Collaborative multi-regression models for predicting students' performance in course activities

Elbadrawy, Asmaa; Studham, R. Scott; Karypis, George

doi:10.1145/2723576.2723590

Cited by 72 publications

(42 citation statements)

References 4 publications

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“…But, the majority of researchers in the field of educational data mining is focused on predicting core academic performance of students' based on their past academic results [10][11][12][13][14][15].…”

Section: Related Workmentioning

confidence: 99%

A Unified Model of Clustering and Classification to Improve Students’ Employability Prediction

Thakar¹,

Mehta²,

Manisha³

2017

IJISA

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Abstract-DataMining is gaining immense popularity in the field of education due to its predictive capabilities. But, most of the prior effort in this area is only directed towards prediction of performance in academic results only. Nowadays, education has become employment oriented. Very little attempt is made to predict students' employability.Precise prediction of students' performance in campus placements at an early stage can identify students, who are at the risk of unemployment and proactive actions can be taken to improve their performance.Existing researches on students' employability prediction are either based upon only one type of course or on single University/Institute; thus is not scalable from one context to another. With this necessity, the conception of a unified model of clustering and classification is proposed in this paper.With the notion of unification, data of professional courses namely Engineering and Masters in Computer Applications students are collected from various universities and institutions pan India. Data is large, multivariate, incomplete, heterogeneous and unbalanced in nature. To deal with such a data, a unified predictive model is built by integrating clustering and classification techniques. Two-Level clustering (k-means kernel) with chi-square analysis is applied at the pre-processing stage for the automated selection of relevant attributes and then ensemble vote classification technique with a combination of four classifiers namely k-star, random tree, simple cart and the random forest is applied to predict students' employability. Proposed framework provides a generalized solution for student employability prediction. Comparative results clearly depict model performance over various classification techniques. Also, when the proposed model is applied up to the level of the state, classification accuracy touches 96.78% and 0.937 kappa value.

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Section: Related Workmentioning

confidence: 99%

A Unified Model of Clustering and Classification to Improve Students’ Employability Prediction

Thakar¹,

Mehta²,

Manisha³

2017

IJISA

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“…Other studies that follow less common approaches include those that use Markov Networks ( [39]), Collaborative Multi-Regression models ( [40]), smartphone data to investigation the correlation between students' social and study behaviour and academic performance ( [41]) and those that perform Sentiment Analysis of discussion form posts in MOOCs ( [42]). Yet, some studies discuss algorithms developed for the sole purpose of student performance prediction ( [43], [44]).…”

Section: Prediction Techniques and Algorithmsmentioning

confidence: 99%

A Comparative Analysis of Selected Studies in Student Performance Prediction

Ashenafi¹

2017

IJDKP

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“…Text mining of written comments has been applied for performance prediction by [13,23], while [15,20] apply classification and genetic algorithms with features about student interaction and the use of the LMS. In order to analyze the student's past performance and interaction with the LMS and predict how well he/she will perform in course activities, multi-regression models have also been proposed [9]. Various approaches for modeling and predicting the success or failure of students in the context of intelligent tutoring systems have been developed.…”

Section: Related Workmentioning

confidence: 99%

Grade prediction with models specific to students and courses

Polyzou

Karypis

2016

Int J Data Sci Anal

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The accurate estimation of students' grades in future courses is important as it can inform the selection of next term's courses and create personalized degree pathways to facilitate successful and timely graduation. This paper presents future course grade predictions methods based on sparse linear and low-rank matrix factorization models that are specific to each course or student-course tuple. These methods identify the predictive subsets of prior courses on a course-by-course basis and better address problems associated with the not-missing-at-random nature of the student-course historical grade data. The methods were evaluated on a dataset obtained from the University of Minnesota, for two different departments with different characteristics. This evaluation showed that focusing on course-specific data improves the accuracy of grade prediction.

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Collaborative multi-regression models for predicting students' performance in course activities

Cited by 72 publications

References 4 publications

A Unified Model of Clustering and Classification to Improve Students’ Employability Prediction

A Unified Model of Clustering and Classification to Improve Students’ Employability Prediction

A Comparative Analysis of Selected Studies in Student Performance Prediction

Grade prediction with models specific to students and courses

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