Abstract

I Learning Analytics is a process to analyze the learners which improves the educational performance. Learning Analytics also helps the higher educational institutions to improve the educational practices and techniques. This paper provides a detailed survey of the current research activities conducted in the education system. The review of Learning Analytics is based on some of existing Learning Analytics applications, purpose of the Learning Analytics in the education system and the type of students. The review based on various tools, techniques and data collection methods used to implement the Learning analytics is discussed in this paper.

References

• Charles S. Elliott, " JACME2T: AN INDUSTRY - ACADEMIC CONSORTIA TO ENHANCE CONTINUING ENGINEERING EDUCATION ", FIE Conference, 1998
• Khalid Isa, Shamsul Mohamad, ZarinaTukiran," Development of INPLANS: An Analysis on Students' Performance using Neuro-Fuzzy", symposium on information technology, vol 3, pages 1-7, 2008
• Tsolakidis Anastasios, Cleo Sgouropoulou, Ioannis Xydas, Olivier Terraz, Georgios Miaoulis,"Academic Research Policy-making and Evaluation using Graph Visualisation", Panhellenic conference on Informatics, 2011, pages 28-32
• Hsu-Chen Cheng, Wen-Wei Liao," ESTABLISHING AN LIFELONG LEARNING ENVIRONMENT USING IOT AND LEARNING ANALYTICS", ICACT, pages 1178-1183, 2012
• Israel Gutiérrez Rojas, Raquel M. Crespo García,"Towards efficient provision of feedback supported by learning analytics", ICALT, pages 599-603, 2012
• Naif Radi Aljohani Hugh C. Davis, "Significance of Learning Analytics in Enhancing The Mobile and Pervasive Learning Environments ", NGMAST, pages 70-74, 2012
• Eleni Koulocheri Alexandros Soumplis Michalis Xenos, "Applying Learning Analytics in an Open Personal Learning Environment A quantitative approach", PCI, pages 290-295, 2012
• Ángel F. Agudo-Peregrina, Ángel Hernández-García, Santiago Iglesias-Pradas, "Predicting academic performance with learning analytics in virtual learning environments - A comparative study of three interaction classifications", SIIE, pages 1-6, 2012
• Carlos Márquez-Vera, Cristóbal Romero Morales, and Sebastián Ventura Soto, "Predicting School Failure and Dropout by Using Data Mining Techniques" , IEEE-RITA, Vol 8, pages 7-14, 2013
• N. Pratheesh T. Devi, " Influence of Learning Analytics in Software Engineering education" , ICE-CCN, pages 712-716,2013
• Carlos Delgado Kloos, Abelardo Pardo*, Pedro J. Muñoz-Merino, Israel Gutiérrez, Derick Leony, " Learning Analytics @ UC3M" , EDUCON, pages 1232-1238,2013
• Gregorio Robles, Jesus M Gonzalez Barahona, "Mining student repositories to gain learning analytics – An Experience report", EDUCON, pages 1249-1254, 2013
• Muazzam Ahmed Siddiqui and Shehab Gemalel-Din, "Evaluation of Academic Plans of Study Using Data Mining Techniques", ICALT, pages 224-228, 2013
• Yohannes Kurniawan Erwin Halim, "Use Data Warehouse and Data Mining to Predict Student Academic Performance in Schools: A Case Study (Perspective Application and Benefits)", TALE, pages 98-103, 2013
• Naif Radi Aljohani Hugh C. Davis, "Learning Analytics and Formative Assessment to Provide Immediate Detailed Feedback Using a Student Centered Mobile Dashboard", NGMAST, pages 262-267, 2013
• Kannan Govindarajan, Thamarai Selvi Somasundaram, Vivekanandan S Kumar, Kinshuk, "Continuous Clustering in Big Data Learning Analytics", ICT4E(TECHNOLOGY FOR EDUCATION), pages 61-64,2013
• Ferreira S. A and Andrade A, "Academic Analytics: Mapping the Genome of the University", IEEE-RITA, Vol 9, issue 3, pages 98-105, 2014
• Juan Cruz-Benito, Roberto Therón, Francisco J. García-Peñalvo Cristina Maderuelo, Jonás Samuel Pérez-Blanco, "Monitoring and feedback of Learning Processes in Virtual Worlds through analytics architectures: a real case", CISTI, pages 1-6, 2014
• George Lepouras, Akrivi Katifori, Costas Vassilakis, Angeliki Antoniou, Nikos Platis, "Towards a Learning Analytics Platform for Supporting the Educational Process", IISA, 2014
• Chatti M. A, Dugoija D, Thus H, Schroeder U, "Learner Modeling in Academic Networks", ICALT, pages 117-121, 2014
• Luis de-la-Fuente-Valentín, Daniel Burgos, Rubén González Crespo, "A4Learning: a case study to improve the user performance Alumni Alike Activity Analytics to self-assess personal progress" , ICALT, pages 360-362, 2014
• Sven MANSKE, Tobias HECKING, Lars BOLLEN, Tilman GÖHNERT, Alfredo RAMOS, H. Ulrich HOPPE, "A Flexible Framework for the Authoring of Reusable and Portable Learning Analytics Gadgets", ICALT, pages 254-258, 2014
• Manav Malhotra I-Han Hsiao Hui Soo Chae Gary Natriello, "Data Depository: Business & Learning Analytics for Educational Web Applications" , ICALT, pages 363-364, 2014
• Olga C. Santos Sergio Salmeron-Majadas Mar Saneiro Jesus G. Boticario, "A Methodological Approach to Eliciting Affective Educational Recommendations", ICALT, pages 529-533, 2014
• Carlotta Schatten, Martin Wistuba, Lars Schmidt-Thieme Sergio Gutierr´ez-Santos, "Minimal Invasive Integration of Learning Analytics Services in Intelligent Tutoring Systems", ICALT, pages 746-748, 2014
• Sandra Gama, Daniel Goncalves, " Visualizing Large Quantities of Educational Data mining", International Conference in Information Visualization(ICIV), pages 102-107, 2014
• Hayriye Tugba Ozturk Deniz Deryakulu Huseyin Ozcinar Deniz Atal, "Advancing Learning Analytics in Online Learning Environments Through the Method of Sequential Analysis", International Conference on Multimedia Computing and Systems(ICMCS), pages 512-516, 2014
• Usamah bin Mat, Norlida Buniyamin, Pauziah Mohd Arsad, RosniAbu Kassim, "An Overview of Using Academic Analytics to Predict and Improve Students' Achievement: A Proposed Proactive Intelligent Intervention", IEEE Conference on Engineering Education (ICEED), pages 126-130, 2013