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Virtual labs provide space for students to iteratively test, observe, and revise their understanding so as to improve their scientific literacy. However, one of the challenges that students face is that they need to think and act like scientists so as to be sensitively alert to methodological flaws and various sources of error. This study thus compared the effect of two instructional approaches using a virtual lab to enhance students' scientific literacy. Before students were given the opportunity to conduct science inquiries with the virtual lab, they were required to critique problematic inquiry cases (the critique group) or watch teachers' demonstrations (the teacher demonstration group) before taking part in the inquiry. By analyzing data from 50 middle school students, this study found that the effect of applying virtual labs can be augmented by an instructional design that engages students in critiquing experiments prior to their inquiry with the virtual lab. This study also found a limitation of the use of virtual labs in helping students transfer what they have learned from the teacher's demonstration to new inquiry contexts. A close relation among scientific literacy post-test scores, critiquing performance, and inquiry performance in the inquiry activity was detected, suggesting that student critiquing prior to inquiry is in alignment with the goal of developing students' inquiry skills and scientific literacy with virtual labs.

Background: Collaborative computer simulations are available on some online platforms which support students at distributed locations to synchronously collaborate on the simulations to learn sciences. However, how students collaborate with each other in collaborative simulations is not clear. Objectives: The aim of this study was to investigate whether functional interdependency would influence learning. Methods: Two collaborative learning models with different functional interdependencies were examined (the synchronous model and the distributed accountability model). Multiple data sources obtained from 64 students were collected and analyzed. We discuss the collaborative learning with two levels of factors: the individual-level factors (i.e., individual perceptions of teamwork quality, visual attention and learning performance before and after a simulation activity) and the pair-level factors (i.e., discourse data and joint attention). Given that the pair-level factors in the collaborative learning environment may affect the individual-level factors, we further discuss the interactions of the two levels of factors in different collaborative learning models with hierarchical linear modeling (HLM) analysis. Results and Conclusions: The analytical results of the individual-level factors found participants in the distributed accountability model demonstrated more frequent and longer attention to the learning activity. Furthermore, the results indicated that the two simulation learning activities would enhance the participants' learning performance except in Q2. For the pair-level factors, the results demonstrated that the participants in the distributed accountability model showed more active discourse segments but similar joint attention patterns in the learning activity. The results of the HLM analysis indicated that the students in the distributed accountability model had to collaborate with their partners through more constructive dialogue, and focused on the same areas of interest to enhance their learning performance, as the complexity of the distributed accountability design was implemented in a simulation learning activity. Major takeaways: We found that the increase of functional interdependency cannot guarantee students' learning effect in collaborative learning and may hinder the communication that is needed during collaboration. Researcher may need to consider other collaboration tools to enhance the reciprocal reflection.

This study investigated students' modeling progress and strategies in a problem-solving simulation game through content analysis, and through supervised and unsupervised lag sequential analysis (LSA). Multiple data sources, including self-report models and activity logs, were collected from 25 senior high school students. The results of the content analysis found that the problem-solving simulation game helped most of the students to reflectively play with the science problem and build a workable model to solve it. By using the supervised LSA, it was found that the students who successful solved the game frequently linked the game contexts with the physics terminologies, while those who did not solve the problem simply relied on the intuitive knowledge provided in the reference materials. Furthermore, the unsupervised LSA identified four activity patterns that were not noticed in the supervised LSA: the fragmented, reference material centered, reference material aided modeling, and modeling centered patterns. Each pattern has certain associations with certain problem-solving outcomes. The results of this study also shed light on the use of different analytics techniques. While the supervised LSA is particularly helpful for depicting a contrast of activity patterns between two specific student groups, the unsupervised LSA is able to identify hidden significant patterns which were not clearly distinguished in the pre-defined student groups. Researchers may find these analytics techniques useful for analyzing students' learning processes.

One issue facing teachers who implement model-based instruction within the school system is the tight curricular schedule. The 'lesson objective tension' calls for investigations to address instructional issues such as how to organise modelling-based instruction at the curriculum level which is across multiple units. We investigated the effect of the cyclic curricular design spacing out three modelling-based instruction units with virtual labs over three semesters, each involving 73, 181, and 150 junior high school students, respectively. Results indicate that, overall, the approach of modelling with virtual labs better promoted students' scientific literacy than the traditional instruction did. Specifically, students with low and moderate school science achievements benefitted more as shown in their scientific literacy scores than did the high achievement students. Furthermore, this study found that receiving one unit of modelling-based instruction may not be sufficient to promote students' scientific literacy, whereas the cyclic approach that suggests students receive two or more units of modelling-based instruction with virtual labs spaced over consecutive semesters had significant effects on promoting students' scientific literacy. The results provide insights into when and how modelling-based learning with virtual labs can promote scientific literacy at a curricular level. [ABSTRACT FROM AUTHOR]

Background: Collaborative computer simulations are available on some online platforms which support students at distributed locations to synchronously collaborate on the simulations to learn sciences. However, how students collaborate with each other in collaborative simulations is not clear. Objectives: The aim of this study was to investigate whether functional interdependency would influence learning. Methods: Two collaborative learning models with different functional interdependencies were examined (the synchronous model and the distributed accountability model). Multiple data sources obtained from 64 students were collected and analyzed. We discuss the collaborative learning with two levels of factors: the individual‐level factors (i.e., individual perceptions of teamwork quality, visual attention and learning performance before and after a simulation activity) and the pair‐level factors (i.e., discourse data and joint attention). Given that the pair‐level factors in the collaborative learning environment may affect the individual‐level factors, we further discuss the interactions of the two levels of factors in different collaborative learning models with hierarchical linear modeling (HLM) analysis. Results and Conclusions: The analytical results of the individual‐level factors found participants in the distributed accountability model demonstrated more frequent and longer attention to the learning activity. Furthermore, the results indicated that the two simulation learning activities would enhance the participants' learning performance except in Q2. For the pair‐level factors, the results demonstrated that the participants in the distributed accountability model showed more active discourse segments but similar joint attention patterns in the learning activity. The results of the HLM analysis indicated that the students in the distributed accountability model had to collaborate with their partners through more constructive dialogue, and focused on the same areas of interest to enhance their learning performance, as the complexity of the distributed accountability design was implemented in a simulation learning activity. Major takeaways: We found that the increase of functional interdependency cannot guarantee students' learning effect in collaborative learning and may hinder the communication that is needed during collaboration. Researcher may need to consider other collaboration tools to enhance the reciprocal reflection. Lay Description: What is already known about this topic: Collaborative simulations are helpful in making the learning process cohesive.The learning effects of functional interdependence in collaborative learning are under debate. What this paper adds: We examined collaborative learning models with different functional interdependencies.We discuss the collaborative learning with the individual‐, the pair‐level factors and their interactions Implications for practice and/or policy: The importance of decreasing cognitive load induced by simulation with high functional interdependency.It is necessary to increase participants' transactive and constructive engagement in collaborative models. [ABSTRACT FROM AUTHOR]