An artificial intelligence case based approach to motivational students assessment in (e)-learning environments

ABSTRACT

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, team-learning, 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.

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Index Terms

• Applied computing

  • Education

    • Distance learning

    • E-learning

• Computing methodologies

  • Artificial intelligence

    • Knowledge representation and reasoning

  • Machine learning

    • Machine learning approaches

      • Instance-based learning

• Theory of computation

  • Logic

    • Constraint and logic programming