Adaptive learning path recommender approach using auxiliary learning objects

Nabizadeh, Amir Hossein; Gonçalves, Daniel; Gama, Sandra; Jorge, Joaquim; Rafsanjani, Hamed Nabizadeh

doi:10.1016/j.compedu.2019.103777

Cited by 80 publications

(51 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Promotion represents the increase in the knowledge of students. This measure is close to the one presented in [20,10], mentioned in the online evaluation section. The second measure proposed is more original, it represents the logicality of a LP, with respect to the knowledge structure in the recommended LP.…”

Section: Offline Evaluation Of Lp Recommendationssupporting

confidence: 79%

“…are rarely considered. [10] proposed to use A/B testing, one of the most popular and simple online evaluation form. Students are divided into two groups: one group gets personalised LP recommendations, while the other group gets non personalised LP recommendations.…”

Section: Online Evaluation Of Lp Recommendationsmentioning

confidence: 99%

“…More and more attention is being paid to these recommenders, that are also called sequence-aware or path recommender systems. In education, recommending a sequence of learning resources is referred to as a Learning Path (LP) recommendation [8,10,22]. When students adopt a recommended LP, they may improve their level of knowledge, increase their chance of success at exams, maintain their motivation, etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

New Measures for Offline Evaluation of Learning Path Recommenders

Zhang

Brun

Boyer

2020

Addressing Global Challenges and Quality Education

View full text Add to dashboard Cite

Recommending students useful and effective learning paths is highly valuable to improve their learning experience. The evaluation of the effectiveness of this recommendation is a challenging task that can be performed online or offline. Online evaluation is highly popular but it relies on actual path recommendations to students, which may have dramatic implications. Offline evaluation relies on static datasets of students' learning activities and simulates paths recommendations. Although easier to run, it is difficult to accurately evaluate offline the effectiveness of a learning path recommendation. To tackle this issue, this work proposes simple offline evaluation measures. We show that they actually allow to characterise and differentiate the algorithms.

show abstract

Section: Offline Evaluation Of Lp Recommendationssupporting

confidence: 79%

Section: Online Evaluation Of Lp Recommendationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New Measures for Offline Evaluation of Learning Path Recommenders

Zhang

Brun

Boyer

2020

Addressing Global Challenges and Quality Education

View full text Add to dashboard Cite

show abstract

“…Most of the current personalized learning models/ideas are built on technology integration. For example, while Chen, Lee and Chen (2005) proposed a personalized system that provides learning paths (Nabizadeh, Gonçalves, Gama, Jorge and Rafsanjani, 2020) that can be adapted to various levels of difficulty of course materials (Zou and Xie, 2018) and various abilities of learners (p. 239). Klašnja-Milićević et al (2011) stated that personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences (Flores, Ari, Inan and Arslan-Ari, 2012) that fit the needs, goals, talents, motivations, and interests of their learners (p. 885).…”

Section: Discussionmentioning

confidence: 99%

A systematic literature review of personalized learning terms

Shemshack

Spector

2020

Smart Learn. Environ.

212

View full text Add to dashboard Cite

Learning is a natural human activity that is shaped by personal experiences, cognitive awareness, personal bias, opinions, cultural background, and environment. Learning has been defined as a stable and persistent change in what a person knows and can do. Learning is formed through an individual’s interactions, including the conveyance of knowledge and skills from others and experiences. So, learning is a personalized experience that allows one to expand their knowledge, perspective, skills, and understanding. Therefore, personalized learning models can help to meet individual needs and goals. Furthermore, to personalize the learning experience, technology integration can play a crucial role. This paper provides a review of the recent research literature on personalized learning as technology is changing how learning can be effectively personalized. The emphasis is on the terms used to characterize learning as those can suggest a framework for personalized and will eventually be used in meta-analyses of research on personalized learning, which is beyond the scope of this paper.

show abstract

“…ERSs are tools that offer personalized suggestions to users by predicting, for example, their level of satisfaction with a given resource [9][10][11]. Such systems are based on preferences, likes, moods, learning styles, and all the variables that enable the characterization of the user and the retrieval process [12].…”

mentioning

confidence: 99%

Personalized Hybrid Educational Recommender System Using Matrix Factorization with User and Item Information

Marín¹,

Méndez²,

Ovalle³

et al. 2020

Preprint

View full text Add to dashboard Cite

One of the main challenges for autonomous learning in virtual environments is finding the right material that fits students’ needs and supports their learning process. Personalized recommender systems partially solve this problem by suggesting online educational resources to students based on their preferences. However, in educational environments (which need a proper characterization of both users and educational resources), most existing recommendation algorithms either fail to include all the available information or use hybrid processes that do not exploit possible relationships between users and item features. This article presents a personalized recommender system for educational resources aimed at combining user and item information into a single mathematical model based on matrix factorization. As a result, estimated latent factors can provide insight into possible interactions between users and item features, improving the quality of the information retrieval process. We validated the proposed model on a real dataset that contains the ratings assigned by students from Universidad Nacional de Colombia and Universidade Feevale to educational resources in the Colombian Federation of Learning Object Repositories (FROAC in Spanish). User characterization included learning style and educational level, whereas item characterization (obtained from the objects’ metadata), included interactivity level, aggregation level and type, and resource format. These results, compared to those obtained when not all the available information is included, show that our method can improve the recommendation process.

show abstract

Adaptive learning path recommender approach using auxiliary learning objects

Cited by 80 publications

References 21 publications

New Measures for Offline Evaluation of Learning Path Recommenders

New Measures for Offline Evaluation of Learning Path Recommenders

A systematic literature review of personalized learning terms

Personalized Hybrid Educational Recommender System Using Matrix Factorization with User and Item Information

Contact Info

Product

Resources

About