In the last decades effective teaching and learning and e-learning environments have been performed in order to construct courses jointly with the collaboration with Industry and High-Level Educational Institutions. On another way there are several terminologies that attempt to specify the best teaching and learning methods applied to engineering, from problem-based learning, project-based learning, work-based learning, teamlearning, self-direct learning for example. However motivational studies and motivational scales typically discard uncertainty characteristic in for quantitatively evaluating the different dimensions on student's motivational assessment in (e)-learning environments. This paper presents a computerized framework grounded on Artificial Intelligence techniques, namely the Case Based Reasoning approach for problem solving, complemented with a Knowledge Representation and Reasoning method that considers unknown, incomplete or even self-contradictory data or knowledge in the motivational student's assessment. © 2019 Association for Computing Machinery.
Author Keywords
Index Keywords
