Recommender system for predicting student performance

AbstractRecommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning tasks such as recommending resources (e.g. papers, books,..) to the learners (students). In this work, we propose a novel approach which uses recommender system techniques for educational data mining, especially for predicting student performance. To validate this approach, we compare recommender system techniques with traditional regression methods such as logistic/linear regression by using educational data for intelligent tutoring systems. Experimental results show that the proposed approach can improve prediction results

Building Student’s Study Path using Markov Chain Process with Apriori Cross Join Pearson Correlation

Student’s study path could be advised by using bestpossible path from Markov Chain rule based on student’sacademic performance records with several assumption on thecurrent curriculum. Finding the Markov’s rule is crucial processbecause it will determine study path’s scenarios which rely onstudent current performance to choose the next best possiblepath. The rule would be built using the whole student’s academicperformance on the same curriculum by implementing AprioriCross Join Pearson Correlation Test on two consecutivesemesters. It will then create path consist of paired courses A->B with Pearson value that would be implemented as rule in Markov Proces

An Intelligent Recommendation System to Evaluate Teaching Faculty Performance using Self Adaptive HMM and PSO

The addition of recommender systems has completely changed the landscape of digital marketing. The use of recommender systems in digital marketing, e-commerce, entertainment, and healthcare, among other industries, has greatly increased business. The right ideas have improved ease of usage and user experience as well. Nonetheless, there hasn't been much research done on the use of recommender systems in the field of education. This paper suggests a recommender system based on machine learning to provide a framework of suggestions for the teaching faculty based on different performance metrics. In terms of improving students' academic and research performance, it can have a significant impact on the education system as a whole. The accurate recommendation in this work has been achieved by the usage of self-adaptive HMM. Particle swarm optimization (PSO) has been used to optimize the tuning parameters in order to lower the model's temporal complexity. The recommendation in this work has been derived through the use of collaborative filtering. Through the experimental investigation, the suggested systems' performance was confirmed, and it was discovered that their accuracy was greater than 90%

MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education

Since the introduction of the original BERT (i.e., BASE BERT), researchers have developed various customized BERT models with improved performance for specific domains and tasks by exploiting the benefits of transfer learning. Due to the nature of mathematical texts, which often use domain specific vocabulary along with equations and math symbols, we posit that the development of a new BERT model for mathematics would be useful for many mathematical downstream tasks. In this resource paper, we introduce our multi-institutional effort (i.e., two learning platforms and three academic institutions in the US) toward this need: MathBERT, a model created by pre-training the BASE BERT model on a large mathematical corpus ranging from pre-kindergarten (pre-k), to high-school, to college graduate level mathematical content. In addition, we select three general NLP tasks that are often used in mathematics education: prediction of knowledge component, auto-grading open-ended Q&A, and knowledge tracing, to demonstrate the superiority of MathBERT over BASE BERT. Our experiments show that MathBERT outperforms prior best methods by 1.2-22% and BASE BERT by 2-8% on these tasks. In addition, we build a mathematics specific vocabulary 'mathVocab' to train with MathBERT. We discover that MathBERT pre-trained with 'mathVocab' outperforms MathBERT trained with the BASE BERT vocabulary (i.e., 'origVocab'). MathBERT is currently being adopted at the participated leaning platforms: Stride, Inc, a commercial educational resource provider, and ASSISTments.org, a free online educational platform. We release MathBERT for public usage at: https://github.com/tbs17/MathBERT.Comment: Accepted by NeurIPS 2021 MATHAI4ED Workshop (Best Paper