Abstract-Intelligent agents have been used in collaborative learning. However, they are rarely used to facilitate group interactions in collaborative m-learning environments. In view of this, the paper discusses the use of intelligent agents in facilitating collaborative learning in mobile learning environments. The paper demonstrates how to design intelligent agents and integrate them in collaborative mobile learning environments to allow group learners to improve their levels of group knowledge construction. The design was implemented in a collaborative mobile learning system running on Modular Object-Oriented Dynamic Learning Environment (Moodle) platform. The application was used in some experiments to investigate the effects of those facilitated interactions on the level of group knowledge construction. The results showed improved levels of group knowledge construction in instances where the facilitations were enabled compared to where they were disabled. The paper concludes that the use of intelligent agents in facilitating learner group interactions in collaborative mobile learning environments improves the levels of group knowledge construction. For future work, the use of intelligent agents can be tested in other areas of group interactions to enhance group learning.
Purpose-Learning Management Systems (LMS) provide a platform for collaborative learning with features such as online group discussions. Grouping members together to participate in a group discussion does not guarantee their participation in the online discussion. However, the use of intelligent agents to facilitate group participation can motivate group members to participate in the online group discussions leading to improved levels of group knowledge construction. This paper discusses an experimental design for evaluating facilitated learner participation in online group discussions using intelligent agents. Method-In our experimental design we use two treatment groups (turn taking and informative feedback group facilitations) and one control group. We compared the levels of group knowledge construction amongst the three groups. 37 Results-The results of the study show improved levels of group knowledge construction where the facilitations for participation were provided compared to where they were not provided. Conclusion-The study concluded that facilitating collaborative mobile learning using intelligent agents improves the levels of group knowledge construction. Recommendation-Thus, we recommend the use of intelligent agents in facilitating group participation and consequently improving the group knowledge construction in collaborative mobile learning environments. Practical Implication-The study implies that intelligent agents are effective in collecting and analysing group processes leading to improved interactions which improve group knowledge construction.
Deep learning has proven to be a landmark computing approach to the computer vision domain. Hence, it has been widely applied to solve complex cognitive tasks like the detection of anomalies in surveillance videos. Anomaly detection in this case is the identification of abnormal events in the surveillance videos which can be deemed as security incidents or threats. Deep learning solutions for anomaly detection has outperformed other traditional machine learning solutions. This review attempts to provide holistic benchmarking of the published deep learning solutions for videos anomaly detection since 2016. The paper identifies, the learning technique, datasets used and the overall model accuracy. Reviewed papers were organised into five deep learning methods namely; autoencoders, continual learning, transfer learning, reinforcement learning and ensemble learning. Current and emerging trends are discussed as well.
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