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Using Student Approaches to Learning research as a theoretical framework, the present study used both self-reported and observational log data to understand students’ study approaches in a flipped classroom course amongst 143 computer science undergraduate students. Specifically, it aimed to examine: 1) to what extent students’ study approaches identified by self-reported and observational log data are consistent with each other; and 2) to what extent students’ academic learning outcomes differ between students who showed consistent and inconsistent study approaches by self-reported and observational log data. Using The Revised Study Process Questionnaire, students were clustered as reporting either a Deep or a Surface Study Approach. Using frequencies of students’ participation in five online learning activities, they were classified as adopting either an Active or a Passive Study Approach. A 2 x 2 cross-tabulation showed a positive and moderate association between clusters of students’ study approaches resulted from two types of data. Amongst students who self-reported a Deep Study Approach, the proportion of students who adopted an Active Study Approach (80.7%) was significantly higher than those who adopted a Passive Study Approach (19.3%). In contrast, of the students who self-reported a Surface Study Approach, the proportion of students who used a Passive Study Approach (51.2%) was significantly higher than those who used an Active Study Approach (48.8%). Furthermore, students who had good study approaches by both self-report and observation did not differ from students who adopted an Active study approach by observation but reported a Surface Study Approach on course grades. Likewise, there was no significant difference in terms of academic learning outcomes between those who had poor study approaches by both self-report and observation and those who adopted Passive study approach by observation but reported a Deep Study Approach. Future studies may consider incorporating some qualitative methods in order to find out possible reasons behind the inconsistencies between self-reported and observed study approaches.

Adopting an ecological perspective on student learning, this study compares the cognitive, social, and material elements of the learning experience of 193 domestic and 120 international students in blended courses. For cognitive elements, domestic students adopted relatively more deep approaches to learning while international students held more positive perceptions towards the blended learning environment. In terms of the social elements, domestic group had a higher proportion of students who chose to collaborate than international group. The domestic and international students also differed with regard to their experience of material elements that international students were more likely to select the option of showing the answer for solving multiple choice questions. International students particularly need help in the improvement of their collaborative learning experience, which may be enhanced by strategies, such as mixing domestic and international students in group work and training international students’ intercultural communication strategies.

This study examined the effect of using a digital game-based language learning mobile application “Liulishuo” (speaking English fluently) to develop complexity, accuracy, and fluency of English monologic oral production among 30 English language learners in China. Monologic oral production was measured using the same narrative picture description task in pre- and post-tests. The learners followed the “Imitation of English Monologues” game 30 minutes each time, twice a week, for 20 weeks. The oral production was measured using six indices: the mean words per T-unit and lexical density (i.e., complexity), the mean repairs and errors per 100 words (i.e., accuracy), speech rate and the mean length of pauses (i.e., fluency). The paired sample t -tests showed that the participants produced more complex monologic speech, had significantly fewer errors, and increased speech rate, but the mean repairs and mean length of pauses remained unchanged. The unchanged repairs and pauses could be possibly due to the non-proceduralized linguistic knowledge in oral production, which may require a more extended period of treatment. Our study showed positive effects of using a digital game-based language learning mobile application on the improvement of complexity, accuracy, and fluency of English language learners in China’ monologic oral production with varying effects.

Although flipped classrooms have been increasingly adopted in higher education, there is a lack of investigations on the interrelations between students’ perceptions of learning environment, learning approach and time, and academic achievement in flipped classrooms. This study examined the extent to which the relations between students’ perceptions of learning environment, academic achievement, and learning approach and time measured by self-reported and log data were consistent among 271 Australian undergraduates. The self-reported data identified two clusters of students reporting using either deep or surface learning approach more frequently. Students who reported using deep approach more frequently also obtained higher academic achievement. The log data also identified two clusters of students using either a preparation-oriented or an assessment-oriented learning approach, with the preparation-oriented learners having higher academic achievement than the assessment-oriented learners. Students with more or less favourable perceptions of learning environment demonstrated differences in their learning approach and time though the differences were not consistent across the self-reported and log data. While learning approach and time measured by both types of data significantly and positively predicted course marks; the observed learning approach and time could explain more variance in students’ course marks. These results seemed to suggest that log data could be more sensitive and accurate indicators of learning approach and time. To foster high quality learning in flipped classrooms, teachers should raise students’ awareness of the importance of pre-lecture online learning activities and design assessment tasks aligning learning activities.

This study explores the structure of motivation in foreign language (FL) reading among 186 university English language learners (ELLs) in China, and investigated the relations between motivation, strategy use, frequency, and proficiency in FL reading. The data were collected from three questionnaires and the reading section of a mock College English Test (Band 4). The data were analyzed using exploratory factor analysis (EFA), correlation, hierarchical cluster analysis, and t tests. The EFA generated three scales, one for intrinsic motivation (i.e., the Efficacy and Engagement scale) and two for extrinsic motivation (i.e., the Utility Value scale and the Academic Value scale). The correlation analyses showed that only the Efficacy and Engagement scale was significantly correlated with reading proficiency. The cluster analysis identified two groups of students, who differed in the three motivation scales as their motivational profiles. Based on the motivational profiles, differences on strategy use, frequency, and proficiency in English were also observed.

E-tailers such as Amazon and Tmall can accurately recognize consumer interest in product categories and grasp current consumer trends through product recommendation algorithms and data-driven analysis. In this study, we develop a game-theoretic model to investigate the encroachment and information sharing decisions considering data-driven marketing (DDM). Our outcomes reveal that the manufacturer has the incentive to reduce the wholesale price to incentivize the e-tailer to increase the DDM effort when the spillover effect is high and the marginal cost is low. When the manufacturer encroaches on the direct channel, the e-tailer may share information if the spillover effect is low. Moreover, we derive the conditions under which the manufacturer encroaches on the direct channel. Lastly, we extend the model to the case where the agency channel exists, and we find that the manufacturer should select the agency channel only if the marginal cost of DDM effort is moderate and the commission rate is low. Our study provides useful insights for managers to understand and make channel choice and information sharing decisions in the e-commerce supply chain.

This study investigated the relations between the five important external factors outlined in the General Extended Technology Acceptance Model for E-Learning and the four core components in Technology Acceptance Model (Perceived Usefulness: PU, Perceived Ease of Use: PEOU, Behavioural Intention of Use: BIU, and Actual Use: AU). It also examined the extent to which these relations differed by academic majors (STEM versus non-STEM). A total of 745 Chinese undergraduates answered a Likert-scale questionnaire. The data were analysed using structural equation modeling, measurement invariance, and multi-group analysis. Except Computer Anxiety, the other four external factors were significantly and positively associated with either PU or PEOU. Although there were no significant differences in the total effects of the five external factors on PU, differences were found in the effects from Computer Self-efficacy, Subjective Norm, and Prior Experience on PEOU between STEM and non-STEM students. Furthermore, the effects of PEOU on PU and BIU were stronger for non-STEM undergraduates; whereas the path from PU to BIU was stronger for STEM undergraduates. The results suggested that different strategies should be implemented according to students’ academic majors to encourage Chinese undergraduates to actively adopt e-learning. For Chinese STEM undergraduates, emphasis should be placed to improve their perceptions of the usefulness aspect in e-learning via strategies such as incorporating gamified elements and engaging features into the learning activities. It is important to enable non-STEM students to operate and navigate the e-learning systems free of effort through essential orientation programs and quality technical support.

Metallabenzenes are recognized as a unique class of aromatic compounds, not only of structural and theoretical interest but also as platforms to design powerful transformations. Here, we report the successful transmutation of a metallabenzene for pyridine synthesis. This ‘metal-to-nitrogen swapping’ process utilizes readily available ruthenabenzene phosphonium salts and commercially available 2-aminopyridines under mild conditions. The isolation of ruthena-azepines, containing a planar seven-membered aza-metallacycle, along with DFT calculations, supports the nitrogen insertion/metal deletion cascade driven by aromatization. Additionally, we investigate the tunable photophysical properties of the resulting pyridine phosphonium salts. Metallabenzenes are recognized as a unique class of aromatic compounds, not only of structural and theoretical interest but also as platforms to design powerful transformations. Here, the authors report the successful transmutation of a metallabenzene for pyridine synthesis.

Background Type 2 diabetes mellitus (T2DM) is a major driver of coronary artery disease (CAD). Prior studies often conflate T2DM- and CAD-specific metabolic alterations, limiting insights into CAD pathogenesis in T2DM. This study aimed to distinguish CAD-unique signatures from T2DM-specific dysmetabolism, and to identify potential metabolic biomarkers for CAD risk escalation in T2DM patients. Methods We performed an untargeted plasma metabolomic study with 123 healthy controls (HCs), 50 T2DM patients without CAD, and 155 T2DM patients with CAD. T2DM_CAD was defined as T2DM diagnosed at least 5 years prior to CAD, with coronary angiography-confirmed stenosis (> 30%) in major coronary arteries. Differential metabolites were identified via intergroup comparisons, with T2DM-specific and CAD-specific signatures distinguished based on unique expression patterns. Machine learning models were developed to evaluate the discriminatory performance of these metabolites for CAD. Results Plasma metabolomic profiling identified distinct metabolic patterns across the three cohorts. Metabolites specific to T2DM were enriched in carbohydrates and certain lipid species, reflecting disturbances in glucose and lipid metabolism. CAD-specific metabolites were predominantly lipids and organic acids, with notable involvement in amino acid and fatty acid metabolic pathways. Several metabolites changed progressively from HCs through T2DM to T2DM_CAD, reflecting advancing metabolic dysregulation, whereas others showed opposing trends, suggesting compensatory or protective adaptations. Integration of key metabolites with clinical parameters in machine learning models effectively distinguished between study groups, demonstrating promising performance for CAD risk assessment in T2DM patients. Conclusions These findings disentangle T2DM- and CAD-specific metabolic disturbances and identify escalation/de-escalation features of CAD risk in diabetic patients, which are potential candidates for future risk stratification pending validation. Graphical abstract

One of the major objectives of precision education is to improve prediction of educational outcome. This study combined theory-driven and data-driven approaches to address the limitations of current practice of predicting learning outcomes only using a single approach. The study identified the online learning patterns by using students' self-reported approaches and perceptions of online learning and by using the observational digital traces of the sequences of their online learning events in a blended course. The study examined predictions of the academic performance using the online learning patterns generated by the two approaches separately. It also investigated the extent to which the online learning patterns identified by the two approaches were associated with each other. The theory-driven approach adopted a hierarchical cluster analysis using the self-reported data and found a 'deep' and a 'surface' online learning patterns, which were related to differences in the academic performance. The data-driven approach used an agglomerative sequence clustering and detected four patterns of online learning, which not only differed by quantity (number of learning events), but also differed by quality (the proportions of types of learning events). A one-way ANOVA revealed that the online learning pattern which had the most learning events, and was characterized by high proportions of viewing course contents and of performing problem-solving exercises, had the highest academic performance. A cross-tabulation revealed significant association between the self-reported and observational online learning patterns, demonstrating consistency of the evidence by a theory-driven and a data-driven approach and triangulating the results of the two approaches.