Deep Learning applied to Learning Analytics and Educational Data Mining: A Systematic Literature Review

This work presents, to the extent of the authors’ knowledge, the first systematic literature review of the application of Deep Learning to Educational Data Mining and Learning Analytics. Previous literature reviews have documented several works in the areas of Educational Data Mining and Learning Analytics that used classical Artificial Neural Networks techniques. But none of them mentioned the new and much more powerful paradigm in Artificial Neural Networks: Deep Learning. This work surveys this new technique and identifies recent works in Learning Analytics and Educational Data Mining that have applied Deep Learning techniques.

[1]  Davide Anguita,et al.  Advances in learning analytics and educational data mining , 2015, ESANN.

[2]  Anastasios A. Economides,et al.  Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence , 2014, J. Educ. Technol. Soc..

[3]  Nitish Srivastava,et al.  Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..

[4]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

[5]  Amy Loutfi,et al.  A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..

[6]  Shreyash Tambe,et al.  EFFECTIVE DATA MINING USING NEURAL NETWORKS , 2016 .

[7]  Sebastián Ventura,et al.  Educational Data Mining: A Review of the State of the Art , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[8]  Hugh C. Davis,et al.  Linked data, data mining and external open data for better prediction of at-risk students , 2014, 2014 International Conference on Control, Decision and Information Technologies (CoDIT).

[9]  Zaidatun Tasir,et al.  Educational data mining: A review , 2013 .

[10]  Chris Piech,et al.  Deep Knowledge Tracing On Programming Exercises , 2017, L@S.

[11]  Ulrik Schroeder,et al.  Learning Analytics: Challenges and Future Research Directions , 2014 .

[12]  Li Yang,et al.  Predicting Students Performance in Educational Data Mining , 2015, 2015 International Symposium on Educational Technology (ISET).

[13]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  Wahidah Husain,et al.  A Review on Predicting Student's Performance Using Data Mining Techniques , 2015 .

[15]  Jude W. Shavlik,et al.  Using neural networks for data mining , 1997, Future Gener. Comput. Syst..

[16]  William W. Guo Incorporating statistical and neural network approaches for student course satisfaction analysis and prediction , 2010, Expert Syst. Appl..

[17]  Chitu Okoli,et al.  A Guide to Conducting a Systematic Literature Review of Information Systems Research , 2010 .

[18]  Ryan S. Baker,et al.  Educational Data Mining and Learning Analytics , 2014 .

[19]  Arlene Fink,et al.  Conducting research literature reviews : from the internet to paper , 2014 .

[20]  Mansureh Kebritchi,et al.  Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. , 2016 .

[21]  Rajni Jindal,et al.  A SURVEY ON EDUCATIONAL DATA MINING AND RESEARCH TRENDS , 2013 .

[22]  Hiroaki Ogata,et al.  A neural network approach for students' performance prediction , 2017, LAK.

[23]  Rebecca Ferguson,et al.  Learning analytics: drivers, developments and challenges , 2012 .

[24]  Safwan Wshah,et al.  A handwriting recognition system for the classroom , 2015, LAK.

[25]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement , 2009, BMJ.

[26]  Zachary A. Pardos,et al.  Deep Neural Networks and How They Apply to Sequential Education Data , 2016, L@S.

[27]  Unhawa Ninrutsirikun,et al.  Effect of the Multiple Intelligences in multiclass predictive model of computer programming course achievement , 2016, 2016 IEEE Region 10 Conference (TENCON).

[28]  Mohan S. Kankanhalli,et al.  Multi-stream Deep Learning Framework for Automated Presentation Assessment , 2016, 2016 IEEE International Symposium on Multimedia (ISM).

[29]  Hung-Chang Liao,et al.  Data mining for adaptive learning in a TESL-based e-learning system , 2011, Expert Syst. Appl..

[30]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.

[31]  Zivadin Micic,et al.  A web-based intelligent report e-learning system using data mining techniques , 2013, Comput. Electr. Eng..

[32]  Jamalul-lail Ab Manan,et al.  Prediction of engineering students' academic performance using Artificial Neural Network and Linear Regression: A comparison , 2013, 2013 IEEE 5th Conference on Engineering Education (ICEED).

[33]  Samuel DiGangi,et al.  A Data Mining Approach for Identifying Predictors of Student Retention from Sophomore to Junior Year , 2021, Journal of Data Science.

[34]  Ivan Jordanov,et al.  An overview of the use of neural networks for data mining tasks , 2012, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

[35]  Ganesh Chandra Deka,et al.  Application of knowledge based decision technique to predict student enrollment decision , 2011, 2011 International Conference on Recent Trends in Information Systems.

[36]  Jamalul-lail Ab Manan,et al.  A neural network students' performance prediction model (NNSPPM) , 2013, 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA).