Facing students' decreasing motivation to pursue scientific study, schools and educators need to coordinate new technologies with pedagogical agents to effectively sustain or promote students' scientific learning and motivation to learn. Although the provision of pedagogical agents in student learning has been studied previously, it is not clear what benefits the strategy might offer with regard to student motivation. This study proposes an agent‐based mechanism that integrates problem‐solving and inquiry‐based instructions to help students better understand complex scientific concepts and to sustain their motivation to learn science. In this study, a quasi‐experiment was conducted to evaluate the performance and feasibility of our proposed mechanism. The results revealed that the agent‐based mechanism was effective and feasible for enhancing students' learning and motivation to learn. The mechanism was associated with increases in the acquisition of knowledge when compared with the control group. Its effect in promoting and sustaining students' motivation was also statistically significant. Detailed discussions of the findings are provided in this study.

This paper proposes a weighted variance method to compute the limits of the X and S charts for skewed distributions. The proposed charts extend the weighted variance X and R charts in [1] by enabling a process from a skewed distribution with moderate and large sample sizes to be monitored efficiently, hence producing more favourable Type-I and Type-II error rates than the charts in [1]. Note that the charts in [1] are only intended to be used for small sample sizes. The Type-I and Type-II error rates computed show that the proposed charts outperform the existing heuristic charts, as well as those in [1] for moderate and large sample sizes, involving cases with known and unknown parameters, when the distribution of a process is skewed.