Smart learning environments (SLEs) have gained considerable momentum in the last 20 years. The term SLE has emerged to encompass a set of recent trends in the field of educational technology, heavily influenced by the growing impact of technologies, such as cloud services, mobile devices, and interconnected objects. However, the term SLE has been used inconsistently by the technology-enhanced learning (TEL) community since different research works employ the adjective “smart” to refer to different aspects of novel learning environments. Previous surveys on SLEs are narrowly focused on specific technologies or remain at a theoretical level that does not discuss practical implications found in empirical studies. To address this inconsistency and also to contribute to a common understanding of the SLE concept, this article presents a systematic literature review of papers published between 2000 and 2019 discussing SLEs in empirical studies. Sixty-eight papers out of an initial list of 1341 papers were analyzed to identify the following: 1) what affordances make a learning environment smart; 2) which technologies are used in SLEs; and 3) in what pedagogical contexts are SLEs used. Considering the limitations of previous surveys, and the inconsistent use of the SLE concept in the TEL community, this article presents a comprehensive characterization to describe SLEs through their affordances, the technologies used, and pedagogical approaches considered in the selected papers. As a result, specific core functions of SLEs are identified and explained. This work aims at ensuring a relevant knowledge base and reference toward the implementation of future SLEs.

The IEEE/Association for Computing Machinery (ACM) Computing Curricula and the Accreditation Board of Engineering and Technology (ABET) Evaluation Criteria 2000 emphasize the use of recurrent concepts and system design/evaluation through projects and case studies in the curriculum of Computer and Electrical Engineering. In addition, efficient teamwork, autonomy, and initiative are commonly required qualifications for a professional in this field. Project-based learning approaches that require the students to handle realistic case studies are adequate to pursue these objectives. However, these pedagogical approaches tend to be rejected because they promote deep learning but focus on a restricted set of concepts, whereas many engineering curricula require a broad range of concepts to be covered in each course. The introduction of multiple case studies carried out simultaneously in the same course by different teams of students can broaden the set of concepts studied, but collaboration at different levels must be strongly enforced to achieve effective learning. This paper describes a multiple-case-study project design that has been applied to a computer architecture course for four years. After systematically evaluating the experience, the authors conclude that students achieve a deep learning of the concepts required in their own case study, while they are able to generalize their knowledge to case studies of different characteristics from those considered during the course. Furthermore, a number of collaborative skills and attitudes are developed as a consequence of the proposed environment based on multiple levels of collaboration.