Skip to main content
SpringerLink
Log in
Book cover

Object-Oriented User Interfaces for Personalized Mobile Learning pp 11–24Cite as

Related Work

  • Chapter
  • First Online:

  • 1037 Accesses

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 64))

Abstract

In this chapter we present related work in the scientific area of mobile learning and we also investigate current research efforts in the area of affective mobile computing. Furthermore, we give a brief overview of past works and approaches that have been based on Object-Oriented programming. As one may observe, mobile-learning, in its modern concept, traces its roots to the first decade of 2000. More specifically, a small number of mobile learning applications were built during the first half of the decade with a very high scientific and social interest in this area. Vincent (2009) states that over one half of the world’s population is expected to be using mobile phones by 2009 and many people have become attached to and even dependent on mobile devices. In the same study mobile phones are found to maintain close ties within peoples’ families and friends. As expected, during the last 5 years there has been a continuously increasing development, using modern technology, both in mobile software and hardware driven mostly by economic benefits.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Alepis E, Virvou M (2009) Emotional intelligence in multimodal object oriented user interfaces. Stud Comput Intell 226:349–359

    Google Scholar 

  • Alepis E, Virvou M (2010) Object oriented architecture for affective multimodal e-learning interfaces. Intell Decis Technol 4(3):171–180

    Google Scholar 

  • Aoidh EM (2006) Personalised multimodal interfaces for mobile geographic information systems, Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 4018 LNCS, 2006, pp 452–456

    Google Scholar 

  • Benz UC, Hofmann P, Willhauck G, Lingenfelder I, Heynen M (2004) Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogrammetry Remote Sens 58(3–4):239–258

    Article  Google Scholar 

  • Booch G (1996) The unified modeling language. Perform Comput/Unix Rev 14(13):41–48

    Google Scholar 

  • Brusilovsky P, Peylo C (2003) Adaptive and intelligent web-based educational systems. Int J Artif Intell Educ 13(2–4):159–172

    Google Scholar 

  • Busso C, Deng Z, Yildirim S, Bulut M, Lee C, Kazemzadeh A, Lee S, Neu-mann U, Narayanan S (2004) Analysis of emotion recognition using facial expressions, speech and multimodal infor-mation. In: Proceedings of the 6th international conference on multimodal interfaces, ACM: State College, PA, USA

    Google Scholar 

  • Cacioppo JT, Berntson GG, Larsen JT, Poehlmann KM, Ito TA (2000) The Psycho-physiology of emotion. In: Lewis M, Haviland-Jones JM (eds) Handbook of emotions. Guilford Press, NY, pp 173–191

    Google Scholar 

  • Caridakis G, Karpouzis K, Wallace M, Kessous L, Amir N (2010) Multimodal user’s affective state analysis in naturalistic interaction. J Multimodal User Interfaces 3(1):49–66

    Article  Google Scholar 

  • Cavus N, Uzunboylu H (2009) Improving critical thinking skills in mobile learning. In Procedia—Social and behavioral sciences, vol 1, Issue 1, 2009, pp 434–438

    Google Scholar 

  • Chen Y, Gao Y (2009) Research on mobile learning based on 3G technology. In: 7th international conference on web-based learning, ICWL 2008, Jinhua, pp 33–36

    Google Scholar 

  • Chittaro L (2010) Distinctive aspects of mobile interaction and their implications for the design of multimodal interfaces. J Multimodal User Interfaces 3(3):157–165

    Article  Google Scholar 

  • Cho Y-H, Park K-S, Pak RJ (2007) Speech emotion pattern recognition agent in mobile communication environment using fuzzy-SVM. Adv Soft Comput 40:419–430

    Google Scholar 

  • Cowie R, Douglas-Cowie E, Tsapatsoulis N, Votsis G, Kollias S, Fellenz W, Taylor J (2001) Emotion recognition in human-computer interaction. IEEE Signal Process Mag 18(1):32–80

    Article  Google Scholar 

  • De Sá M, Carriço L (2009) Supporting end-user development of personalized mobile learning tools. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 5613, Issue PART 4. LNCS, Portugal, pp 217–225

    Google Scholar 

  • De Silva L, Miyasato T, Nakatsu R (1997) Facial emotion recognition using multimodal informa-tion. In: IEEE international conference on information, communications and signal pro-cessing (ICICS’97), pp 397–401

    Google Scholar 

  • Du Boulay B (2000) Can we learn from ITSs?. In: Gauthier G, Frasson C, VanLehn K (eds) ITS 2000, LNCS 1839. Springer, Berlin, pp 9–17

    Google Scholar 

  • Elliott C, Rickel J, Lester J (1999) Lifelike pedagogical agents and affective computing: an exploratory synthesis. In: Wooldridge MJ, Veloso M (eds) Artificial intelligence today, LNCS 1600. Springer, Berlin, pp 195–212

    Google Scholar 

  • Farooq U, Shafer W, Rosson MB, Caroll JM (2002) M-education: bridging the gap of mobile and desktop computing. In: IEEE international workshop on wireless and mobile technologies in education (WMTE’02), pp 91−94

    Google Scholar 

  • Feng J, Johnston M, Bangalore S (2011) Speech and multimodal interaction in mobile search. In: Proceedings of the 20th international conference companion on world wide Web, pp 293–294

    Google Scholar 

  • Frohberg D, Göth C, Schwabe G (2009) Mobile learning projects – a critical analysis of the state of the art: original article. J Comput Assist Learn 25(4):307–331

    Article  Google Scholar 

  • Geven A, Tscheligi M, Noldus L (2009) Measuring mobile emotions: measuring the impossible? In: Proceeding of ACM international conference series. Article number 109

    Google Scholar 

  • Hasebrook JP, Gremm M (1999) Multimedia for vocational guidance: effects of individualised testing, videos and photography on acceptance and recall. J Educ Multimedia Hypermedia 8(4):377–400

    Google Scholar 

  • Huang TS, Chen LS, Tao H (1998) Bimodal emotion recognition by man and ma-chine. In: ATR workshop on virtual communication environments, Kyoto, Japan

    Google Scholar 

  • Hürst W, Van Wezel C (2011) Multimodal interaction concepts for mobile augmented reality applications. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 6524, Issue PART 2. LNCS, pp 157–167

    Google Scholar 

  • Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications, vol 186. Springer, Berlin

    Google Scholar 

  • Ketamo H (2003) An adaptive geometry game for handheld devices. Educ Technol Soc 6(1):83–95

    Google Scholar 

  • Kondratova I (2009) Multimodal interaction for mobile learning. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 5615, Issue PART 2, LNCS, pp 327–334

    Google Scholar 

  • Kuo Y-H, Huang Y-M (2009) MEAT: An authoring tool for generating adaptable learning resources. Educ Technol Soc 12(2):51–68

    MathSciNet  Google Scholar 

  • Lehner F, Nosekabel H (2002) The role of mobile devices in E-learning- first experiences with a wireless E-learning environment. In: IEEE international workshop on wireless and mobile technologies in education (WMTE’02), pp 103–106

    Google Scholar 

  • Liao W, Zhang W, Zhu Z, Ji Q, Gray WD (2006) Toward a decision-theoretic framework for affect recognition and user assistance. Int J Hum Comput Stud 64:847–873

    Google Scholar 

  • Lim MY, Aylett R (2007) Feel the difference: a guide with attitude!. Lecture notes in computer science, vol 4722. LNCS, pp 317–330

    Google Scholar 

  • Mäntyjärvi J, Paternò F, Santoro C (2007) Incorporating tilt-based interaction in multimodal user interfaces for mobile devices. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 4385. LNCS, pp 230–244

    Google Scholar 

  • Motiwalla LF (2007) Mobile learning: a framework and evaluation. Comput Educ 49(3):581–596

    Article  Google Scholar 

  • Motiwalla LF, Qin J (2007) Enhancing mobile learning using speech recognition technologies: a case study. In: Conference proceedings of 8th world congress on the management of e-business, WCMeB 2007, Article number 4285317

    Google Scholar 

  • Moulin C, Giroux S, Pintus A, Sanna R (2002) Mobile lessons using geo-referenced data in e-learning. In: Gouarderes SA, Paraguacu F (eds) Intelligent tutoring systems 2002, vol 2363. LNCS, p 1004

    Google Scholar 

  • Mueller W, Schaefer R, Bleul S (2004) Interactive multimodal user interfaces for mobile devices. In: Proceedings of the Hawaii international conference on system sciences, vol 37, 2004, Article number STMDI07, pp 4545–4554

    Google Scholar 

  • Nielek R, Wierzbicki A (2010) Emotion aware mobile application. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) vol 6422, Issue PART 2. LNAI pp 122–131

    Google Scholar 

  • Oviatt S (2000) Multimodal system processing in mobile environments, UIST (User Interface Software and Technology). In: Proceedings of the ACM symposium, pp 21–30

    Google Scholar 

  • Oviatt S (2003) User-modeling and evaluation of multimodal interfaces. In: Proceedings of the IEEE, pp 1457–1468

    Google Scholar 

  • Oviatt S, Lunsford R (2005) Multimodal interfaces for cell phones and mobile technology. Int J Speech Technol 8(2):127–132

    Article  Google Scholar 

  • Pantic M, Rothkrantz LJM (2003) Toward an affect-sensitive multimodal human-computer interaction. In: Proceedings of the IEEE, vol 91, pp 1370–1390

    Google Scholar 

  • Park Y-W, Lim C-Y, Nam T-J (2010) CheekTouch: an affective interaction technique while speaking on the mobile phone. In: Proceedings of conference on human factors in computing systems, pp 3241–3246

    Google Scholar 

  • Pastor O, Gómez J, Insfrán E, Pelechano V (2001) The OO-Method approach for information systems modeling: from object-oriented conceptual modeling to automated programming. Inf Syst 26(7):507–534

    Article  MATH  Google Scholar 

  • Picard, RW (2003) Affective computing: challenges. Int J Human-Comput Stud 59(1–2):55–64

    Google Scholar 

  • Platinum Technology Inc, Paradigm Plus: Round-Trip Engineering for JAVA, White Paper (1997)

    Google Scholar 

  • Rational Software Corporation, Rational Rose User’s Manual (1997)

    Google Scholar 

  • Razak AA, Abidin MIZ, Komiya R (2008) Voice driven emotion recognizer mobile phone: proposal and evaluations. Int J Inf Technol Web Eng 3(1):53–69

    Article  Google Scholar 

  • Réhman SU, Liu L (2008) Vibrotactile emotions on a mobile phone. In: Proceedings of the 4th international conference on signal image technology and internet based systems, SITIS 2008. Article number 4725810, pp 239–243

    Google Scholar 

  • Réhman SU, Liu L (2010) iFeeling: vibrotactile rendering of human emotions on mobile phones, lecture notes in computer science (including subseries lecture notes in artificial Intelligence and lecture notes in bioinformatics) vol 5960. LNCS, pp 1–20

    Google Scholar 

  • Reis T, De Sá M, Carriço L (2008) Multimodal interaction: real context studies on mobile digital artefacts. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 5270. LNCS, pp 60–69

    Google Scholar 

  • Rico J (2010) Evaluating the social acceptability of multimodal mobile interactions. In: Proceedings of conference on human factors in computing systems, 2010, pp 2887–2890

    Google Scholar 

  • Salomon G (1990) Studying the flute and the orchestra: controlled vs. classroom research on computers. Int J Educ Res 14:521–532

    Article  Google Scholar 

  • Self J (1999) The defining characteristics of intelligent tutoring systems research: itss care Precisely. Int J Artif Intel Educ 10:350–364

    Google Scholar 

  • Shieh C-K, Mac S-C, Chang T-C, Lai C-M (1996) An object-oriented approach to develop software fault-tolerant mechanisms for parallel programming systems. J Syst Softw 32(3):215–225

    Article  Google Scholar 

  • Sison R, Shimura M (1998) Student modelling and machine learning. Inter J Artif Intell Educ 9:128–158

    Google Scholar 

  • Spika M (2010) Synchronizing multimodal user input information in a modular mobile software platform. In: Proceedings of the international symposium on consumer electronics, ISCE 2010, Article number 5522701

    Google Scholar 

  • Turner M, Love S, Howell M (2008) Understanding emotions experienced when using a mobile phone in public: the social usability of mobile (cellular) telephones. Telematics Inform 25(3):201–215

    Google Scholar 

  • Uzunboylu H, Cavus N, Ercag E (2009) Using mobile learning to increase environmental awareness. Comput Educ 52(2):381–389

    Article  Google Scholar 

  • Vainio T (2009) Exploring multimodal navigation aids for mobile users. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 5726, Issue PART 1, LNCS, pp 853–865

    Google Scholar 

  • Vavoula G, Sharples M (2002) KleOS: a personal, mobile, knowledge and learning organisation system. In: IEEE international workshop on wireless and mobile technologies in education (WMTE’02), pp 152–155

    Google Scholar 

  • Vincent J (2009) Affiliations, emotion and the mobile phone, Lect Notes Comput Sci 5641 LNAI:28−41

    Google Scholar 

  • Virvou M (2002) A cognitive theory in an authoring tool for intelligent Tutoring systems. In: proceedings of IEEE international conference on systems man and cybernetics 2002 (SMC’02), vol 2, pp 410–415

    Google Scholar 

  • Virvou M, Kabassi K (2001) F-SMILE: an intelligent multi-agent learning environment. In: proceedings of IEEE international conference on advanced learning technologies 2002 (ICALT’02)

    Google Scholar 

  • Virvou M, Alepis E, Troussas C (2011) MMALL: multilingual mobile-assisted language learning. In: Proceedings of the 1st international symposium on business modeling and software design 2011—BMSD 2011, pp 129–135

    Google Scholar 

  • Wang Y-S, Wu M-C, Wang H-Y (2009) Investigating the determinants and age and gender differences in the acceptance of mobile learning. Br J Educ Technol 40(1):92–118

    Article  Google Scholar 

  • Weber G, Brusilovski P (2001) ELM-ART: an adaptive versatile system for web-based instruction. Int J Artif Intell Educ 12:351–384

    Google Scholar 

  • Welch M, Brownell K (2000) The development and evaluation of a multimedia course on educational collaboration. J Educ Multimedia Hypermedia 9(3):169–194

    Google Scholar 

  • Wenger E. (1987) Artificial intelligence and tutoring systems. Morgan Kaufmann, Los Altos

    Google Scholar 

  • Williamson J, Murray-Smith R, Hughes S (2007) Shoogle: excitatory multimodal interaction on mobile devices. In: Proceedings of conference on human factors in computing systems, 2007, pp 121–124

    Google Scholar 

  • Yoon WJ, Cho YH, Park KS (2007) A study of speech emotion recognition and its application to mobile services. Lecture notes in computer science, vol 4611. LNCS, pp 758–766

    Google Scholar 

  • Zeng Z, Tu J, Liu M, Huang T, Pianfetti B, Roth D, Levinson S (2007) Audio-visual affect recognition. IEEE Trans Multimedia 9:424–428

    Article  Google Scholar 

  • Zhan H-F, Deng H–J (2010) Study on a portable education administration assistant system. In: WRI international conference on communications and mobile computing, CMC 2010, vol 1, pp 555–558

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    Efthimios Alepis & Maria Virvou

Authors
  1. Efthimios Alepis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria Virvou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Efthimios Alepis .

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Alepis, E., Virvou, M. (2014). Related Work. In: Object-Oriented User Interfaces for Personalized Mobile Learning. Intelligent Systems Reference Library, vol 64. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53851-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-53851-3_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53850-6

  • Online ISBN: 978-3-642-53851-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation