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To explore what learning behaviors affect learners science-based inquiry learning performance, this work develops an xAPI (Experience Application Programming Interface)-based learning record store module embedded in a Collaborative Web-based Inquiry Science Environment (CWISE) to record students detailed learning process data, including learning behaviors, learning time, and learning sequence. More importantly, based on the recorded learning behaviors, this work discusses the significant correlation and cause-effect relation among science inquiry ability, learning time, and learning performance, and to explore the remarkable shift and difference in the learning behaviors of learners with different learning performance and inquiry abilities by using sequential pattern mining and lag sequential analysis. The research results show that inquiry ability, total learning time, and inquiry simulation experiment learning time present notably positive correlations with learning performance, with linear regression, showing the predictability of learning performance, on which inquiry simulation experiment learning time appears the most significant effect. Moreover, based on sequential pattern mining, learners with different learning performance do not show significant differences on the learning sequence of the entire inquiry learning course, but follow the inquiry learning process sequence in the curriculum design. Also, based on lag sequential analysis, learners with high learning performance and high inquiry ability would re-adjust the previous hypotheses after proceeding the simulation experiment of buoyancy, while learners with low learning performance and low inquiry ability are lack of such a key inquiry learning behavior.

Although online courseware often includes multimedia materials, exactly how different video lecture types impact student performance has seldom been studied. Therefore, this study explores how three commonly used video lectures styles affect the sustained attention, emotion, cognitive load, and learning performance of verbalizers and visualizers in an autonomous online learning scenario by using a two-factor experimental design, brainwave detection, emotion-sensing equipment, cognitive load scale, and learning performance test sheet. Analysis results indicate that, while the three video lecture types enhance learning performance, learning performance with lecture capture and picture-in-picture types is superior to that associated with the voice-over type. Verbalizers and visualizers achieve the same learning performance with the three video types. Additionally, sustained attention induced by the voice-over type is markedly higher than that with the picture-in-picture type. Sustained attention of verbalizers is also significantly higher than that of visualizers when learning with the three video lectures. Moreover, the positive and negative emotions induced by the three video lectures do not appear to significantly differ from each other. Also, cognitive load related to the voice-over type is significantly higher than that with by the lecture capture and picture-in-picture types. Furthermore, the cognitive load for visualizers markedly exceeds that of verbalizers who are presented with the voice-over type. Results of this study significantly contribute to efforts to design of video lectures and also provide a valuable reference when selecting video lecture types for online learning.