Grade Prediction with Temporal Course-wise Influence

There is a critical need to develop new educational technology applications that analyze the data collected by universities to ensure that students graduate in a timely fashion (4 to 6 years); and they are well prepared for jobs in their respective fields of study. In this paper, we present a novel approach for analyzing historical educational records from a large, public university to perform next-term grade prediction; i.e., to estimate the grades that a student will get in a course that he/she will enroll in the next term. Accurate next-term grade prediction holds the promise for better student degree planning, personalized advising and automated interventions to ensure that students stay on track in their chosen degree program and graduate on time. We present a factorization-based approach called Matrix Factorization with Temporal Course-wise Influence that incorporates course-wise influence effects and temporal effects for grade prediction. In this model, students and courses are represented in a latent "knowledge" space. The grade of a student on a course is modeled as the similarity of their latent representation in the "knowledge" space. Course-wise influence is considered as an additional factor in the grade prediction. Our experimental results show that the proposed method outperforms several baseline approaches and infer meaningful patterns between pairs of courses within academic programs.

[1]  Haruna Chiroma,et al.  Data Mining for Education Decision Support: A Review , 2014, Int. J. Emerg. Technol. Learn..

[2]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[3]  Minchao Ye,et al.  Correction: Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering , 2015, PloS one.

[4]  Hana Bydzovská.  Are Collaborative Filtering Methods Suitable for Student Performance Prediction? , 2015, EPIA.

[5]  Michelle Parker.  Advising for Retention and Graduation , 2015 .

[6]  Upendra Prasad.  Nonnegative matrix factorization: Analysis, algorithms and applications , 2009 .

[7]  Alejandro Peña Ayala,et al.  Educational data mining: A survey and a data mining-based analysis of recent works , 2014, Expert Syst. Appl..

[8]  Ee-Peng Lim,et al.  Modeling Temporal Adoptions Using Dynamic Matrix Factorization , 2013, 2013 IEEE 13th International Conference on Data Mining.

[9]  Wenjun Zhang,et al.  Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences , 2015, IJCAI.

[10]  Rouven Schoppmann,et al.  Data mining in an educational setting : a cluster analysis of browsing behaviour , 2018 .

[11]  Scott Studham,et al.  Personalized Multi-Regression Models for Predicting Students ’ Performance in Course Activities , 2014 .

[12]  J. Simons,et al.  A national study of student early alert models at four-year institutions of higher education , 2011 .

[13]  Rabab Naqvi,et al.  Data Mining in Educational Settings , 2015 .

[14]  Aditya Johri,et al.  Next-Term Student Performance Prediction: A Recommender Systems Approach , 2016, EDM.

[15]  Wu He,et al.  Examining students' online interaction in a live video streaming environment using data mining and text mining , 2013, Comput. Hum. Behav..

[16]  Kush R. Varshney,et al.  Dynamic matrix factorization: A state space approach , 2011, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[17]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[18]  Anuj Sharma,et al.  A Collaborative Filtering Based Approach for Recommending Elective Courses , 2011, ICISTM.

[19]  George Karypis,et al.  A Comprehensive Survey of Neighborhood-based Recommendation Methods , 2011, Recommender Systems Handbook.

[20]  Štefan Pero,et al.  Comparison of Collaborative-Filtering Techniques for Small-Scale Student Performance Prediction Task , 2015 .

[21]  Xue Li,et al.  Time weight collaborative filtering , 2005, CIKM '05.

[22]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[23]  Ke Wang,et al.  Latent Factor Transition for Dynamic Collaborative Filtering , 2014, SDM.

[24]  Charu C. Aggarwal,et al.  Recommender Systems: The Textbook , 2016 .

[25]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[26]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[27]  Lars Schmidt-Thieme,et al.  Recommender system for predicting student performance , 2010, RecSysTEL@RecSys.

[28]  Huzefa Rangwala,et al.  Next-term student grade prediction , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[29]  Xi Chen,et al.  Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization , 2010, SDM.

[30]  Kush R. Varshney,et al.  Collaborative Kalman Filtering for Dynamic Matrix Factorization , 2014, IEEE Transactions on Signal Processing.

[31]  Chein-Shung Hwang,et al.  Unified Clustering Locality Preserving Matrix Factorization for Student Performance Prediction , 2022 .

[32]  George Karypis,et al.  Grade prediction with models specific to students and courses , 2016, International Journal of Data Science and Analytics.