Automated Tutoring System

  • John Vong
  • Insu Song
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 11)


This chapter reports a new simulated smart learning environment, called Mobile Collaborative Experiential Learning (MCEL). MCEL provides automated, continuous, personalized, and formative feedback. Students interact with the simulated learning system via simple text messages using mobile devices in order to change the state of the system to a desired state over time. In the process, students engage in complex problem solving activities, and the system provides continuous formative assessment to help the students achieve learning objectives. Unlike conventional summative assessment approaches, students acquire set competency levels during their journey to achieving certain goals while interacting with the simulated system. MCEL allows instructors easily define learning journeys using event-condition-action (ECA) rules and reliable structured-text parser. A pilot study of MCEL is conducted and evaluated on a group of twenty Masters-in-IT students. Their participation is observed and a survey is conducted. The evaluation results show that MCEL supports Bigg’s constructive alignment in curriculum design, contextualized experimental learning, and personalized formative learning.


Mobile education M-learning SOA 2.0 ECA Automated tutoring 


  1. Biggs J, Tang C (2011) Teaching for quality learning at university. Open University Press, UKGoogle Scholar
  2. Boticki I, Wong LH, Looi CK (2013) Designing technology for content-independent collaborative mobile learning. IEEE Trans Learn Technol 6(1):14–24. doi: 10.1109/tlt.2012.8 CrossRefGoogle Scholar
  3. Brown AL (1992) Design experiments: theoretical and methodological challenges in creating complex interventions in classroom settings. J Learn Sci 2(2):141–178CrossRefGoogle Scholar
  4. Conole G (2004) E-learning: the hype and the reality. J Interact Media Educ 11Google Scholar
  5. Coulby C, Hennessey S, Davies N, Fuller R (2011) The use of mobile technology for work-based assessment: the student experience. Br J Educ Technol 42(2):251–265. doi: 10.1111/j.1467-8535.2009.01022.x CrossRefGoogle Scholar
  6. Devlin M (2012) Non-traditional student achievement: theory, policy and practice in Australian higher education. In: Proceedings of the 13th Pacific Rim first year in higher education conference 2010 on FYHE 2010: aspiration, access, achievement. Queensland University of Technology, pp 1–13Google Scholar
  7. ITU (2011) ITU world telecommunication/ICT International Telecommunications UnionGoogle Scholar
  8. Kneebone R, Brenton H (2005) Training perioperative specialist practitioners. Mobile learning: a handbook for educators and trainers, Routledge, Milton Park, pp 106–115Google Scholar
  9. Lefoe GE, Hedberg J (2006) Blending on and off campus: a tale of two citiesGoogle Scholar
  10. Manning CD, Schütze H (1999) Foundations of statistical natural language processing, vol 999. MIT Press, CambridgeGoogle Scholar
  11. McInnis C, Hartley R (2002) Managing study and work: the impact of full-time study and paid work on the undergraduate experience in Australian universitiesGoogle Scholar
  12. Ouyang C, Dumas M, Breutel S, ter Hofstede (2006) A translating standard process models to BPEL. In: Advanced information systems engineering. Springer, New York, pp 417–432Google Scholar
  13. Parr T (2007) The definitive ANTLR reference: building domain-specific languages. Pragmatic BookshelfGoogle Scholar
  14. Roche E, Schabes Y (1997) Finite-state language processing. The MIT Press, CambridgeGoogle Scholar
  15. Sharpe R, Benfield G, Roberts G, Francis R (2006) The undergraduate experience of blended e-learning: a review of UK literature and practice. Higher Education Academy, LondonGoogle Scholar
  16. Song YJ, Wong LH, Looi CK (2012) Fostering personalized learning in science inquiry supported by mobile technologies. Etr&D-Educ Technol Res Dev 60(4):679–701. doi: 10.1007/s11423-012-9245-6 CrossRefGoogle Scholar
  17. Ting YL (2013) Using mobile technologies to create interwoven learning interactions: an intuitive design and its evaluation. Comput Edu 60(1):1–13. doi: 10.1016/j.compedu.2012.07.004 CrossRefGoogle Scholar
  18. Zhang B, Looi C-K, Seow P, Chia G, Wong L-H, Chen W, So H-J, Soloway E, Norris C (2010) Deconstructing and reconstructing: transforming primary science learning via a mobilized curriculum. Comput Educ 55(4):1504–1523. doi:

Copyright information

© Springer Science+Business Media Singapore 2015

Authors and Affiliations

  1. 1.Financial IT AcademySingapore Management UniversitySingaporeSingapore
  2. 2.School of Business (IT)James Cook UniversitySingaporeSingapore

Personalised recommendations