A Learning Analytics Approach for Job Scheduling on Cloud Servers

  • Mohammad Samadi Gharajeh
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 94)


Learning analytics improves the teaching and learning procedures by using the educational data. It uses analysis tools to carry out the statistical evaluation of rich data and the pattern recognition within data. This chapter, firstly, describes four learning analytics methods in educational institutions. Secondly, it proposes a learning analytics approach for job scheduling on cloud servers, called LAJOS. This approach applies a learning-based mechanism to prioritise users’ jobs on scheduling queues. It uses the three basic attributes “importance level”, “waiting time” and “deadline time” of various jobs on cloud servers. The cloud broker acts as a teacher and local schedulers of cloud sites act as students. The broker learns to local schedulers how to prioritise users’ jobs according to the values of their attributes. In the deployment phase, the effect of the above attributes on the system throughput is studied separately to select the best attribute. In the service phase, users’ jobs are prioritised by computer systems according to the selected attribute. Simulation results show that the LAJOS approach is more efficient compared to some of the job scheduling methods in terms of schedule length and system throughput.


Learning analytics Teaching procedure Learning management Cloud computing Job scheduling 





Cyclomatic Complexity


Central Processing Unit


Computer-Supported Collaborative Learning


Human Computer Interaction


Hellenic Open University




Identifier Number


Information Technology


Learning Analytics approach for JOb Scheduling


Quality of Service


Random Access Memory


Schedule Length


Services Management Agent


System Throughput


Virtual Machines


Web Service Agent


  1. Abdullahi M, Ngadi MA, Abdulhamid SM (2016) Symbiotic organism search optimization based task scheduling in cloud computing environment. Future Gener Comp Sy 56:640–650CrossRefGoogle Scholar
  2. Antonopoulos N, Gillam L (2010) Cloud computing: principles, systems and applications. Springer Science & Business MediaGoogle Scholar
  3. Arnold KE, Pistilli MD (2012) Course signals at Purdue: using learning analytics to increase student success. Paper presented at the 2nd international conference on learning analytics and knowledge, ACM, New York, NY, USA, pp 267–270Google Scholar
  4. Baker BM (2007) A conceptual framework for making knowledge actionable through capital formation. Dissertation, University of Maryland University CollegeGoogle Scholar
  5. Baker RS, Inventado PS (2014) Educational data mining and learning analytics. In: Larusson JA, White B (eds) Learning analytics. Springer, pp 61–75Google Scholar
  6. Bandiera M, Bruno C (2006) Active/cooperative learning in schools. J Biol Educ 40:130–134CrossRefGoogle Scholar
  7. Barab SA, Barnett M, Yamagata-Lynch L et al (2002) Using activity theory to understand the systemic tensions characterizing a technology-rich introductory astronomy course. Mind Cult Act 9:76–107CrossRefGoogle Scholar
  8. Bele JL, Rugelj J (2010) Comparing learning results of web based and traditional learning students. Lect Notes Comput Sci, pp 375–380Google Scholar
  9. Burkimsher A, Bate I, Indrusiak LS (2013) A survey of scheduling metrics and an improved ordering policy for list schedulers operating on workloads with dependencies and a wide variation in execution times. Future Gener Comp Sy 29:2009–2025CrossRefGoogle Scholar
  10. Chatti MA, Dyckhoff AL, Schroeder U et al (2012) A reference model for learning analytics. Int J Technol Enhanc Learn 4:318–331CrossRefGoogle Scholar
  11. Chung W-C, Hsu C-J, Lai K-C et al (2013) Chung. Direction-aware resource discovery in large-scale distributed computing environments. J Supercomput 66:229–248CrossRefGoogle Scholar
  12. Dawson S (2008) A study of the relationship between student social networks and sense of community. Educ Technol Soc 11:224–238Google Scholar
  13. Dron J, Anderson T (2009) On the design of collective applications. Paper presented at the IEEE international conference on computational science and engineering (CSE’09), Vancouver, BC, 29–31 Aug 2009, pp 368–374Google Scholar
  14. Dutta D, Joshi RC (2011) A genetic: algorithm approach to cost-based multi-QoS job scheduling in cloud computing environment. Paper presented at the international conference & workshop on emerging trends in technology (ICWET’11), New York, NY, USA, 2011, pp 422–427Google Scholar
  15. Engeström Y (1999) Activity theory and individual and social transformation. In: Engeström Y, Miettinen R, Punamäki R-L (eds) Perspectives on activity theory. Cambridge University Press, pp 19–38Google Scholar
  16. Ferguson R (2012) Learning analytics: drivers, developments and challenges. Int J Techn Enhanc Learn 4:304–317CrossRefGoogle Scholar
  17. Fernández-Delgado M, Mucientes M, Vázquez-Barreiros B et al (2014) Learning analytics for the prediction of the educational objectives achievement. Paper presented at the IEEE frontiers in education conference (FIE), Madrid, Spain, 22–25 Oct 2014, pp 1–4Google Scholar
  18. Fernández-Gallego B, Lama M, Vidal JC et al (2013) Learning analytics framework for educational virtual worlds. Procedia Comput Sci 25:443–447CrossRefGoogle Scholar
  19. Ferreira SA, Andrade A (2014) Academic analytics: mapping the genome of the University. IEEE Rev Iberoam Technol Aprendizaje 9:98–105CrossRefGoogle Scholar
  20. Ghanbari S, Othman M (2012) A priority based job scheduling algorithm in cloud computing. Procedia Eng 50:778–785CrossRefGoogle Scholar
  21. Gharajeh MS (2015) The significant concepts of cloud computing: technology, architecture, applications, and security. CreateSpace Independent Publishing PlatformGoogle Scholar
  22. Günther CW, Van Der Aalst WMP (2007) Fuzzy mining–adaptive process simplification based on multi-perspective metrics. In: Alonso G, Dadam P, Rosemann M (eds) Business process management. Springer, pp 328–343Google Scholar
  23. Haythornthwaite C, De Laat M, Dawson S et al (2013) Introduction to learning analytics and networked learning minitrack. Paper presented at the IEEE 46th Hawaii international conference on system sciences (HICSS), Wailea, Maui, HI, 7–10 Jan 2013, p 3077Google Scholar
  24. Hrastinski S (2009) A theory of online learning as online participation. Comput Educ 52:78–82CrossRefGoogle Scholar
  25. Jena RK (2015) Multi objective task scheduling in cloud environment using nested PSO framework. Procedia Comput Sci 57:1219–1227CrossRefGoogle Scholar
  26. Kalra M, Singh S (2015) A review of metaheuristic scheduling techniques in cloud computing. Egypt Inform J 16:275–295CrossRefGoogle Scholar
  27. Kao Y-C, Chen Y-S (2016) Data-locality-aware mapreduce real-time scheduling framework. J Syst Softw 112:65–77CrossRefGoogle Scholar
  28. Kaur R, Kinger S (2014) Analysis of job scheduling algorithms in cloud computing. Int J Comp Trends Technol 9:379–386CrossRefGoogle Scholar
  29. Kavvadia E, Sagiadinos S, Oikonomou K et al (2015) Elastic virtual machine placement in cloud computing network environments. Comput Netw 93:435–447CrossRefGoogle Scholar
  30. Leont’ev AN (1974) The problem of activity in psychology. Sov Psychol 13:4–33Google Scholar
  31. Li L (2009) An optimistic differentiated service job scheduling system for cloud computing service users and providers. Paper presented at the IEEE third international conference on multimedia and ubiquitous engineering (MUE’09), Qingdao, 4–6 June 2009, pp 295–299Google Scholar
  32. Li J, Qiu M, Ming Z et al (2012) Online optimization for scheduling preemptable tasks on IaaS cloud systems. J Parallel Distr Com 72:666–677CrossRefGoogle Scholar
  33. Liu X, Zha Y, Yin Q et al (2015) Scheduling parallel jobs with tentative runs and consolidation in the cloud. J Syst Softw 104:141–151CrossRefGoogle Scholar
  34. Lotsari E, Verykios VS, Panagiotakopoulos C et al (2014) A learning analytics methodology for student profiling. In: Likas A, Blekas K, Kalles D (eds) Artificial intelligence: methods and applications. Springer, pp 300–312Google Scholar
  35. Ma J, Han X, Yang J et al (2015) Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: The role of the instructor. Internet High Educ 24:26–34CrossRefGoogle Scholar
  36. Maguluri ST, Srikant R, Ying L (2012) Stochastic models of load balancing and scheduling in cloud computing clusters. Paper presented at the IEEE INFOCOM, Orlando, Florida, USA, 25–30 Mar 2012, pp 702–710Google Scholar
  37. McCabe TJ (1976) A complexity measure. IEEE Trans Softw Eng 4:308–320CrossRefzbMATHGoogle Scholar
  38. Mohialdeen IA (2013) Comparative study of scheduling algorithms in cloud computing environment. J Comput Sci Technol 9:252–263Google Scholar
  39. Norris D, Baer L, Leonard J et al (2008a) Action analytics: measuring and improving performance that matters in higher education. Educause Rev 43:42–67Google Scholar
  40. Norris D, Baer L, Leonard J et al (2008b) Framing action analytics and putting them to work. Educause Rev 43:1–10Google Scholar
  41. Ozturk HT, Deryakulu D, Ozcinar H et al (2014) Advancing learning analytics in online learning environments through the method of sequential analysis. Paper presented at the IEEE international conference on multimedia computing and systems (ICMCS), Marrakech, 14–16 Apr 2014, pp 512–516Google Scholar
  42. Park EL, Choi BK (2014) Transformation of classroom spaces: traditional versus active learning classroom in colleges. High Educ 68:749–771CrossRefGoogle Scholar
  43. Patel SJ, Bhoi UR (2014) Improved priority based job scheduling algorithm in cloud computing using iterative method. Paper presented at the IEEE fourth international conference on advances in computing and communications (ICACC), Cochin, 27–29 Aug 2014, pp 199–202Google Scholar
  44. Picciano AG (2012) The evolution of big data and learning analytics in american higher education. J Learn Asynchronous Netw 16:9–20Google Scholar
  45. Ratten V (2016) Continuance use intention of cloud computing: innovativeness and creativity perspectives. J Bus Res 69:1737–1740CrossRefGoogle Scholar
  46. Romero C, Ventura S (2007) Educational data mining: a survey from 1995 to 2005. Expert Syst Appl 33:135–146CrossRefGoogle Scholar
  47. Rovai AP (2002a) Building sense of community at a distance. Int Rev Res Open Distr Learn 3Google Scholar
  48. Rovai AP (2002b) Development of an instrument to measure classroom community. Internet High Educ 5:197–211CrossRefGoogle Scholar
  49. Sánchez M, Aguilar J, Cordero J et al (2016) Cloud computing in smart educational environments: application in learning analytics as service. In: Rocha A, Correia AM, Adeli H et al (eds) New advances in information systems and technologies. Springer, pp 993–1002Google Scholar
  50. Scheffel M, Niemann K, Leony D et al (2012) Key action extraction for learning analytics. In: Ravenscroft A, Lindstaedt S, Kloos CD et al (eds) 21st century learning for 21st century skills. Springer, pp 320–333Google Scholar
  51. Selvarani S, Sadhasivam GS (2010) Improved cost-based algorithm for task scheduling in cloud computing. Paper presented at the IEEE international conference on computational intelligence and computing research (ICCIC), Tamil Nadu, India, 28–29 Dec 2010, pp 1–5Google Scholar
  52. Thomas A, Krishnalal G, Raj VPJ (2015) Credit based scheduling algorithm in cloud computing environment. Procedia Comput Sci 46:913–920CrossRefGoogle Scholar
  53. Thrun S, Pratt L (2012) Learning to learn. Springer Science & Business MediaGoogle Scholar
  54. Tinto V (2006) Research and practice of student retention: what next? J Coll Stud Ret Res Theory Pract 8:1–19Google Scholar
  55. Vahdat M, Oneto L, Anguita D et al (2015) A learning analytics approach to correlate the academic achievements of students with interaction data from an educational simulator. In: Conole G, Klobučar T, Rensing C et al (eds) Design for teaching and learning in a networked world. Springer, pp 352–366Google Scholar
  56. Vonderwell S (2003) An examination of asynchronous communication experiences and perspectives of students in an online course: A case study. Internet High Educ 6:77–90CrossRefGoogle Scholar
  57. Wang B, Qi Z, Ma R et al (2015) A survey on data center networking for cloud computing. Comput Netw 91:528–547Google Scholar
  58. Xing W, Guo R, Petakovic E et al (2015) Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Comput Hum Behav 47:168–181CrossRefGoogle Scholar
  59. Yang C-N, Lin BMT, Hwang FJ et al (2016) Acquisition planning and scheduling of computing resources. Comput Oper Res 76:167–182MathSciNetCrossRefzbMATHGoogle Scholar
  60. Zhang H, Almeroth K, Knight A et al (2007) Moodog: Tracking students’ online learning activities. Paper presented at world conference on educational multimedia, hypermedia & telecommunications (ED-MEDIA), 25 June 2007, pp 4415–4422Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Young Researchers and Elite Club, Tabriz BranchIslamic Azad UniversityTabrizIran

Personalised recommendations