Soft Computing

, Volume 22, Issue 8, pp 2449–2461 | Cite as

A hybrid recommender system for e-learning based on context awareness and sequential pattern mining

  • John K. Tarus
  • Zhendong NiuEmail author
  • Dorothy Kalui


The rapid evolution of the Internet has resulted in the availability of huge volumes of online learning resources on the web. However, many learners encounter difficulties in retrieval of suitable online learning resources due to information overload. Besides, different learners have different learning needs arising from their differences in learner’s context and sequential access pattern behavior. Traditional recommender systems such as content based and collaborative filtering (CF) use content features and ratings, respectively, to generate recommendations for learners. However, for accurate and personalized recommendation of learning resources, learner’s context and sequential access patterns should be incorporated into the recommender system. Traditional recommendation techniques do not incorporate the learner’s context and sequential access patterns in computing learner similarities and providing recommendations; hence, they are likely to generate inaccurate recommendations. Furthermore, traditional recommender systems provide unreliable recommendations in cases of high rating sparsity. In this paper, we propose a hybrid recommendation approach combining context awareness, sequential pattern mining (SPM) and CF algorithms for recommending learning resources to the learners. In our recommendation approach, context awareness is used to incorporate contextual information about the learner such as knowledge level and learning goals; SPM algorithm is used to mine the web logs and discover the learner’s sequential access patterns; and CF computes predictions and generates recommendations for the target learner based on contextualized data and learner’s sequential access patterns. Evaluation of our proposed hybrid recommendation approach indicated that it can outperform other recommendation methods in terms of quality and accuracy of recommendations.


Recommender systems Hybrid recommendation Context awareness E-learning Collaborative filtering Sequential pattern mining 



This work is supported by the National Natural Science Foundation of China (No. 61370137), the National Basic Research Program of China (No. 2012CB7207002), the Ministry of Education—China Mobile Research Foundation Project (Nos. 2015/5-9 and 2016/2-7) and the 111 Project of Beijing Institute of Technology.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina
  2. 2.Directorate of ICTMoi UniversityEldoretKenya
  3. 3.School of Information SciencesUniversity of PittsburghPittsburghUSA
  4. 4.University of Science and Technology BeijingBeijingChina

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