Using machine learning to predict student difficulties from learning session data-Reference-Cited by-同舟云学术

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Using machine learning to predict student difficulties from learning session data

Published:2018-02-10 Issue:1 Volume:52 Page:381-407
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Hussain Mushtaq,Zhu Wenhao,Zhang Wu,Abidi Syed Muhammad Raza,Ali Sadaqat

Funder

Nation Key R & D Program

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

http://link.springer.com/article/10.1007/s10462-018-9620-8/fulltext.html

Reference52 articles.

1. Abu Saa A (2016) Educational data mining and students’ performance prediction. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2016.070531

2. Acharya A, Sinha D (2014) Early prediction of students performance using machine learning techniques. Int J Comput Appl 107(1):37–43. https://doi.org/10.5120/18717-9939

3. Ameri S, Fard MJ, Chinnam RB, Reddy CK (2016) Survival analysis based framework for early prediction of student dropouts. In: 25th Procedding of the ACM conference information and knowledge management, pp 903–912. https://doi.org/10.1145/2983323.2983351

4. Arnold KE, Pistilli (2012) Course signals at purdue: using learning analytics to increase student success. In: 2nd International conference on learning analytics and knowledge (LAK’12), pp 267–270. https://doi.org/10.1145/2330601.2330666

5. Bakki A, Oubahssi L, Cherkaoui C, George S (2015) Motivation and engagement in MOOCs: How to increase learning motivation by adapting pedagogical scenarios? Desing for teaching and learning in a network world. Lecture notes in computer science 9307:556–559

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