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Efficiency of LSA and K-means in Predicting Students' Academic Performance Based on Their Comments Data.

, , , and . CSEDU (1), page 63-74. SciTePress, (2014)

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Analysis of Students' Learning Activities through Quantifying Time-Series Comments., and . KES (2), volume 6882 of Lecture Notes in Computer Science, page 154-164. Springer, (2011)Correlation of grade prediction performance and validity of self-evaluation comments., , and . SIGITE Conference, page 35-42. ACM, (2013)Comments Data Mining for Evaluating Student's Performance., , , and . IIAI-AAI, page 25-30. IEEE Computer Society, (2014)A Predictive Model to Evaluate Student Performance., , , and . J. Inf. Process., 23 (2): 192-201 (2015)Correlation of Topic Model and Student Grades Using Comment Data Mining., , and . SIGCSE, page 441-446. ACM, (2015)Student Performance Estimation Based on Topic Models Considering a Range of Lessons., , and . AIED, volume 9112 of Lecture Notes in Computer Science, page 790-793. Springer, (2015)Estimation of Student Performance by Considering Consecutive Lessons., , and . IIAI-AAI, page 121-126. IEEE Computer Society, (2015)Correlation of Grade Prediction Performance with Characteristics of Lesson Subject., , , and . ICALT, page 247-249. IEEE Computer Society, (2015)Efficiency of LSA and K-means in Predicting Students' Academic Performance Based on Their Comments Data., , , and . CSEDU (1), page 63-74. SciTePress, (2014)PCN: Quantifying Learning Activity for Assessment based on Time-series Comments., and . CSEDU (2), page 419-424. SciTePress, (2011)