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The aim of this study was to develop a scale to measure students' blended learning course experience. A total of 792 undergraduate students from Malaysia participated in this study. Exploratory factor analysis (EFA) was employed to evaluate the factor structure of the scale. As a result of EFA, three factors with 19 items that explained 68.06% of the total variance were obtained. The three factors are: course design, learning experience, and personal factors. Confirmatory factor analysis (CFA) was used to establish the structural validity of the scale, and item discrimination was conducted to validate the scale. The Cronbach's coefficients (a) for the factors ranged from 0.71 to 0.95. The overall Cronbach's coefficient (a) of the scale was .92, demonstrating a satisfactory level of reliability. The results showed that the developed scale is reliable and valid. Future implications for course designers and instructors are discussed.

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.

The purpose of this study was to analyze visual attention during students' learning of Toulmin's argument pattern (TAP) and the effects of epistemic beliefs in science on the understanding of argument components. A total of 43 undergraduates were recruited through the Internet. Online questionnaires targeting beliefs about knowledge in science [Conley, A. M., Pintrich, P. R., Vekiri, I., & Harrison, D. (2004). Changes in epistemological beliefs in elementary science students. "Contemporary Educational Psychology," 29(2), 186-204] and beliefs about justification for knowing in science [Ferguson, L. E., & Bråten, I. (2013). Student profiles of knowledge and epistemic beliefs: Changes and relations to multiple-text comprehension. "Learning and Instruction," 25, 49-61] were used to assess students' epistemic beliefs. Identification tests for the understanding of TAP were performed before and after an argument reading treatment designed as a self-learning activity. The whole reading process was recorded by the eye-tracking system. To explore the effect of the reading treatment, participants were divided into a progress (P) group and a no-progress (NP) group based on their learning gains. The results indicated that students' epistemic beliefs in science and their learning strategies would affect their TAP learning performance. The analyses of visual attention distribution indicated that integration information indicated by inter-scanning counts would enhance students' learning performance. In addition, the LSA results suggested that understanding of data is of primary importance when learning TAP. Suggestions on how to guide students in learning TAP are provided based on the findings.

Although AR technology seems to be promising for helping students learn complicated and abstract concepts, whether the positive effects of this new technology can be applied to the learning of three-dimensional geometric concepts, and what factors might mediate its effectiveness are questions worth further investigation. This study used the eye-tracking method to examine middle school students' learning of three-dimensional geometric concepts with AR-based instruction in comparison with a group of students who learned via paper-based instruction. Motivation and conceptual achievements were assessed using the Instructional Materials Motivation Survey (IMMS) questionnaire and Geometry Achievement Test (GAT), respectively. The results showed that while AR learners performed the same as the paper-based learners in the post concept test, the two groups differed significantly in terms of their motivation orientations and visual attention patterns. AR students were more satisfied with and engaged more in the corresponding instruction. To explore in-depth the effectiveness of the AR system, the AR participants were divided into improved and unimproved learners. The former displayed higher confidence in and perceived greater relevance of the AR instruction. Their attention to and engagement in the simple geometric concepts was higher. Correlation and regression analyses indicated that the performance of paper-based learners was affected by their visual attention, while the performance of the AR learners was predicted by their beliefs in the relevance of the material to the learning goals. Noticeably, for the AR learners, their motivation was significantly associated with their visual attention.

Osteoporosis is characterized by low bone mass, bone microarchitecture disruption, and collagen loss, leading to increased fracture risk. In the current study, collagen peptides were extracted from milkfish scales (MS) to develop potential therapeutic candidates for osteoporosis. MS was used to synthesize a crude extract of fish scales (FS), collagen liquid (COL), and hydroxyapatite powder (HA). COL samples were further categorized according to the peptide size of total COL (0.1 mg/mL), COL < 1 kDa (0.1 mg/mL), COL: 1–10 kDa (0.1 mg/mL), and COL > 10 kDa (0.1 mg/mL) to determine it. Semi‐quantitative reverse transcription polymerase chain reaction (sqRT‐PCR) and immunofluorescence labeling were used to assess the expression levels of specific mRNA and proteins in vitro. For in vivo studies, mice ovariectomy (OVX)‐induced postmenopausal osteoporosis were developed, while the sham surgery (Sham) group was treated as a control. Collagen peptides (CP) from MS inhibited osteoclast differentiation in RAW264.7 cells following an insult with nuclear factor kappa‐B ligand (RANKL). CP also enhanced osteoblast proliferation in MG‐63 cells, possibly through downregulating NFATc1 and TRAP mRNA expression and upregulating ALP and OPG mRNA levels. Furthermore, COL1 kDa also inhibited bone density loss in osteoporotic mice. Taken together, CP may reduce RANKL‐induced osteoclast activity while promoting osteoblast synthesis, and therefore may act as a potential therapeutic agent for the prevention and control of osteoporosis. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop a scale to measure students' blended learning course experience. A total of 792 undergraduate students from Malaysia participated in this study. Exploratory factor analysis (EFA) was employed to evaluate the factor structure of the scale. As a result of EFA, three factors with 19 items that explained 68.06% of the total variance were obtained. The three factors are: course design, learning experience, and personal factors. Confirmatory factor analysis (CFA) was used to establish the structural validity of the scale, and item discrimination was conducted to validate the scale. The Cronbach's coefficients (α) for the factors ranged from.71 to.95. The overall Cronbach's coefficient (α) of the scale was.92, demonstrating a satisfactory level of reliability. The results showed that the developed scale is reliable and valid. Future implications for course designers and instructors are discussed. [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]

Osteoporosis is characterized by low bone mass, bone microarchitecture disruption, and collagen loss, leading to increased fracture risk. In the current study, collagen peptides were extracted from milkfish scales (MS) to develop potential therapeutic candidates for osteoporosis. MS was used to synthesize a crude extract of fish scales (FS), collagen liquid (COL), and hydroxyapatite powder (HA). COL samples were further categorized according to the peptide size of total COL (0.1 mg/mL), COL < 1 kDa (0.1 mg/mL), COL: 1-10 kDa (0.1 mg/mL), and COL > 10 kDa (0.1 mg/mL) to determine it. Semi-quantitative reverse transcription polymerase chain reaction (sqRT-PCR) and immunofluorescence labeling were used to assess the expression levels of specific mRNA and proteins in vitro. For in vivo studies, mice ovariectomy (OVX)-induced postmenopausal osteoporosis were developed, while the sham surgery (Sham) group was treated as a control. Collagen peptides (CP) from MS inhibited osteoclast differentiation in RAW264.7 cells following an insult with nuclear factor kappa-B ligand (RANKL). CP also enhanced osteoblast proliferation in MG-63 cells, possibly through downregulating NFATc1 and TRAP mRNA expression and upregulating ALP and OPG mRNA levels. Furthermore, COL1 kDa also inhibited bone density loss in osteoporotic mice. Taken together, CP may reduce RANKL-induced osteoclast activity while promoting osteoblast synthesis, and therefore may act as a potential therapeutic agent for the prevention and control of osteoporosis., Competing Interests: The authors report no conflict of interest in relation to the work., (© 2023 The Authors. Food Science & Nutrition published by Wiley Periodicals LLC.)