Cognitive feedback and behavioral feedforward automation perspectives for modeling and validation in a learning context

Gayane Sedrakyan; Monique Snoeck

doi:10.1007/978-3-319-66302-9_4

Cognitive feedback and behavioral feedforward automation perspectives for modeling and validation in a learning context

Gayane Sedrakyan
, Monique Snoeck

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

22 Citations (Scopus)

Abstract

State-of-the-art technologies have made it possible to provide a learner with immediate computer-assisted feedback by delivering a feedback targeting cognitive aspects of learning, (e.g. reflecting on a result, explaining a concept, i.e. improving understanding). Fast advancement of technology has recently generated increased interest for previously non-feasible approaches for providing feedback based on learning behavioral observations by exploiting different traces of learning processes stored in information systems. Such learner behavior data makes it possible to observe different aspects of learning processes in which feedback needs of learners (e.g. difficulties, engagement issues, inefficient learning processes, etc.) based on individual learning trajectories can be traced. By identifying problems earlier in a learning process it is possible to deliver individualized feedback helping learners to take control of their own learning, i.e. to become self-regulated learners, and teachers to understand individual feedback needs and/or adapt their teaching strategies. In this work we (i) propose cognitive computer-assisted feedback mechanisms using a combination of MDE based simulation augmented with automated feedback, and (ii) discuss perspectives for behavioral feedback, i.e. feedforward, that can be based on learning process analytics in the context of learning conceptual modeling. Aggregated results of our previous studies assessing the effectiveness of the proposed cognitive feedback method with respect to improved understanding on different dimensions of knowledge, as well as feasibility of behavioral feedforward automation based on learners behavior patterns, are presented. Despite our focus on conceptual modeling and specific diagrams, the principles of the approach presented in this work can be used to support educational feedback automation for a broader spectrum of diagram types beyond the scope of conceptual modeling.

Original language	English
Title of host publication	Model-Driven Engineering and Software Development
Subtitle of host publication	4th International Conference, MODELSWARD 2016, Rome, Italy, February 19-21, 2016, Revised Selected Papers
Editors	Slimane Hammoudi, Luis Ferreira Pires, Bran Selic, Philippe Desfray
Place of Publication	Cham
Publisher	Springer
Pages	70-92
Number of pages	23
ISBN (Electronic)	978-3-319-66302-9
ISBN (Print)	978-3-319-66301-2
DOIs	https://doi.org/10.1007/978-3-319-66302-9_4
Publication status	E-pub ahead of print/First online - 10 Sept 2017
Externally published	Yes
Event	4th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2016 - Rome, Italy Duration: 19 Feb 2016 → 21 Feb 2016 Conference number: 4 https://modelsward.scitevents.org/?y=2016

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer Nature
Volume	692
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	4th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2016
Abbreviated title	MODELSWARD 2016
Country/Territory	Italy
City	Rome
Period	19/02/16 → 21/02/16
Internet address	https://modelsward.scitevents.org/?y=2016

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

n/a OA procedure
Model driven development
Simulation
Simulation feedback
Rapid prototyping
Model testing/validation
Feedback automation
Learning process analysis
Smart learning environments
Conceptual modeling

Access to Document

10.1007/978-3-319-66302-9_4

Cite this

Sedrakyan, G., & Snoeck, M. (2017). Cognitive feedback and behavioral feedforward automation perspectives for modeling and validation in a learning context. In S. Hammoudi, L. Ferreira Pires, B. Selic, & P. Desfray (Eds.), Model-Driven Engineering and Software Development: 4th International Conference, MODELSWARD 2016, Rome, Italy, February 19-21, 2016, Revised Selected Papers (pp. 70-92). (Communications in Computer and Information Science; Vol. 692). Springer. Advance online publication. https://doi.org/10.1007/978-3-319-66302-9_4

Sedrakyan, Gayane ; Snoeck, Monique. / Cognitive feedback and behavioral feedforward automation perspectives for modeling and validation in a learning context. Model-Driven Engineering and Software Development: 4th International Conference, MODELSWARD 2016, Rome, Italy, February 19-21, 2016, Revised Selected Papers. editor / Slimane Hammoudi ; Luis Ferreira Pires ; Bran Selic ; Philippe Desfray. Cham : Springer, 2017. pp. 70-92 (Communications in Computer and Information Science).

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title = "Cognitive feedback and behavioral feedforward automation perspectives for modeling and validation in a learning context",

abstract = "State-of-the-art technologies have made it possible to provide a learner with immediate computer-assisted feedback by delivering a feedback targeting cognitive aspects of learning, (e.g. reflecting on a result, explaining a concept, i.e. improving understanding). Fast advancement of technology has recently generated increased interest for previously non-feasible approaches for providing feedback based on learning behavioral observations by exploiting different traces of learning processes stored in information systems. Such learner behavior data makes it possible to observe different aspects of learning processes in which feedback needs of learners (e.g. difficulties, engagement issues, inefficient learning processes, etc.) based on individual learning trajectories can be traced. By identifying problems earlier in a learning process it is possible to deliver individualized feedback helping learners to take control of their own learning, i.e. to become self-regulated learners, and teachers to understand individual feedback needs and/or adapt their teaching strategies. In this work we (i) propose cognitive computer-assisted feedback mechanisms using a combination of MDE based simulation augmented with automated feedback, and (ii) discuss perspectives for behavioral feedback, i.e. feedforward, that can be based on learning process analytics in the context of learning conceptual modeling. Aggregated results of our previous studies assessing the effectiveness of the proposed cognitive feedback method with respect to improved understanding on different dimensions of knowledge, as well as feasibility of behavioral feedforward automation based on learners behavior patterns, are presented. Despite our focus on conceptual modeling and specific diagrams, the principles of the approach presented in this work can be used to support educational feedback automation for a broader spectrum of diagram types beyond the scope of conceptual modeling.",

keywords = "n/a OA procedure, Model driven development, Simulation, Simulation feedback, Rapid prototyping, Model testing/validation, Feedback automation, Learning process analysis, Smart learning environments, Conceptual modeling",

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booktitle = "Model-Driven Engineering and Software Development",

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Sedrakyan, G & Snoeck, M 2017, Cognitive feedback and behavioral feedforward automation perspectives for modeling and validation in a learning context. in S Hammoudi, L Ferreira Pires, B Selic & P Desfray (eds), Model-Driven Engineering and Software Development: 4th International Conference, MODELSWARD 2016, Rome, Italy, February 19-21, 2016, Revised Selected Papers. Communications in Computer and Information Science, vol. 692, Springer, Cham, pp. 70-92, 4th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2016, Rome, Italy, 19/02/16. https://doi.org/10.1007/978-3-319-66302-9_4

Cognitive feedback and behavioral feedforward automation perspectives for modeling and validation in a learning context. / Sedrakyan, Gayane; Snoeck, Monique.
Model-Driven Engineering and Software Development: 4th International Conference, MODELSWARD 2016, Rome, Italy, February 19-21, 2016, Revised Selected Papers. ed. / Slimane Hammoudi; Luis Ferreira Pires; Bran Selic; Philippe Desfray. Cham: Springer, 2017. p. 70-92 (Communications in Computer and Information Science; Vol. 692).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

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AU - Sedrakyan, Gayane

AU - Snoeck, Monique

N1 - Conference code: 4

PY - 2017/9/10

Y1 - 2017/9/10

N2 - State-of-the-art technologies have made it possible to provide a learner with immediate computer-assisted feedback by delivering a feedback targeting cognitive aspects of learning, (e.g. reflecting on a result, explaining a concept, i.e. improving understanding). Fast advancement of technology has recently generated increased interest for previously non-feasible approaches for providing feedback based on learning behavioral observations by exploiting different traces of learning processes stored in information systems. Such learner behavior data makes it possible to observe different aspects of learning processes in which feedback needs of learners (e.g. difficulties, engagement issues, inefficient learning processes, etc.) based on individual learning trajectories can be traced. By identifying problems earlier in a learning process it is possible to deliver individualized feedback helping learners to take control of their own learning, i.e. to become self-regulated learners, and teachers to understand individual feedback needs and/or adapt their teaching strategies. In this work we (i) propose cognitive computer-assisted feedback mechanisms using a combination of MDE based simulation augmented with automated feedback, and (ii) discuss perspectives for behavioral feedback, i.e. feedforward, that can be based on learning process analytics in the context of learning conceptual modeling. Aggregated results of our previous studies assessing the effectiveness of the proposed cognitive feedback method with respect to improved understanding on different dimensions of knowledge, as well as feasibility of behavioral feedforward automation based on learners behavior patterns, are presented. Despite our focus on conceptual modeling and specific diagrams, the principles of the approach presented in this work can be used to support educational feedback automation for a broader spectrum of diagram types beyond the scope of conceptual modeling.

AB - State-of-the-art technologies have made it possible to provide a learner with immediate computer-assisted feedback by delivering a feedback targeting cognitive aspects of learning, (e.g. reflecting on a result, explaining a concept, i.e. improving understanding). Fast advancement of technology has recently generated increased interest for previously non-feasible approaches for providing feedback based on learning behavioral observations by exploiting different traces of learning processes stored in information systems. Such learner behavior data makes it possible to observe different aspects of learning processes in which feedback needs of learners (e.g. difficulties, engagement issues, inefficient learning processes, etc.) based on individual learning trajectories can be traced. By identifying problems earlier in a learning process it is possible to deliver individualized feedback helping learners to take control of their own learning, i.e. to become self-regulated learners, and teachers to understand individual feedback needs and/or adapt their teaching strategies. In this work we (i) propose cognitive computer-assisted feedback mechanisms using a combination of MDE based simulation augmented with automated feedback, and (ii) discuss perspectives for behavioral feedback, i.e. feedforward, that can be based on learning process analytics in the context of learning conceptual modeling. Aggregated results of our previous studies assessing the effectiveness of the proposed cognitive feedback method with respect to improved understanding on different dimensions of knowledge, as well as feasibility of behavioral feedforward automation based on learners behavior patterns, are presented. Despite our focus on conceptual modeling and specific diagrams, the principles of the approach presented in this work can be used to support educational feedback automation for a broader spectrum of diagram types beyond the scope of conceptual modeling.

KW - n/a OA procedure

KW - Model driven development

KW - Simulation

KW - Simulation feedback

KW - Rapid prototyping

KW - Model testing/validation

KW - Feedback automation

KW - Learning process analysis

KW - Smart learning environments

KW - Conceptual modeling

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M3 - Conference contribution

SN - 978-3-319-66301-2

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A2 - Hammoudi, Slimane

A2 - Ferreira Pires, Luis

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A2 - Desfray, Philippe

PB - Springer

CY - Cham

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ER -

Sedrakyan G, Snoeck M. Cognitive feedback and behavioral feedforward automation perspectives for modeling and validation in a learning context. In Hammoudi S, Ferreira Pires L, Selic B, Desfray P, editors, Model-Driven Engineering and Software Development: 4th International Conference, MODELSWARD 2016, Rome, Italy, February 19-21, 2016, Revised Selected Papers. Cham: Springer. 2017. p. 70-92. (Communications in Computer and Information Science). Epub 2017 Sept 10. doi: 10.1007/978-3-319-66302-9_4

Cognitive feedback and behavioral feedforward automation perspectives for modeling and validation in a learning context

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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