Recommender System for Analyzing Students’ Performance Using Data Mining Technique

Petwal, Shruti; John, K. Sunny; Vikas, Gorla; Rawat, Sandeep Singh

doi:10.1007/978-981-15-5309-7_19

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Given the reliance on data-driven approaches in academic advising, our study addresses the fair and effective handling of data imbalance, ensuring that algorithms do not introduce bias that could impact the prediction process. Petwal et al [40] demonstrated the effectiveness of K-means clustering in identifying areas requiring improvement. Table 1 offers a concise overview of several studies within the field, delineating their respective contributions, strengths, and weaknesses.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Academic Advising in Engineering Education with Machine Learning Using a Real-World Dataset

Maphosa,

Doorsamy,

Paul

2024

Algorithms

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The role of academic advising has been conducted by faculty-student advisors, who often have many students to advise quickly, making the process ineffective. The selection of the incorrect qualification increases the risk of dropping out, changing qualifications, or not finishing the qualification enrolled in the minimum time. This study harnesses a real-world dataset comprising student records across four engineering disciplines from the 2016 and 2017 academic years at a public South African university. The study examines the relative importance of features in models for predicting student performance and determining whether students are better suited for extended or mainstream programmes. The study employs a three-step methodology, encompassing data pre-processing, feature importance selection, and model training with evaluation, to predict student performance by addressing issues such as dataset imbalance, biases, and ethical considerations. By relying exclusively on high school performance data, predictions are based solely on students’ abilities, fostering fairness and minimising biases in predictive tasks. The results show that removing demographic features like ethnicity or nationality reduces bias. The study’s findings also highlight the significance of the following features: mathematics, physical sciences, and admission point scores when predicting student performance. The models are evaluated, demonstrating their ability to provide accurate predictions. The study’s results highlight varying performance among models and their key contributions, underscoring the potential to transform academic advising and enhance student decision-making. These models can be incorporated into the academic advising recommender system, thereby improving the quality of academic guidance.

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Section: Literature Reviewmentioning

confidence: 99%

Improving Academic Advising in Engineering Education with Machine Learning Using a Real-World Dataset

Maphosa,

Doorsamy,

Paul

2024

Algorithms

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“…Some of the direct areas of clustering application generally discussed in the literature have been textual document classification, image segmentation, object recognition, character recognition, information retrieval, data mining, spatial data analysis, business analytics, data reduction, and big data mining. Other areas indicated by Saxena et al ( 2017 ), have been sequence analysis (Durbin et al 1998 ; Li et al 2012 ), human genetic clustering, (Kaplan and Winther 2013 ; Lelieveld et al 2017 ; Marbac et al 2019 ), mobile banking and information system (Motiwalla et al 2019 ; Shiau et al 2019 ), social network analysis (Scott and Carrington 2011 ; Shiau et al 2017 ; Khamparia et al 2020 ), search result grouping (Mehrotra and Kohli 2016 ; Kohli and Mehrotra 2016 ), software evolution (Rathee and Chhabra 2018 ; Izadkhah and Tajgardan 2019 ), recommender systems (Petwal et al 2020 ), educational data mining (Baker 2010 ; Guleria and Sood 2020 ), climatology (Sharghi et al 2018 ; Pike and Lintner 2020 ; Chattopadhyay et al 2020 ) and robotics (Khouja and Booth 1995 ; Zhang et al 2013 ). In Table 6 below we briefly discuss a few applications as indicated by Saxena et al ( 2017 ) and also provide references for more detailed studies.…”

Section: Applications Of Clusteringmentioning

confidence: 99%

Data clustering: application and trends

Oyewole

Thopil

2022

Artif Intell Rev

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Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no clustering algorithm can solve all clustering problems has resulted in the development of several clustering algorithms with diverse applications. We review data clustering, intending to underscore recent applications in selected industrial sectors and other notable concepts. In this paper, we begin by highlighting clustering components and discussing classification terminologies. Furthermore, specific, and general applications of clustering are discussed. Notable concepts on clustering algorithms, emerging variants, measures of similarities/dissimilarities, issues surrounding clustering optimization, validation and data types are outlined. Suggestions are made to emphasize the continued interest in clustering techniques both by scholars and Industry practitioners. Key findings in this review show the size of data as a classification criterion and as data sizes for clustering become larger and varied, the determination of the optimal number of clusters will require new feature extracting methods, validation indices and clustering techniques. In addition, clustering techniques have found growing use in key industry sectors linked to the sustainable development goals such as manufacturing, transportation and logistics, energy, and healthcare, where the use of clustering is more integrated with other analytical techniques than a stand-alone clustering technique.

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Recommender System for Analyzing Students’ Performance Using Data Mining Technique

Cited by 2 publications

References 5 publications

Improving Academic Advising in Engineering Education with Machine Learning Using a Real-World Dataset

Improving Academic Advising in Engineering Education with Machine Learning Using a Real-World Dataset

Data clustering: application and trends

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