extensive experiments on two real-world public datasets and one industrial dataset.
  arXiv  Detail & Related papers  (2023-06-07T08:24:44Z)
• An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering [79.17319280791237]
  We propose an adaptive graph pre-training framework for localized collaborative filtering (ADAPT) ADAPT captures both the common knowledge across different graphs and the uniqueness for each graph. It does not require transferring user/item embeddings, and is able to capture both the common knowledge across different graphs and the uniqueness for each graph.
  arXiv  Detail & Related papers  (2021-12-14T06:53:13Z)
• Tri-Branch Convolutional Neural Networks for Top-$k$ Focused Academic Performance Prediction [28.383922154797315]
  Academic performance prediction aims to leverage student-related information to predict their future academic outcomes. In this paper, we analyze students' daily behavior trajectories, which can be comprehensively tracked with campus smartcard records. We propose a novel Tri-Branch CNN architecture, which is equipped with row-wise, column-wise, and depth-wise convolution and attention operations.
  arXiv  Detail & Related papers  (2021-07-22T02:35:36Z)
• KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting [52.623349754076024]
  We provide an overview of the recommendation approaches integrated in KnowledgeCheckR. Examples thereof are utility-based recommendation that helps to identify learning contents to be repeated in the future, collaborative filtering approaches that help to implement session-based recommendation, and content-based recommendation that supports intelligent question answering.
  arXiv  Detail & Related papers  (2021-02-15T20:06:28Z)
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [104.87483578308526]
  We propose the model S3-Rec, which stands for Self-Supervised learning for Sequential Recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-18T11:44:10Z)
• Analyzing Student Strategies In Blended Courses Using Clickstream Data [32.81171098036632]
  We use pattern mining and models borrowed from Natural Language Processing to understand student interactions. Fine-grained clickstream data is collected through Diderot, a non-commercial educational support system. Our results suggest that the proposed hybrid NLP methods can provide valuable insights even in the low-data setting of blended courses.
  arXiv  Detail & Related papers  (2020-05-31T03:01:00Z)
• Context-aware Non-linear and Neural Attentive Knowledge-based Models for Grade Prediction [12.592903558338444]
  Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection. One of the successful approaches for accurately predicting a student's grades in future courses is Cumulative Knowledge-based Regression Models (CKRM) CKRM learns shallow linear models that predict a student's grades as the similarity between his/her knowledge state and the target course. We propose context-aware non-linear and neural attentive models that can potentially better estimate a student's knowledge state from his/her prior course information.
  arXiv  Detail & Related papers  (2020-03-09T20:20:48Z)
• Academic Performance Estimation with Attention-based Graph Convolutional Networks [17.985752744098267]
  Given a student's past data, the task of student's performance prediction is to predict a student's grades in future courses. Traditional methods for student's performance prediction usually neglect the underlying relationships between multiple courses. We propose a novel attention-based graph convolutional networks model for student's performance prediction.
  arXiv  Detail & Related papers  (2019-12-26T23:11:27Z)

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.