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In this article, smart learning environment (SLE) is defined as a hybrid learning system that provides learners and other stakeholders with a joyful learning process while achieving learning outcomes as a result of the employed intelligent tools and techniques. From literature study, existing SLE models and frameworks are difficult to understand and to use as guidance, both for implementation and maturity measurement. This research proposes a simple model of SLE as a guideline that is easy to understand and to be used for developing learning environments that support effective, efficient, and engaging learning processes. The proposed model adopted the intelligent tutoring system architecture as a foundation. The components of the model were generated by mapping of 12 existing SLE models, frameworks, and best practices. Model formation and validation followed the system dynamics (SD) modeling process. In addition, indirect model validation process was also performed by interviewing educational technology practitioners and experts, based on assessment results of two newly developed SLE instances. For measuring maturity of existing SLE instances, this research also derived SLE maturity model. It is concluded that the simple model is usable for easy understanding, for establishing new SLE instances, and also for assessing the maturity level of existing SLE instances. [ABSTRACT FROM AUTHOR]

Group development is an initial step and an important influence on learning collaborative problem solving (CPS) based on the digital learning environment (DLE). Group development based on the Myers-Briggs types indicators (MBTI) rule proved successful for the educational and industrial environment. The MBTI ideal group rules are reached when a group leader has the highest level of leadership and compatibility between group members. The level of leadership and suitability of group members is determined based on the MBTI learning style (LS). Problems arise when the population of MBTI LS with the highest level of leadership is over. This will lead to dual leadership problems and have an impact on group disharmony. This study proposes an intelligent agent software for the development of the ideal group of MBTI, using the Fuzzy algorithm. The intelligent agent was developed on the SKACI platform. SKACI is a DLE for CPS learning. Fuzzy algorithm for solving dual leadership problems in a group. Fuzzy algorithm is used to increase the population of MBTI LS to 3 levels, namely low, medium and high. Increasing the population of MBTI LS can increase the probability of forming an ideal group of MBTI. Intelligent agents are tested based on a quantitative analysis between experimental classes (applying intelligent agents), and control classes (without intelligent agents). Experiment results show an increase in performance and productivity is better in the experimental class than in the control class. It was concluded that the development of intelligent agents had a positive impact on group development based on the MBTI LS. [ABSTRACT FROM AUTHOR]