Fuzzy Cognitive Map (FCM) and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal knowledge. Despite their extensive use in causal knowledge engineering, there is no reported work which compares their respective roles. This paper aims to fill the gap by providing a qualitative comparison of the two frameworks through a systematic analysis based on some inherent features of the frameworks. We proposed a set of comparison criteria which covers the entire process of causal knowledge engineering, including modeling, representation, and reasoning. These criteria are usability, expressiveness, reasoning capability, formality, and soundness. The results of comparison have revealed some important facts about the characteristics of FCM and BBN, which will help to determine how FCM and BBN should be used, with respect to each other, in causal knowledge engineering.

Research found that the use of technology in teaching and learning is able to reduce mathematics anxiety by initiating students' motivation to explore and enjoy mathematics instead of feeling worries over it. There is a better humancomputer interaction for student engagement and motivation in learning when using technology and pedagogical agent. This study aimed to investigate the effectiveness of integrating the pedagogical agent in mathematics learning. An experimental research approach was employed with the involvement of undergraduates at a university in Malaysia. Undergraduates' learning was via a system with the integration of a pedagogical agent who provided motivational type of messages in teaching Calculus. Mathematics Anxiety Rating Scale was employed to measure students' anxiety levels before and after the study. Before the study, students were grouped into high, medium and low mathematics anxiety levels. Prior to the study the sample consisted of a high number of medium math-anxious students. There was no significant difference in the mean score of anxiety level for high and low math-anxious students, respectively, but there was a significant difference in the mean score between pre-and post-tests for medium math-anxious students. This agreed with the sample of this study that majority of the students were medium math-anxious students. Thus, integrating the pedagogical agent in mathematics learning effectively decrease students' mathematics anxiety level particularly in Calculus. A better human-computer interaction for student engagement and motivation in learning was gained when pedagogical agent was integrated in learning, hence effectively reduce students' mathematics anxiety.

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