Top

Hint

Swipe to navigate through the chapters of this book

Published in:

Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Opening Up an Intelligent Tutoring System Development Environment for Extensible Student Modeling

Authors : Kenneth Holstein, Zac Yu, Jonathan Sewall, Octav Popescu, Bruce M. McLaren, Vincent Aleven

Published in: Artificial Intelligence in Education

Publisher: Springer International Publishing

Abstract

ITS authoring tools make creating intelligent tutoring systems more cost effective, but few authoring tools make it easy to flexibly incorporate an open-ended range of student modeling methods and learning analytics tools. To support a cumulative science of student modeling and enhance the impact of real-world tutoring systems, it is critical to extend ITS authoring tools so they easily accommodate novel student modeling methods. We report on extensions to the CTAT/Tutorshop architecture to support a plug-in approach to extensible student modeling, which gives an author full control over the content of the student model. The extensions enhance the range of adaptive tutoring behaviors that can be authored and support building external, student- or teacher-facing real-time analytics tools. The contributions of this work are: (1) an open architecture to support the plugging in, sharing, re-mixing, and use of advanced student modeling techniques, ITSs, and dashboards; and (2) case studies illustrating diverse ways authors have used the architecture.

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 69.000 Bücher
über 500 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 50.000 Bücher
über 380 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 58.000 Bücher
über 300 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt 90 Tage mit der neuen Mini-Lizenz testen!

inform now

previous chapter Student Learning Benefits of a Mixed-Reality Teacher Awareness Tool in AI-Enhanced Classrooms

next chapter Better Late Than Never but Never Late Is Better: Towards Reducing the Answer Response Time to Questions in an Online Learning Community

Aleven, V., McLaren, B.M., Sewall, J., van Velsen, M., Popescu, O., Demi, S., Ringenberg, M., Koedinger, K.R.: Example-tracing tutors: intelligent tutor development for non-programmers. IJAIED 26, 224–269 (2016)

Aleven, V., Mclaughlin, E.A., Glenn, R.A., Koedinger, K.R.: Instruction based on adaptive learning technologies. In: Mayer, R.E., Alexander, P. (eds.) Handbook of Research on Learning and Instruction. Routledge, London (2017)

Aleven, V., Roll, I., McLaren, B.M., Koedinger, K.R.: Help helps, but only so much: research on help seeking with intelligent tutoring systems. IJAIED 26(1), 205–223 (2016)

Baker, R.S.J.D., et al.: Adapting to when students game an intelligent tutoring system. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 392–401. Springer, Heidelberg (2006). https://doi.org/10.1007/11774303_39 CrossRef

Blessing, S.B., Aleven, V., Gilbert, S., Heffernan, N.T., Matsuda, N., Mitrovic, A.: Authoring example-based tutors for procedural tasks. In: Design Recommendations for Adaptive Intelligent Tutoring Systems, pp. 71–93. US Army Research Laboratory (2015)

Cen, H., Koedinger, K., Junker, B.: Learning factors analysis – a general method for cognitive model evaluation and improvement. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 164–175. Springer, Heidelberg (2006). https://doi.org/10.1007/11774303_17 CrossRef

Clow, D.: The learning analytics cycle. In: LAK, pp. 134–138. ACM (2012)

Corbett, A.T., Anderson, J.R.: Knowledge tracing: modeling the acquisition of procedural knowledge. UMUAI 4(4), 253–278 (1995)

CT+A - Detector plugins. https://github.com/d19fe8/CTAT-detector-plugins/wiki/

10.

D’Mello, S.K., Lehman, B., Graesser, A.: A motivationally supportive affect-sensitive AutoTutor. In: Calvo, R.A., D’Mello, S.K. (eds.) New Perspectives on Affect and Learning Technologies, pp. 113–126. Springer, Heidelberg (2011). https://doi.org/10.1007/978-1-4419-9625-1 CrossRef

11.

Desmarais, M.C., Baker, R.S.J.D.: A review of recent advances in learner and skill modeling in intelligent learning environments. UMUAI 22, 9–38 (2012)

12.

Doroudi, S., Aleven, V., Brunskill, E.: Robust evaluation matrix: Towards a more principled offline exploration of instructional policies. In: L@S, pp. 3–12. ACM (2017)

13.

Doroudi, S., Holstein, K., Aleven, V., Brunskill, E.: Sequence matters, but how exactly? A method for evaluating activity sequences from data. In: EDM, pp. 70–77 (2016)

14.

Fancsali, S.: Causal discovery with models : behavior, affect, and learning in cognitive tutor algebra. In: EDM, pp. 28–35 (2014)

15.

Fancsali, S.E., Ritter, S., Stamper, J. and Nixon, T.: Toward “hyper-personalized” cognitive tutors: non-cognitive personalization in the generalized intelligent framework for tutoring. In: AIED, pp. 71–79 (2013)

16.

Grawemeyer, B., Holmes, W., Gutiérrez-Santos, S., Hansen, A., Loibl, K., Mavrikis, M.: Light-bulb moment? Towards adaptive presentation of feedback based on students’ affective state. In: IUI, pp. 400–404. ACM (2015)

17.

Holstein, K., Hong, G., Tegene, M., McLaren, B. M., Aleven, V.: The classroom as a dashboard: co-designing wearable cognitive augmentation for K-12 teachers. In: LAK, pp. 79–88. ACM (2018)

18.

Holstein, K., McLaren, B.M., Aleven, V.: SPACLE: investigating learning across virtual and physical spaces using spatial replays. In: LAK, pp. 358–367 (2017)

19.

Holstein, K., McLaren, B.M., Aleven, V.: Student learning benefits of a mixed-reality teacher awareness tool in AI-enhanced classrooms. In: AIED (2018, to appear)

20.

Holstein, K., Xhakaj, F., Aleven, V., Mclaren, B.M.: Luna: a dashboard for teachers using intelligent tutoring systems. In: IWTA@EC-TEL (2016)

21.

Kai, S., Almeda, V.A., Baker, R.S., Shechtman, N., Heffernan, C., Heffernan, N.: Modeling wheel-spinning and productive persistence in Skill Builders. JEDM (2018, to appear)

22.

Kery, M.B., Myers, B.A.: Exploring exploratory programming. In: VL/HCC, pp. 25–29 (2018)

23.

Khajah, M., Lindsey, R.V., Mozer, M.C.: How deep is knowledge tracing? In: EDM, pp. 94–101 (2015)

24.

Koedinger, K.R., Baker, R.S.J.D., Cunningham, K., Skogsholm, A., Leber, B., Stamper, J.: A data repository for the EDM community: the PSLC DataShop. In: Handbook of Educational Data Mining, pp. 43–56. CRC Press, Boca Raton (2010)

25.

Liu, Z., Pataranutaporn, V., Ocumpaugh, J., Baker, R.S.J.D.: Sequences of frustration and confusion, and learning. In: EDM, pp. 114–120 (2013)

26.

Long, Y., Aleven, V.: Supporting students’ self-regulated learning with an open learner model in a linear equation tutor. In: AIED, pp. 219–228 (2013)

27.

MacLellan, C.J., Koedinger, K.R., Matsuda, N.: Authoring tutors with SimStudent: an evaluation of efficiency and model quality. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 551–560. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07221-0_70 CrossRef

28.

Microsoft HoloLens. https://www.microsoft.com/en-us/hololens

29.

Mitrovic, A., Martin, B., Suraweera, P., Zakharov, K., Milik, N., Holland, J., McGuigan, N.: ASPIRE: an authoring system and deployment environment for constraint-based tutors. IJAIED 19(2), 155–188 (2009)

30.

Node.js. https://nodejs.org/en/

31.

Olsen, J.K., Belenky, D.M., Aleven, V., Rummel, N., Sewall, J., Ringenberg, M.: Authoring tools for collaborative intelligent tutoring system environments. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 523–528. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07221-0_66 CrossRef

32.

Paquette, L., Baker, R.S., Moskal, M.: A system-general model for the detection of gaming the system behavior in CTAT and LearnSphere. In: AIED (2018, to appear)

33.

Pavlik, P.I., Cen, H., Koedinger, K.R.: Performance factors analysis - a new alternative to knowledge tracing, pp. 531–538. IOS Press, Amsterdam (2009)

34.

Pelánek, R., Řihák, J.: Experimental analysis of mastery learning criteria. In: UMAP, pp. 156–163 (2017)

35.

Razzaq, L., Patvarczki, J., Almeida, S.F., Vartak, M., Feng, M., Heffernan, N.T., Koedinger, K.R.: The assistment builder: supporting the life cycle of tutoring system content creation. IEEE TLT 2(2), 157–166 (2009)

36.

Ritter, S., Koedinger, K.: Towards lightweight tutoring agents. In: AIED, pp. 16–19 (1995)

37.

Sottilare, R.A., Baker, R.S., Graesser, A.C., Lester, J.C.: Special issue on the Generalized Intelligent Framework for Tutoring (GIFT): creating a stable and flexible platform for innovations in AIED research. In: IJAIED, pp. 1–13 (2017)

38.

Stamper, J., Koedinger, K., Pavlik Jr, P.I., Rose, C., Liu, R., Eagle, M., Veeramachaneni, K.: Educational data analysis using LearnSphere. In: EDM 2016 Workshops and Tutorials (2016)

39.

VanLehn, K.: The behavior of tutoring systems. IJAIED 16(3), 227–265 (2006)

40.

VanLehn, K.: Regulative loops, step loops and task loops. IJAIED 26(1), 107–112 (2016)

41.

Vatrapu, R.K., Kocherla, K., Pantazos, K.: iKlassroom: real-time, real-place teaching analytics. In: IWTA@LAK (2013)

42.

Xhakaj, F., Aleven, V., McLaren, B.M.: Effects of a teacher dashboard for an intelligent tutoring system on teacher knowledge, lesson planning, lessons and student learning. In: EC-TEL, pp. 315–329 (2017)

43.

Yudelson, M.V., Koedinger, K.R., Gordon, G.J.: Individualized Bayesian knowledge tracing models. In: AIED, pp. 171–180 (2013)

Title: Opening Up an Intelligent Tutoring System Development Environment for Extensible Student Modeling
Authors: Kenneth Holstein
Zac Yu
Jonathan Sewall
Octav Popescu
Bruce M. McLaren
Vincent Aleven
Publisher: Springer International Publishing
Book: Artificial Intelligence in Education
Print ISBN: 978-3-319-93842-4

Electronic ISBN: 978-3-319-93843-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-93843-1_13

Springer Professional

Hint

Swipe to navigate through the chapters of this book

Abstract

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner