research-article

An artificial intelligence case based approach to motivational students assessment in (e)-learning environments

Authors:

Liliana Ávidos,

Isabel Araújo,

Margarida FigueiredoAuthors Info & Claims

IC4E '19: Proceedings of the 10th International Conference on E-Education, E-Business, E-Management and E-Learning

Pages 1 - 6

https://doi.org/10.1145/3306500.3306515

Published: 10 January 2019 Publication History

Abstract

In the last decades effective teaching and learning and e-learning environments have been performed in order to construct courses jointly with the collaboration with Industry and High-Level Educational Institutions. On another way there are several terminologies that attempt to specify the best teaching and learning methods applied to engineering, from problem-based learning, project-based learning, work-based learning, team-learning, self-direct learning for example. However motivational studies and motivational scales typically discard uncertainty characteristic in for quantitatively evaluating the different dimensions on student's motivational assessment in (e)-learning environments. This paper presents a computerized framework grounded on Artificial Intelligence techniques, namely the Case Based Reasoning approach for problem solving, complemented with a Knowledge Representation and Reasoning method that considers unknown, incomplete or even self-contradictory data or knowledge in the motivational student's assessment.

References

[1]

Hmelo-Silver C. E. 2004. Problem-Based Learning: What and How Do Students Learn?. Educational Psychology Review. 16, 3, 235--266.

[2]

Pike, J., Spangler, W., Williams, V., and Kollar, R. 2017. Role-Playing and Problem-Based Learning: The Use of Cross-Functional Student Teams in Business Application Development. Information Systems Education Journal 15, 4 (July 2017), 75--83.

[3]

Requies, J., Agirre, I., Barrio, L., and Graells, M. 2018. Evolution of Project-Based Learning in Small Groups in Environmental Engineering Courses. Journal of Technology and Science Education 8, 1 (), 45--62.

[4]

Stolk, J., and Martello, R. 2015. Can Disciplinary Integration Promote Students' Lifelong Learning Attitudes and Skills in Project-Based Engineering Courses?. International Journal of Engineering Education, 31, 1B, 434--449.

[5]

European Commission 2013. Work-Based Learning in Europe - Practices and Policy Pointers in Vocational Education and Training. http://ec.europa.eu/dgs/education_culture/repository/education/policy/vocational-policy/doc/alliance/work-based-learning-in-europe_en.pdf. Last accessed 2018/06/15.

[6]

Stefanou, C., Stolk, J., Prince, M., Chen, J., and Lord, S. 2013. Self-regulation and autonomy in problem- and project-based learning environments. Active Learning in Higher Education 14, 2, 109--122.

[7]

Lord, S., Prince, M., Stefanou C., Stolk, J., and Chen, J. 2012. The Effect of Different Active Learning Environments on Students Outcomes Related to Lifelong Learning. International Journal of Engineering Education 28, 3, 606--620.

[8]

Hattinger, M., and Eriksson, K. 2018. Co-construction of Knowledge in Work-Integrated E-learning Courses in Joint Industry-University Collaboration. International Journal of Advanced Corporate Learning 11, 1, 10--16.

[9]

Spector, J. 2016. Smart Learning Environments: Concepts and Issues. In Proceedings of Society for Information Technology & Teacher Education International Conference (Savannah, GA, USA, March 21, 2016). G. Chamblee and L. Langub Eds. Association for the Advancement of Computing in Education, Chesapeake, VA, 2728--2737.

[10]

Shrivastava, R., Jain, Y. K., and Sachan, A. K. 2013. Designing and Developing e-Learning Solution: Study on Moodle 2.0. International Journal of Machine Learning and Computing 3, 3 (June 2013), 305--308.

[11]

Stolk, J., and Harari, J. 2014. Student motivations as predictors of high-level cognitions in project based classrooms. Active Learning in Higher Education Journal 15, 3, 231.247.

[12]

Efklides, A. 2011. Interactions of Metacognition with Motivation and Affect in Self-Regulated Learning: The MASRL Model. Educational Psychologist Journal 46, 1, 6--25.

[13]

Butz, N., and Stupnisky, R. 2016. A mixed methods study of graduate students' self-determined motivation in synchronous hybrid learning environments. Internet and Higher Education 28, 85--95.

[14]

Maferima T., and Ayelet, F. 2014. How to Measure Motivation: A Guide for the Experimental Social Psychologist. Social and Personality Psychology Compass 8/7, 328--341

[15]

Bernik, A., Bubaš, G., and Radošević, D. 2018. Measurement of the effects of e-learning courses gamification on motivation and satisfaction of students. In Proceedings of the 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (Opatija, Croatia, May 21 -- 25, 2018) MTPRO' 2018. IEEE, Los Alamitos, CA, 806--811.

[16]

Fryer, L., and Bovee, N. 2016. Supporting students' motivation for e-learning: Teachers matter on and offline. The Internet and Higher Education Journal 30, 21--29.

[17]

Neves, J., Vicente, H., Ferraz, F., Leite, A.C., Rodrigues, A.R., Cruz, M., Machado, J., Neves, J., and Sampaio, L. 2018. A Deep Learning approach to Case Based Reasoning to the Evaluation and Diagnosis of Cervical Carcinoma. In Modern Approaches to Intelligent Information and Database Systems. Springer Studies in Computational Intelligence Series. Springer, Cham, 185--197.

[18]

Richter, M., and Weber, R. 2013. Case-Based Reasoning: A Textbook. Springer, Berlin.

Digital Library

[19]

Chen, K., Stolk, J., and Herter, R. 2015. Moving from Quantitative to Qualitative Analysis to Capture the Development of Self-Directed Learning for a Cohort of Engineering Students. In Proceedings of the 122<sup>nd</sup> ASEE Annual Conference & Exposition (Seattle, WA, June 14 - 17, 2015). American Society for Engineering Education, USA, Paper ID #13425.

[20]

Fernandes, F., Vicente, H., Abelha, A., Machado, J., Novais, P., and Neves J. 2015. Artificial Neural Networks in Diabetes Control. In Proceedings of the 2015 Science and Information Conference (London, United Kindgon, July 28 - 30, 2015). IEEE, Los Alamitos, CA, 362--370.

Cited By

Sembey RHoda RGrundy J(2024)Emerging technologies in higher education assessment and feedback practices: A systematic literature reviewJournal of Systems and Software10.1016/j.jss.2024.111988211(111988)Online publication date: May-2024
https://doi.org/10.1016/j.jss.2024.111988
Acuna GAlvarez LMiraflores JSamonte M(2021)Towards the Development of an Adaptive E-learning System with Chatbot Using Personalized E-learning ModelProceedings of the 7th International Conference on Frontiers of Educational Technologies10.1145/3473141.3473236(120-125)Online publication date: 4-Jun-2021
https://dl.acm.org/doi/10.1145/3473141.3473236
MOURALI YFARHAT RAGREBI MJEMNI MKOLSKI CEZZEDINE H(2021)An educational decision support system: case of learners clustering2021 8th International Conference on ICT & Accessibility (ICTA)10.1109/ICTA54582.2021.9809425(1-3)Online publication date: 8-Dec-2021
https://doi.org/10.1109/ICTA54582.2021.9809425

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An artificial intelligence case based approach to motivational students assessment in (e)-learning environments

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cover image ACM Other conferences

IC4E '19: Proceedings of the 10th International Conference on E-Education, E-Business, E-Management and E-Learning

January 2019

469 pages

ISBN:9781450366021

DOI:10.1145/3306500

Conference Chairs:
Kuan-Chou Chen
Purdue University Northwest
,
Yongsheng Ma
University of Alberta, Canada

Copyright © 2019 ACM.

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Published: 10 January 2019

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IC4E 2019

IC4E 2019: 2019 10th International Conference on E-Education, E-Business, E-Management and E-Learning

January 10 - 13, 2019

Tokyo, Japan

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Cited By

Sembey RHoda RGrundy J(2024)Emerging technologies in higher education assessment and feedback practices: A systematic literature reviewJournal of Systems and Software10.1016/j.jss.2024.111988211(111988)Online publication date: May-2024
https://doi.org/10.1016/j.jss.2024.111988
Acuna GAlvarez LMiraflores JSamonte M(2021)Towards the Development of an Adaptive E-learning System with Chatbot Using Personalized E-learning ModelProceedings of the 7th International Conference on Frontiers of Educational Technologies10.1145/3473141.3473236(120-125)Online publication date: 4-Jun-2021
https://dl.acm.org/doi/10.1145/3473141.3473236
MOURALI YFARHAT RAGREBI MJEMNI MKOLSKI CEZZEDINE H(2021)An educational decision support system: case of learners clustering2021 8th International Conference on ICT & Accessibility (ICTA)10.1109/ICTA54582.2021.9809425(1-3)Online publication date: 8-Dec-2021
https://doi.org/10.1109/ICTA54582.2021.9809425

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