The adaptive e-learning systems research field is well populated with literature covering various aspects of adaptive e-learning. Thus, Kardan, Aziz, and Shahpasand selected several leading full-text scientific online databases such as Science Direct, ACM Portal, Taylor & Francis Online, and others to study research papers on adaptive e-learning systems. They reviewed 190 research papers, selected from 45 journals and published between 2000 and 2012. The study is based on traditional methods, focusing on selecting basic keywords related to adaptive e-learning systems and reviewing selected papers along several key criteria with accompanying classification of the categorized papers. The first stage of the methodological process is presented very clearly by a flowchart of the classification process, showing readers not only the project steps, but also the main content areas in which the papers were categorized: adaptive techniques and application of adaptive techniques.

An adaptive e-learning system is focused on individualized learning, and it uses various adaptive techniques to enable or facilitate different aspects of learning. The authors, through a classification framework for adaptive techniques, successfully present five different categories: machine learning and soft computing, semantic web and ontology, application software, hybrid techniques, and special techniques. Each category is explained through findings from the process of reviewing selected papers. The same approach is used in reviewing applications of adaptive techniques. According to the paper, “the research papers are classified into learner’s problem alleviation, presentation, learning style detection, navigation, multidimensional support, concept map construction, and other.” These categories are well analyzed in selected papers, giving users new ways to understand adaptive systems that are used in the design and deployment of e-learning systems.

This is an incredible research paper covering almost all relevant aspects of adaptive e-learning systems; it provides a huge base of sources from this research field. Obviously, it must be a primary source for any further research on adaptive e-learning systems, and would also serve librarians looking to include materials on this topic in a library collection.