Search

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: The myocardial kinetic energy (KE) and its association with pulmonary regurgitation (PR) have yet to be investigated in repaired tetralogy of Fallot (rTOF) patients. Purpose: To evaluate the adaptation of myocardial KE in rTOF patients by tissue phase mapping (TPM). Study Type: Prospective. Population: A total of 49 rTOF patients (23 ± 5 years old; male = 32), 47 normal controls (22 ± 1 year old; male = 29). Field Strength/Sequence: 3‐T/2D dark‐blood three‐directional velocity‐encoded gradient‐echo sequence. Assessment: Left and right ventricle (LV, RV) myocardial KE in radial (KEr), circumferential (KEø), longitudinal (KEz) directions. The proportions of KE in each direction to the sum of all KE (KErøz): %KEr, %KEø, %KEz. PR fraction. Statistical Test: Student's t test, multivariable regression. Statistical significance: P < 0.05. Results: In rTOF group, LV KEz remained normal in systole (P = 0.565) and diastole (P = 0.210), whereas diastolic LV %KEz (62% ± 14% vs. 72% ± 7%) and systolic LV %KEø (9% ± 6% vs. 20% ± 7%) were significantly decreased. The KEr and %KEr of both ventricles significantly increased in the rTOF group (RV in diastole: 6 ± 3 vs. 3 ± 1 μJ and 54% ± 13% vs. 27% ± 7%). The rTOF group exhibited significantly higher RV/LV ratios of %KEr (systole: 1.3 ± 0.3 vs. 1.0 ± 0.3) and %KEø (systole: 1.6 ± 0.8 vs. 1.0 ± 0.3) and significantly lower ratios of %KEz in systole (0.7 ± 0.2 vs. 1.0 ± 0.1) and diastole (0.5 ± 0.2 vs. 0.9 ± 0.1). In multivariable regression analysis, the RV peak systolic KErøz, RV systolic KEz, and LV diastolic %KEø were independently associated with PR fraction in the rTOF group (adjusted R2 = 0.479). Data Conclusion: In rTOF patients, the adaptation of the KE proportion occurred earlier than that of the KE amplitude, and the biventricular balance of %KE was disrupted. PR may cause differential KE adaptation in RV and LV. TPM‐derived KE may be useful in investigation of myocardial adaptation in rTOF patients. Evidence Level: 2 Technical Efficacy: Stage 3 [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]

This work presents a modified extended key equation algorithm in list decoding of generalized Reed-Solomon (GRS) codes. A list decoding algorithm of generalized Reed-Solomon codes has two steps, interpolation and factorization. The extended key equation algorithm (EKE) is an interpolation-based approach with a lower complexity than Sudan's algorithm. To increase the decoding speed, this work proposes a modified EKE algorithm to perform codeword checking prior to such an interpolation process. Since the evaluation mapping is engaged in encoding, a codeword is not generated systematically. Thus, the transmission information is not directly obtained from a received codeword. Therefore, the proposed algorithm undertakes a matrix operation to obtain the transmission information once a received vector has been checked to be error-free. Simulation results demonstrate that the modified EKE algorithm in list decoding of a GRS code provides low complexity, particularly at high signal-to-noise ratios.

Abstract: The system complexity, as calculated from correlation dimension, embedded in each layer and its modulation by specific inputs and general excitatory state are not yet known. The aims of present study were to estimate the system complexity across the cortical layers by analyzing intracortical EEG signals using a nonlinear analytical method, and to identify how layer-related complexity varies with the alteration of thalamic input and brain state. Male Sprague–Dawley rats were anesthetized under l% halothane. Sixteen channels of evoked or spontaneous EEG signals were recorded simultaneously across the six cortical layers in the somatosensory cortex with a single Michigan probe. The system complexity was assessed by computing correlation dimension, D 2, based on the Nonlinear Time Series Analysis data analysis program. Cortical layer IV exhibited a D 2 value, 3.24, that was significantly higher than that of the other cortical layers. The D 2 values in layers IV and II/III were significantly reduced after reversible deactivation of the ventral posterior lateral thalamic nucleus. D 2 decreased with increases in administered halothane concentration from 0.75% to 2.0%, particularly in layer IV. The present findings suggest that cortical layer IV maintains a higher complexity than the other layers and that the complexity of the mid-cortical layers is subject to regulation from specific thalamic inputs and more sensitive to changes in the general state of brain excitation. [Copyright &y& Elsevier]