An e-Learning environment for algorithmic: toward an active construction of skills
Main Article Content
Abstract
Assimilating an algorithmic course is a persistent problem for many undergraduate students. The major problem faced by students is the lack of problem solving ability and flexibility. Therefore, students are generally passive, unmotivated and unable to mobilize all the acquired knowledge (loops, test, variables, etc.) to deal with new encountered problems. Our study is structured around building, step by step, problem solving skills among novice learners. Our approach is based on the use of problem based learning in an e-Learning environment. We begin by establishing a cognitive model which represents knowledge elements, grouped into categories of skills, judged necessary to be appropriated. We then propose a problem built on a concrete situation which aims to actively construct a skill category. We conclude by presenting around the proposed problem a pedagogical scenario for the set of learning activities designed to be incorporated in an E-learning platform.
Downloads
Download data is not yet available.
Article Details
How to Cite
BABORI, Abdelghani et al.
An e-Learning environment for algorithmic: toward an active construction of skills.
World Journal on Educational Technology: Current Issues, [S.l.], v. 8, n. 2, p. 82-90, july 2016.
ISSN 1309-0348.
Available at: <http://sproc.org/ojs/index.php/wjet/article/view/819>. Date accessed: 02 dec. 2017.
doi: https://doi.org/10.18844/wjet.v8i2.819.
Issue
Section
Articles
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
Autoroutes du Maroc. (2015). Retrieved from http://www.adm.co.ma/en/preparez-votre-voyage/pages/tarifs.aspx
Benabbou, F., & Hanoune, M. (2006). EasyAlgo: Un environnement d’apprentissage et d’autoévaluation de l’algorithmique. RIST, 16.
Jarrar, M. (2007). Towards automated reasoning on ORM schemes mapping ORM into the DLR idf description logic. Proceedings of the 26th international conference on Conceptual modeling. 4801, pp. 181-197. Springer-Verlag.
Kaasbol, J. (2002). Learning Programming. University of Oslo.
Lahtinen, E., Ala-Mutka, K., & Järvinen, H.-M. (2005). A study of the difficulties of novice programmers. ACM SIGCSE Bulletin, 37, pp. 14-18. Finland. doi:10.1145/1067445.1067453
Lukichev, S., & Jarrar, M. (2009). Graphical notations for rule modeling. Handbook of Research on Emerging Rule-based Languages and Technologies: open solutions and approaches, 1, 76-98.
Mayer, R. (2013). Teaching for Transfer of Problem-Solving Skills to Computer Programming. Computer-Based Learning Environments and Problem Solving, 84, 193.
Moström, J. (2011). A study of student problems in learning to program. Umea, Sweden: University, Department of Computing Science. Retrieved from http://www.diva portal.org/smash/get/diva2:447104/FULLTEXT02.pdf
Pernin, J.-P., & Lejeune, A. (2004). Dispositifs d'apprentissage instrumentés par les technologies: vers une ingénierie centrée sur les. Technologies de l'Information et de la Connaissance dans l'Enseignement Supérieur et de l'Industrie (pp. 407--414). Université de Technologie de Compiègne.
Richardson, I., & Delaney, Y. (2009). Problem based learning in the software engineering classroom. 22nd Conference on Software Engineering Education and Training (pp. 174-181). Hyderabad, Andhra Pradesh: IEEE Computer Society. Retrieved from https://ulir.ul.ie/handle/10344/1813
Benabbou, F., & Hanoune, M. (2006). EasyAlgo: Un environnement d’apprentissage et d’autoévaluation de l’algorithmique. RIST, 16.
Jarrar, M. (2007). Towards automated reasoning on ORM schemes mapping ORM into the DLR idf description logic. Proceedings of the 26th international conference on Conceptual modeling. 4801, pp. 181-197. Springer-Verlag.
Kaasbol, J. (2002). Learning Programming. University of Oslo.
Lahtinen, E., Ala-Mutka, K., & Järvinen, H.-M. (2005). A study of the difficulties of novice programmers. ACM SIGCSE Bulletin, 37, pp. 14-18. Finland. doi:10.1145/1067445.1067453
Lukichev, S., & Jarrar, M. (2009). Graphical notations for rule modeling. Handbook of Research on Emerging Rule-based Languages and Technologies: open solutions and approaches, 1, 76-98.
Mayer, R. (2013). Teaching for Transfer of Problem-Solving Skills to Computer Programming. Computer-Based Learning Environments and Problem Solving, 84, 193.
Moström, J. (2011). A study of student problems in learning to program. Umea, Sweden: University, Department of Computing Science. Retrieved from http://www.diva portal.org/smash/get/diva2:447104/FULLTEXT02.pdf
Pernin, J.-P., & Lejeune, A. (2004). Dispositifs d'apprentissage instrumentés par les technologies: vers une ingénierie centrée sur les. Technologies de l'Information et de la Connaissance dans l'Enseignement Supérieur et de l'Industrie (pp. 407--414). Université de Technologie de Compiègne.
Richardson, I., & Delaney, Y. (2009). Problem based learning in the software engineering classroom. 22nd Conference on Software Engineering Education and Training (pp. 174-181). Hyderabad, Andhra Pradesh: IEEE Computer Society. Retrieved from https://ulir.ul.ie/handle/10344/1813