G. Durand, F. Laplante, and R. Kop, A learning design recommendation system based on markov decision processes, 17th ACM KDD, 2011.

G. Durand, N. Belacel, and F. Laplante, Graph theory based model for learning path recommendation, Information Sciences, vol.251, pp.10-21, 2013.

P. Dwivedi, V. Kant, and K. Bharadwaj, Learning path reco. based on modified variable length genetic algorithm, Education & Inf. Techno, vol.23, issue.2, pp.819-836, 2018.

S. E. Embretson and S. P. Reise, Item response theory, 2013.

S. Feng, X. Li, Y. Zeng, G. Cong, Y. M. Chee et al., Personalized ranking metric embedding for next new poi recommendation, p.24, 2015.

T. Hsieh and T. Wang, A mining-based approach on discovering courses pattern for constructing suitable learning path, Exp. Syst. with App, vol.37, issue.6, pp.4156-4167, 2010.

M. Léonard, Y. Peter, and Y. Secq, Patterns and loops: Early computational thinking, Eur. Conf. on Technology Enhanced Learning, pp.280-293, 2019.

Q. Liu, S. Tong, C. Liu, H. Zhao, E. Chen et al., Exploiting cognitive structure for adaptive learning, Proc. 25th KDD, pp.627-635, 2019.

D. Monti, E. Palumbo, G. Rizzo, and M. Morisio, Sequeval: An offline evaluation framework for sequence-based rs, Information, vol.10, issue.5, p.174, 2019.

A. Nabizadeh, D. Gonçalves, S. Gama, J. Jorge, and H. Rafsanjani, Adaptive lp recommender approach using auxiliary learning objects, Comp. & Educ, vol.147, 2020.

A. Nabizadeh, A. Jorge, and J. Leal, Estimating time and score uncertainty in generating successful learning paths under time constraints, Exp. Syst, vol.36, issue.2, 2019.

A. Nabizadeh, A. Jorge, and J. P. Leal, Long term goal oriented recommender systems, Proc. of the 11th Webist, pp.552-557, 2015.

M. Quadrana, P. Cremonesi, and D. Jannach, Sequence-aware recommender systems, ACM Computing Surveys (CSUR), vol.51, issue.4, pp.1-36, 2018.

M. Rossetti, F. Stella, and M. Zanker, Contrasting offline and online results when evaluating recommendation algorithms, Proc. 10th RecSys, pp.31-34, 2016.

C. Su, Designing and developing a novel hybrid adaptive learning path recommendation system (alprs) for gamification mathematics geometry course, Science and Technology Education, vol.13, issue.6, pp.2275-2298, 2017.

J. Su, S. Tseng, W. Wang, J. Weng, J. Yang et al., Learning portfolio analysis and mining for scorm compliant environment, J. of Educational Technology & Society, vol.9, issue.1, pp.262-275, 2006.

B. Taraghi, A. Saranti, M. Ebner, and M. Schön, Markov chain and classification of difficulty levels enhances the learning path in one digit multiplication, Int. Conf. on Learning and Collaboration Technologies, pp.322-333, 2014.

R. Venant, K. Sharma, P. Vidal, P. Dillenbourg, and J. Broisin, Using sequential pattern mining to explore learners behaviors and evaluate their correlation with performance in inquiry-based learning, pp.286-299, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01913995

B. Vesin, A. Kla?nja-mili?evi?, M. Ivanovi?, and Z. Budimac, Applying recommender systems and adaptive hypermedia for e-learning personalizatio, Computing and informatics, vol.32, issue.3, pp.629-659, 2013.

H. Xie, D. Zou, F. L. Wang, T. L. Wong, Y. Rao et al., Discover learning path for group users: A profile-based approach, Neurocomputing, vol.254, pp.59-70, 2017.

Y. Zhou, C. Huang, Q. Hu, J. Zhu, and Y. Tang, Personalized learning full-path recommendation model based on lstm neural networks, Inf. Sci, vol.444, pp.135-152, 2018.

H. Zhu, F. Tian, K. Wu, N. Shah, Y. Chen et al., A multi-constraint learning path recommendation algorithm based on knowledge map, vol.143, pp.102-114, 2018.