This study investigated the impact of a chatbot-based micro-learning system on students’ learning motivation and performance. A quasi-experiment was conducted with 99 first-year students taking part in a basic computer course on number system conversion. The students were assigned to a traditional learning group or a chatbot-based micro-learning group. After the experiment, both groups achieved a comparable performance, suggesting that students are sufficiently competent to learn independently in the chatbot-based learning environment without the need for continuous face-to-face delivery. Moreover, students in the chatbot learning group attained significantly higher intrinsic motivation than the traditional learning group with perceived choice and perceived value as core predictors of intrinsic motivation. Further analysis with the Johnson-Neyman procedure revealed differences on interaction between the perceived choice and the learning environments. For students with a high initial perceived choice (>=5.1), chatbot-based learning further enhances their post choice motivation whereas for students with a low initial perceived choice (<=3.0), the traditional classroom is more suitable to enhance their post choice motivation. The implications of the findings can help instructors to incorporate chatbot-based learning in the classroom.

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E-learning systems are widely deployed in higher education institutions but sustaining students’ continued use of e-learning systems remains challenging. This study investigated the relationship between e-learning engagement, flow experience and learning management system continuance via a mediated moderation interaction model. The context of the study is a Moodle LMS supporting a blended learning environment. After controlling age and gender, a PLS analysis of 92 students’ samples with a reflective flow construct explained 49% of the variance in the research model. The analysis shows that flow mediates e-engagement and perceived ease of use with a direct positive impact on e-learning system continuance. Flow has an indirect impact through perceived usefulness on e-learning system continuance. However, the direct impact of flow on system continuance weakens as e-learning engagement increases. This finding may help to explain the mixed and inconsistent impact of flow in the e-learning system continuance literature. The dual effect of flow suggests that instructors must carefully balance pedagogical decisions intended to heighten flow experience to generate positive learning outcomes through e-engagement and its consequence of reduced impact on continued system use.

During the diagenesis of rocks in a sedimentary basin, an intrusion of CO 2 could cause dissolution or/and precipitation of the surrounding rocks. As a result, the reservoir quality may be altered. The mineral composition and heterogeneity have pronounced impacts on the geochemical reaction and reservoir quality evolution. A numerical simulation method is employed to investigate the influences of primary mineral on the diagenesis. Based on the measured data from the Songliao Basin, a total of 26 two-dimensional models with different mineral composition are set up. To mimic the regionally heterogeneous distribution of mineral composition, the Monte Carlo method is employed. A CO 2 gas reservoir of magma intrusion origin, located in the Songliao Basin, northeastern China, is selected for the present study. The reservoir is an ideal site for investigating the impact of mineral heterogeneity on diagenesis after the CO 2 intrusion. In this reservoir, with the presence of high-pressure CO 2 , the mineral heterogeneity causes significant dissolution and precipitation of minerals, which decreases the reservoir porosity and degrades the reservoir quality. The geochemical reactions caused by different mineral composition vary widely. The mineral heterogeneity causes similar distributions of the geochemical reactions and reservoir quality evolution. Dominant secondary minerals are dawsonite in the early diagenesis and ankerite in the late stage. The diagenetic sequences modeled by our numerical simulations reproduce the petrographic and geochemical data well.

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