Next-term student grade prediction

Sweeney, Mack; Lester, Jaime; Rangwala, Huzefa

doi:10.1109/bigdata.2015.7363847

Cited by 94 publications

(57 citation statements)

References 15 publications

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“…Matrix factorization from RS [16] can be applied for the nextterm grade prediction problem, when the student-course grade matrix is considered as the user-item rating matrix. Two low-rank matrices containing latent factors of courses and students in a common knowledge space can be learned from such a student-course grade matrix [19]. Thus, the grade of a student s on a course c can be predicted as…”

Section: Matrix Factorization Based Grade Predictionmentioning

confidence: 99%

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ALE: Additive Latent Effect Models for Grade Prediction

Ren

Ning

Rangwala

2018

Proceedings of the 2018 SIAM International Conference on Data Mining

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The past decade has seen a growth in the development and deployment of educational technologies for assisting college-going students in choosing majors, selecting courses and acquiring feedback based on past academic performance. Grade prediction methods seek to estimate a grade that a student may achieve in a course that she may take in the future (e.g., next term). Accurate and timely prediction of students' academic grades is important for developing effective degree planners and early warning systems, and ultimately improving educational outcomes. Existing grade prediction methods mostly focus on modeling the knowledge components associated with each course and student, and often overlook other factors such as the difficulty of each knowledge component, course instructors, student interest, capabilities and effort.In this paper, we propose additive latent effect models that incorporate these factors to predict the student next-term grades. Specifically, the proposed models take into account four factors: (i) student's academic level, (ii) course instructors, (iii) student global latent factor, and (iv) latent knowledge factors. We compared the new models with several state-of-the-art methods on students of various characteristics (e.g., whether a student transferred in or not). The experimental results demonstrate that the proposed methods significantly outperform the baselines on grade prediction problem. Moreover, we perform a thorough analysis on the importance of different factors and how these factors can practically assist students in course selection, and finally improve their academic performance.

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Section: Matrix Factorization Based Grade Predictionmentioning

confidence: 99%

“…Matrix factorization (MF) based approaches have been widely used for solving the grade prediction problems [19,3]. MF methods decompose the student-course grade matrix into two low-rank matrices containing student and course latent factors.…”

mentioning

confidence: 99%

ALE: Additive Latent Effect Models for Grade Prediction

Ren

Ning

Rangwala

2018

Proceedings of the 2018 SIAM International Conference on Data Mining

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“…Educational data mining research on LMS data has uncovered several important predictors on student success, namely the impact of engagement measures (for example, logins, number of forum posts, time online) on student course completion and final grade (Elbadrawy et al., ; Sweeney, Lester, & Rangwala, ). Campbell, Finnegan, and Collins () in an examination of student academic success and LMS data found that student logins in the LMS were more predictive of student completion than SAT scores, an indicator of prior academic preparation.…”

Section: Introduction To Learning Analytics and Educational Technologmentioning

confidence: 99%

Learning Analytics in Higher Education

Lester¹,

Klein²,

Rangwala³

et al. 2017

ASHE Higher Education Report

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“…Despite their relative simplicity, the estimations obtained by these methods are reasonably accurate indicating that there is sufficient information in the historical student-course grade data to make the estimation problem feasible. Influenced by the area of recommender systems, the authors of [25,26] examine grading prediction as rating prediction using a matrix completion approach. In [26], the features are about the student, course, and instructor, while in [25] (and most relevant to our problem), the matrix contains information about the grades of past courses.…”

Section: Related Workmentioning

confidence: 99%

“…Influenced by the area of recommender systems, the authors of [25,26] examine grading prediction as rating prediction using a matrix completion approach. In [26], the features are about the student, course, and instructor, while in [25] (and most relevant to our problem), the matrix contains information about the grades of past courses. The matrix will be estimated by the product of lower-rank matrices.…”

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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Next-term student grade prediction

Cited by 94 publications

References 15 publications

ALE: Additive Latent Effect Models for Grade Prediction

ALE: Additive Latent Effect Models for Grade Prediction

Learning Analytics in Higher Education

Grade prediction with models specific to students and courses

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