ABSTRACT
Nowadays, the MOOC (Massive Open Online Course) revolution is gaining growing popularity due to the large number of open online courses. However, the retention rate of learners, which is generally around 10%, raises the question of the effectiveness of this mode of education. Our main objective in this paper is to design a new model to improve the courses completion rate and fight against the dropping out through an adaptive e-learning system for each learner, so that the proposed course correspond to the adequate way the learners could accomplish their learning process. The model will be realized by exploiting the traces left during the users' interactions with their learning environment. By using these traces, we get all pertinent information related to the learner profile. Furthermore, we will generate via ant colony algorithms, recommendations tailored to each learner.
- B, R.P. et al. 2018. Visualizing Learning Analytics. Springer International Publishing.Google Scholar
- Bakki, A. et al. 2015. Motivation and engagement in MOOCs: How to increase learning motivation by adapting Pedagogical Scenarios? Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9307, (2015), 556--559. DOI:https://doi.org/10.1007/978-3-319-24258-3_58.Google ScholarDigital Library
- DIng, W. et al. 2018. A New Learner Model in Adaptive Learning System. 2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018. 1 (2018), 472--476. DOI:https://doi.org/10.1109/CCOMS.2018.8463316.Google Scholar
- Dorigo, M. et al. 2007. An Introduction to Ant Colony Optimization. IRIDIA - Technical Report. April 2006 (2007).Google Scholar
- Dorigo, M. et al. 1999. Ant algorithms for discrete optimization. Artificial Life. 5, 2 (1999), 137--172. DOI:https://doi.org/10.1162/106454699568728.Google ScholarDigital Library
- Ennouamani, S. and Mahani, Z. 2018. An overview of adaptive e-learning systems. 2017 IEEE 8th International Conference on Intelligent Computing and Information Systems, ICICIS 2017. 2018-Janua, Icicis (2018), 342--347. DOI:https://doi.org/10.1109/INTELCIS.2017.8260060.Google Scholar
- Gulati, A. 2013. AN OVERVIEW OF MASSIVE OPEN ONLINE COURSES (MOOCs): SOME REFLECTIONS. International Journal of Digital Library Services. 3, 4 (2013), 37--46.Google Scholar
- Khalil, M. 2018. Learning Analytics in Massive Open Online Courses. April (2018).Google Scholar
- Kotova, E.E. and Pisarev, A.S. 2017. Adaptive prediction of student learning outcomes in online mode. Proceedings of 2017 IEEE 2nd International Conference on Control in Technical Systems, CTS 2017. (2017), 138--141. DOI:https://doi.org/10.1109/CTSYS.2017.8109509.Google Scholar
- Li, R. 2019. Adaptive learning model based on ant colony algorithm. International Journal of Emerging Technologies in Learning. 14, 1 (2019), 49--57. DOI:https://doi.org/10.3991/ijet.v14i01.9487.Google ScholarCross Ref
- Muhammad, A. et al. 2016. Learning path adaptation in online learning systems. Proceedings of the 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2016. (2016), 421--426. DOI:https://doi.org/10.1109/CSCWD.2016.7566026.Google Scholar
- Pandit, D. and Bansal, A. 2019. A declarative approach for an adaptive framework for learning in online courses. Proceedings - International Computer Software and Applications Conference. 1, (2019), 212--215. DOI:https://doi.org/10.1109/COMPSAC.2019.00039.Google Scholar
- Pang, Y. et al. 2018. Adaptive recommendation for MOOC with collaborative filtering and time series. Computer Applications in Engineering Education. 26, 6 (2018), 2071--2083. DOI:https://doi.org/10.1002/cae.21995.Google ScholarCross Ref
- Radosavljevic, V. et al. 2019. a Model of Adaptive Learning in Smart Classrooms Based on the Learning Strategies. PEOPLE: International Journal of Social Sciences. 5, 2 (2019), 662--679. DOI:https://doi.org/10.20319/pijss.2019.52.662679.Google ScholarCross Ref
- Rathore, A.S. and Arjaria, S.K. 2019. Intelligent Tutoring System. Icici (2019), 121--144. DOI:https://doi.org/10.4018/978-1-7998-0010-1.ch006.Google Scholar
- Rosen, Y. et al. 2018. The effects of adaptive learning in a massive open online course on learners' skill development. Proceedings of the 5th Annual ACM Conference on Learning at Scale, L at S 2018. June (2018). DOI:https://doi.org/10.1145/3231644.3231651.Google ScholarDigital Library
- Rozenberg, G. et al. 2012. Handbook of Natural Computing. Handbook of Natural Computing. 1-4, (2012), 1--2051. DOI:https://doi.org/10.1007/978-3-540-92910-9.Google Scholar
- Šumak, B. et al. 2019. Development of an autonomous, intelligent and adaptive e-learning system. 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019 - Proceedings. (2019), 1492--1497. DOI:https://doi.org/10.23919/MIPRO.2019.8756889.Google Scholar
- Tashtoush, Y.M. et al. 2017. Adaptive e-learning web-based English tutor using data mining techniques and Jackson's learning styles. 2017 8th International Conference on Information and Communication Systems, ICICS 2017. (2017), 86--91. DOI:https://doi.org/10.1109/IACS.2017.7921951.Google Scholar
- Tlili, A. et al. 2017. A Smart Educational Game to Model Personality Using Learning Analytics. Proceedings -IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017. (2017), 131--135. DOI:https://doi.org/10.1109/ICALT.2017.65.Google ScholarCross Ref
- Uddin, M. et al. 2017. A Learner Model for Adaptable e-Learning. International Journal of Advanced Computer Science and Applications. 8, 6 (2017). DOI:https://doi.org/10.14569/ijacsa.2017.080618.Google ScholarCross Ref
- Zheng, S. et al. 2016. The role of social media in MOOCs: How to use social media to enhance student retention. L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale. (2016), 419--428. DOI:https://doi.org/10.1145/2876034.2876047.Google ScholarDigital Library
Recommendations
Learners Self-directing Learning in FutureLearn MOOCs: A Learner-Centered Study
Transforming Learning with Meaningful TechnologiesSupporting learners' self-regulated learning in Massive Open Online Courses
AbstractIn MOOCs, learners are typically presented with great autonomy over their learning process. Therefore, learners should engage in self-regulated learning (SRL) in order to successfully study in a MOOC. Learners however often struggle to self-...
Highlights- Learners struggle to regulate their learning in massive open online courses (MOOCs).
- A self-regulated learning (SRL) intervention was implemented in three MOOCs.
- Learners' SRL was measured with trace data variables.
- ...
Perception of MOOC Pedagogical Tools and Learners' Learning Styles in MOOC Blended Teaching: a Case Study
ICEBT '19: Proceedings of the 2019 3rd International Conference on E-Education, E-Business and E-TechnologyRapid development has been achieved since the emergence of MOOC in 2008, but there are still many defects in the popularization of MOOC. Developing blended teaching by utilizing is considered to be one of effective means to overcome these shortcomings. ...
Comments