Learning Analytics: A Survey

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2014 by IJCTT Journal
Volume-18 Number-6
Year of Publication : 2014
Authors : Usha Keshavamurthy , Dr. H S Guruprasad
DOI :  10.14445/22312803/IJCTT-V18P155

MLA

Usha Keshavamurthy , Dr. H S Guruprasad "Learning Analytics: A Survey". International Journal of Computer Trends and Technology (IJCTT) V18(6):260-264, Dec 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Learning analytics is a research topic that is gaining increasing popularity in recent time. It analyzes the learning data available in order to make aware or improvise the process itself and/or the outcome such as student performance. In this survey paper, we look at the recent research work that has been conducted around learning analytics, framework and integrated models, and application of various models and data mining techniques to identify students at risk and to predict student performance.

References
[1] Ferguson, Rebecca, Learning analytics: drivers, developments and challenges, International Journal of Technology Enhanced Learning, 4(5/6), pp. 304 – 317, DOI: 10.1504/IJTEL.2012.051816.
[2] Wolfgang Greller, Hendrik Drachsle., Translating Learning into Numbers: A Generic Framework for Learning Analytics, Educational Technology & Society, Volume 3, Issue 5, pp 42-57, ISSN: 1436-4522.
[3] Erik Duval, Attention please! Learning analytics for visualization and recommendation, 1st International Conference on Learning Analytics and Knowledge, 2011, pp 9-17, DOI: 10.1145/2090116.2090118.
[4] Jie Zhang, William Chandra Tjhi, Bu Sung Lee, Kee Khoon Lee, Julita Vassileva, Chee Kit Looi, A Framework of User-Driven Data Analytics in the Cloud for Course Management, 18th International Conference on Computers in Education, Putrajaya, Malaysia, Nov 29 - Dec 3 2010, Asia-Pacific Society for Computers in Education.
[5] Rebecca Barber, Mike Sharkey, Course Correction: Using Analytics to Predict Course Success, 2nd International Conference on Learning Analytics and Knowledge, 2012, pp 259-262, DOI: 10.1145/2330601.2330664.
[6] Alyssa Friend Wise, Yuting Zhao, Simone Nicole Hausknecht, Learning analytics for online discussions: a pedagogical model for intervention with embedded and extracted analytics, Third International Conference on Learning Analytics and Knowledge, 2013, pp 48-56, DOI: 10.1145/2460296.2460308.
[7] Alfred Essa, Hanan Ayad, “Student success system: risk analytics and data visualization using ensembles of predictive models”, 2nd International Conference on Learning Analytics and Knowledge, pp 158-161, DOI: 10.1145/2330601.2330641.
[8] Cristobal Romero, Manuel-Ignacio Lopez, Jose-Maria Luna, Sebastian Ventura, Predicting students` final performance from participation in on-line discussion forums”, Journal Computers and Education, Volume 68, Oct 2013, pp 458-472, DOI: 10.1016/j.compedu.2013.06.009.
[9] Tim Rogers, Cassandra Colvin, Modest analytics: using the index method to identify students at risk of failure, Fourth International Conference on Learning Analytics and Knowledge, 2014, pp 118-122, DOI: 10.1145/2567574.2567629.
[10] K Nandhini, S Saranya, ID3 Classifier for Pupils’ Status Prediction, International Journal of Computer Applications, Volume 57, No. 3, Nov 2012, pp 14-18, DOI: 10.5120/9094-3133.
[11] Brijesh Kumar Baradwaj, Saurabh Pal, Mining Educational Data to Analyze Students’ Performance, International Journal of Advanced Computer Science and Applications, Volume 2, No. 6, 2011, pp 63-69.
[12] Abeer Badr El Din Ahmed, Ibrahim Sayed Elaraby Data Mining: A prediction for Student`s Performance Using Classification Method, World Journal of Computer Application and Technology, Volume 2, pp 43-47, 2014, DOI: 10.13189/wjcat.2014.020203.
[13] Mohammed M. Abu Tair, Alaa M. El-Halees, Mining Educational Data to Improve Students Performance: A Case Study, International Journal of Information and Communication Technology Research, Volume 2, No. 2, February 2012, pp 140-146, ISSN 2223-4985.
[14] P K Srimani, Annapurna S Kamath, Data Mining Techniques for the Performance Analysis of a Learning Model - A Case Study, International Journal of Computer Applications, Volume 53, No.5, Sept 2012, pp 36-42, DOI: 10.5120/8421-1896.
[15] Adhatrao K, Gaykar A, Dhawan A, Jha R, Honrao V, Predicting Students’ Performance using ID3 and C4.5 classification algorithms, International Journal of Data Mining & Knowledge Management Process, Volume 3, No. 5, pp 39-52, Sept 2013, DOI : 10.5121/ijdkp.2013.3504.
[16] Ramanathan L, Saksham Dhanda, Suresh Kumar D, Predicting Students’ Performance using modified ID3 algorithm, International Journal of Engineering and Technology, Volume 5, No. 3, Jun-Jul 2013, pp 2491 - 2497.
[17] Mrinal Pandey, Vivek Kumar Sharma, “A Decision Tree Algorithm Pertaining to the Student Performance Analaysis and Prediction”, International Journal of Computer Applications, volume 61, No. 13, Jan 2013, pp 1-5, DOI: 10.5120/9985-4822.
[18] Kabakchieva D, “Predicting Student Performance by Using Data Mining Methods for Classification”, Cybernetics and Information Technologies, Volume 13, No. 1, pp 61-72, DOI: 10.2478/cait-2013-0006.
[19] Edin Osmanbegovic, Mirza Suljic, Data Mining Approach for predicting student performance, Economic Review - Journal of Economics and Business, Volume 10, Issue 1, May 2012, pp 3-12.
[20] V Ramesh, P Parkavi, K Ramar, Predicting Student Performance: A Statistical and Data Mining Approach, International Journal of Computer Applications, Volume 63, No. 8, Feb 2013, pp 35-39, DOI: 10.5120/10489-5242
[21] Shaymaa E Sorour, Tsunenori Mine, Kazumasa Goda, Sachio Hirokawa, Efficiency of LSA and K-means in Predicting Students’ Academic Performance Based on Their Comments Data, 6th International Conference on Computer Supported Education, pp 63 - 74, 2014.
[22] Jacob Kogan, Student Course Evaluation - Class Size, Class Level, Discipline and Gender Bias, SCITEPRESS Digital Library, 6th International Conference on Computer Supported Education, 2014, pp 221-225, DOI: 10.5220/0004861802210225.
[23] Wolff Annika, Zdrahal Zdenek, Nikolov Andriy, Pantucek Michal, “Improving retention: predicting at-risk students by analyzing clicking behavior in a virtual learning environment”, Third Conference on Learning Analytics and Knowledge (LAK 2013), 8-12 April 2013, Leuven, Belgium, pp 145-149, DOI: 10.1145/2460296.2460324.
[24] Smith V C, Lange A, Huston D R, Predictive modeling to forecast student outcomes and drive effective interventions in online community college courses, June 2012, Journal of Asynchronous Learning Networks, Volume 16, No. 3, pp 51-61, ISSN 1939-5256.
[25] Sotiris Kotsiantis, Nikolaos Tselios, Andromahi Filippidi, Vassilis Komis, Using learning analytics to identify successful learners in a blended learning course, International Journal of Technology Enhanced Learning, Volume 5, Issue 2, pp 133-150, Feb 2013, DOI: 10.1504/IJTEL.2013.059088.
[26] Kimberly E. Arnold, Matthew D. Pistilli, Course Signals at Purdue: Using Learning Analytics to Increase Student Success, Second International Conference on Learning Analytics and Knowledge, 2012, Vancouver, Canada, pp 267-270, DOI: 10.1145/2330601.2330666.
[27] Doug Clow, MOOCs and the Funnel of Participation, Third Conference on Learning Analytics and Knowledge (LAK 2013), 8-12 April 2013, Leuven, Belgium, pp 185-189, DOI: 10.1145/2460296.2460332.
[28] Ourania Petropoulou, Katerina Kasimatis, Ioannis Dimopoulos, Symeon Retalis LAe-R: A new learning analytics tool in Moodle for assessing students’ performance, Bulletin of the IEEE Technical Committee on Learning Technology, Volume 16, Number 1, pp 2-5, January 2014.
[29] Giesbers B, Rienties B, Tempelaar D, Gijselaers W, Investigating the relations between motivation, tool use, participation, and performance in an e-learning course using web-videoconferencing, 2013, Computers in Human Behavior, Volume 29, No. 1, pp 285-292, DOI: 10.1016/j.chb.2012.09.005.
[30] Sharon Slade, Prinsloo Paul, Learning analytics: ethical issues and dilemmas, American Behavioral Scientist, Oct 2013, Volume 57, No. 10, pp 1510-1529, DOI: 10.1177/0002764213479366.

Keywords
Learning Analytics, Student Performance, Student Retention, Academic analytics, Course success.