Goal-oriented adaptive and extensible study-process creation with optimal cyclic-learning in graph-structured knowledge

Ryosuke Konishi, Fumito Nakamura, Yasushi Kiyoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We herein present a method that dynamically generates the curricula specialized to the learning circumstances of individual learners, given prior learning goals and learning objects. A generated curriculum encourages the selection of learning behaviors according to the learning objects that may be feasibly acquired within a limited timeframe. Our method evaluates the circumstances of an individual learner; by dynamically selecting feasible learning objects based on the individual's learning behaviors and their past records, it finds the best learning tasks within the constraints of time, circumstances, and activities. Using prior known rules and strengths of causal/dependency relations between learning items, our method enables, from individual test results, the discovery of the learning objects that are important, and how they should be ordered, on an individual basis. This enables an effective support in choosing the most appropriate learning behaviors, tailored to the individual learner. It also enables the selection of effective learning behaviors by examining the behavior records of other individuals, treating the influence of their prior learning behaviors on subsequent learning behaviors as experience quotients and using them by converting them into expected scores for the individual's learning behaviors. Accordingly, we evaluate whether the learning behaviors selected by the individual are indeed learning tasks that would correspond to anticipated learning results, conduct prior assessment of the influence that the results of this intervention would have upon the learning circumstances and thus prioritize more effective learning behaviors. When implemented, our method assesses the changes in the individual's learning circumstances based on their learning behaviors on a timeline and subsequently adjusts the recommended behaviors. The method can provide effective support for individual learners: Along with effective feedback on learning task selection in response to the individual's circumstances, it dynamically generates an individualized curriculum by measuring the relationships between the individual's learning circumstances and learning items. We herein present a method for dynamically generating the curricula in response to an individual's learning circumstances through measuring the causal/dependency relations between learning items, thus enabling the calculation of the relationship between an individual's past learning record, and the learning behaviors and learning objects available to be chosen by the individual. We investigate its efficacy and achievability through empirical testing by using actual data.

Original languageEnglish
Title of host publicationInformation Modelling and Knowledge Bases XXX
EditorsTatiana Endrjukaite, Hannu Jaakkola, Alexander Dudko, Yasushi Kiyoki, Bernhard Thalheim, Naofumi Yoshida
PublisherIOS Press
Pages1-19
Number of pages19
ISBN (Electronic)9781614999324
DOIs
Publication statusPublished - 2019 Jan 1

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume312
ISSN (Print)0922-6389

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Curricula
Feedback
Testing

Keywords

  • adaptive leaning
  • causal inference
  • graph theory
  • Learning management system
  • mathematical model of meaning
  • reinforce leaning

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Konishi, R., Nakamura, F., & Kiyoki, Y. (2019). Goal-oriented adaptive and extensible study-process creation with optimal cyclic-learning in graph-structured knowledge. In T. Endrjukaite, H. Jaakkola, A. Dudko, Y. Kiyoki, B. Thalheim, & N. Yoshida (Eds.), Information Modelling and Knowledge Bases XXX (pp. 1-19). (Frontiers in Artificial Intelligence and Applications; Vol. 312). IOS Press. https://doi.org/10.3233/978-1-61499-933-1-1

Goal-oriented adaptive and extensible study-process creation with optimal cyclic-learning in graph-structured knowledge. / Konishi, Ryosuke; Nakamura, Fumito; Kiyoki, Yasushi.

Information Modelling and Knowledge Bases XXX. ed. / Tatiana Endrjukaite; Hannu Jaakkola; Alexander Dudko; Yasushi Kiyoki; Bernhard Thalheim; Naofumi Yoshida. IOS Press, 2019. p. 1-19 (Frontiers in Artificial Intelligence and Applications; Vol. 312).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Konishi, R, Nakamura, F & Kiyoki, Y 2019, Goal-oriented adaptive and extensible study-process creation with optimal cyclic-learning in graph-structured knowledge. in T Endrjukaite, H Jaakkola, A Dudko, Y Kiyoki, B Thalheim & N Yoshida (eds), Information Modelling and Knowledge Bases XXX. Frontiers in Artificial Intelligence and Applications, vol. 312, IOS Press, pp. 1-19. https://doi.org/10.3233/978-1-61499-933-1-1
Konishi R, Nakamura F, Kiyoki Y. Goal-oriented adaptive and extensible study-process creation with optimal cyclic-learning in graph-structured knowledge. In Endrjukaite T, Jaakkola H, Dudko A, Kiyoki Y, Thalheim B, Yoshida N, editors, Information Modelling and Knowledge Bases XXX. IOS Press. 2019. p. 1-19. (Frontiers in Artificial Intelligence and Applications). https://doi.org/10.3233/978-1-61499-933-1-1
Konishi, Ryosuke ; Nakamura, Fumito ; Kiyoki, Yasushi. / Goal-oriented adaptive and extensible study-process creation with optimal cyclic-learning in graph-structured knowledge. Information Modelling and Knowledge Bases XXX. editor / Tatiana Endrjukaite ; Hannu Jaakkola ; Alexander Dudko ; Yasushi Kiyoki ; Bernhard Thalheim ; Naofumi Yoshida. IOS Press, 2019. pp. 1-19 (Frontiers in Artificial Intelligence and Applications).
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