ABSTRACT
The accuracy of learner model is the heart of any Intelligent Tutoring System (ITS). More intelligence in the ITS needs a more accurate learner model. In the earlier versions of ITS, the student must submit a test before using the ITS. That test was used to build the student model, which contains information about the knowledge of the student, his/her misconceptions, preferences and other related issues. However, this method doesn't work efficiently for school students, because one test canfit accurately evaluate their knowledge and misconceptions. In this research, we implement a system (web application) to get the student model for school students by allowing the students, parents, and instructors to add their assessment and feedback to the model. Then the system uses these multi-entries together with the traditional test to build an enhanced student model (smart learner model). Furthermore, in order to support collaborative learning, the implemented system gives the student the access to open his/her model for other instructors and peers. The proposed system has been applied on a group of students, their parents and instructors. According to the obtained results and the surveys, the studentfis knowledge has been improved in many students. also the students, parents, instructors found the system to be useful, interesting and easy to use. Furthermore, all parties were happy to be engaged in the educational process.
- Hussein Ahmad. 2016. Knowledge Development, ICT Management, and Education Policy: Global Issues Local Challenges. In Fast forwarding Higher Education Institutions for Global Challenges. Springer, 163--171.Google Scholar
- Nilufar Baghaei. 2007. A collaborative constraint-based intelligent system for learning object-oriented analysis and design using UML. PhD thesis, University of Canterbury, New-Zealand (2007).Google Scholar
- Bryan Bergeron. 2013. Intelligent tutoring system. (Jan. 22 2013). US Patent 8,356,997.Google Scholar
- Susan Bull, Abdallatif S Abu-Issa, Harpreet Ghag, and Tim Lloyd. 2005. Some Unusual Open Learner Models.. In AIED. 104--111. Google ScholarDigital Library
- Susan Bull, Andrew Mabbott, and Abdallatif S Abu Issa. 2007. UMPTEEN: Named and anonymous learner model access for instructors and peers. International Journal of Artificial Intelligence in Education 17, 3 (2007), 227--253. Google ScholarDigital Library
- Susan Bull, Manveer Mangat, Andrew Mabbott, AS Abu Issa, and Josie Marsh. 2005. Reactions to inspectable learner models: seven year olds to University students. In Proceedings of Workshop on Learner Modelling for Reflection, International Conference on Artificial Intelligence in Education. 1--10.Google Scholar
- Hugh Burns, Carol A Luckhardt, James W Parlett, and Carol L Redfield. 2014. Intelligent tutoring systems: Evolutions in design. Psychology Press.Google Scholar
- Vatcharaporn Esichaikul, Supaporn Lamnoi, and Clemens Bechter. 2011. Student Modelling in Adaptive E-Learning Systems. Knowledge Management & E-Learning: An International Journal (KM&EL) 3, 3 (2011), 342--355.Google Scholar
- Jodie L Ferguson, Suzanne C Makarem, and Rebecca E Jones. 2016. Using a class blog for student experiential learning reflection in business courses. Journal of Education for Business 91, 1 (2016), 1--10.Google ScholarCross Ref
- Trude Heift and Devlan Nicholson. 2001. Web delivery of adaptive and interactive language tutoring. International Journal of Artificial Intelligence in Education 12, 4 (2001), 310--325.Google Scholar
- Antonija Mitrovic and K Hausler. 2000. Porting SQL-Tutor to the web. In Proc. ITS2000 workshop on Adaptive and Intelligent Web-based Education Systems. 37--44.Google Scholar
- Hyacinth S Nwana. 1990. Intelligent tutoring systems: an overview. Artificial Intelligence Review 4, 4 (1990), 251--277.Google ScholarCross Ref
- Etienne Wenger. 1987. Artificial intelligence and tutoring system: Computational and Cognitive Approaches to the Communication of Knowledge. Califórnia: Morgan Kaufmann Publishers. Texto publicado na: Pátio-revista pedagógica Editora Artes Médicas Sul Ano 1 (1987), 19--21. Google ScholarDigital Library
- Alexander W Wiseman and Emily Anderson. 2015. A Cross-National Comparison of Ict Resources and Science Teachers Professional Development in and Use of Ict in the Gulf Cooperation Council Countries. In Science Education in the Arab Gulf States. Springer, 137--152.Google Scholar
Recommendations
User Perceptions of Using an Open Learner Model Visualisation Tool for Facilitating Self-regulated Learning
ACE '17: Proceedings of the Nineteenth Australasian Computing Education ConferenceWays to encourage self-regulated learning have become a hot topic in higher education. In this research study, we explored users' perceptions regarding the uptake and effective use of an open learner model visualisation prototype tool -- Doubtfire++, in ...
Enhancing learning outcomes through self-regulated learning support with an Open Learner Model
Open Learner Models (OLMs) have great potential to support students' Self-Regulated Learning (SRL) in Intelligent Tutoring Systems (ITSs). Yet few classroom experiments have been conducted to empirically evaluate whether and how an OLM can enhance ...
Students' understanding of their student model
AIED'11: Proceedings of the 15th international conference on Artificial intelligence in educationOpen Learner Models (OLM) are believed to facilitate students' metacognitive activities in learning. Inspectable student models are a simple but very common form of OLM that grant students opportunities to get feedback on their knowledge and reflect on ...
Comments