A comparative analysis of online learning versus face-to-face learning approach in problem based learning in dentistry.

BACKGROUND: Problem-based learning (PBL), a student-centered approach which has been implemented in many educational institutes worldwide. OBJECTIVES: This study aimed to evaluate and compare the proficiency of students in 5 key areas of PBL process and assess perceptions of students towards PBL learning using a self-explanatory questionnaire. METHOD: This cross-sectional study was conducted from March 2020 to September 2022 and evaluated perceptions of 1st year dental students of Batch 2021 (having Face-to-face PBL sessions) and batch 2020 (having online PBL sessions) using a questionnaire. Three similar PBL cases were used, which assessed PBL parameters that included attendance, group dynamics, knowledge, communication skills and preparation. Statistical analysis was performed for all the categorical and continuous variables. RESULTS: Majority of Distance Learning (DL) group (66.7%) concurred that PBL helped improve their communication skills, decision making power (66.7%), self-directed learning (80%) and promoted interaction with peers (66.7) and teachers (68.9%). The Face -to- Face (FF) group demonstrated better scores (7.7±2.42 & 7.55±2.55) with respect to their attendance and group dynamics (7.44±2.17 & 8.02±2.40) in PBL 1 & 3 as compared to DL group. Overall, better performance in all the 5 key areas of PBL was observed for the FF group versus the DL group. CONCLUSION: The Distance learning (DL) group displayed a positive attitude and perception towards PBL as a learning strategy whereas Face-to-face (FF) group showed greater performance and proficiency in the key PBL parameters scores.

Using machine learning to predict factors affecting academic performance: the case of college students on academic probation.

This study aims to employ the supervised machine learning algorithms to examine factors that negatively impacted academic performance among college students on probation (underperforming students). We used the Knowledge Discovery in Databases (KDD) methodology on a sample of N = 6514 college students spanning 11 years (from 2009 to 2019) provided by a major public university in Oman. We used the Information Gain (InfoGain) algorithm to select the most effective features and ensemble methods to compare the accuracy with more robust algorithms, including Logit Boost, Vote, and Bagging. The algorithms were evaluated based on the performance evaluation metrics such as accuracy, precision, recall, F-measure, and ROC curve, and then validated using 10-folds cross-validation. The study revealed that the main identified factors affecting student academic achievement include study duration in the university and previous performance in secondary school. Based on the experimental results, these features were consistently ranked as the top factors that negatively impacted academic performance. The study also indicated that gender, estimated graduation year, cohort, and academic specialization significantly contributed to whether a student was under probation. Domain experts and other students were involved in verifying some of the results. The theoretical and practical implications of this study are discussed.

"There's absolutely no downside to this, I mean, except community opposition:" A qualitative study of the acceptability of vending machines for harm reduction.

BACKGROUND: Vending machines for harm reduction (VMHR) are an innovative approach to deliver life-saving materials, information, and treatment for hard-to-reach populations, particularly for persons who inject drugs. The current study explores stakeholders' perspectives on the feasibility and acceptability of VMHR in Philadelphia. METHODS: From October 2021 to February 2022, we conducted 31 semi-structured interviews with potential end users, staff, and leadership at a local federally qualified health center, and community members. Trained coders extracted themes from interview transcripts across four key domains: materials and logistics, location, access, and community introduction. RESULTS: Interviewees from all stakeholder groups endorsed using VMHR to provide supplies for wound care, fentanyl test strips, naloxone, and materials to connect individuals to treatment and other services. Dispensing syringes and medications for opioid use disorder were commonly endorsed by health center staff but were more controversial among potential end users. Even within stakeholder groups, views varied with respect to where to locate the machines, but most agreed that the machine should be placed in the highest drug use areas. Across stakeholder groups, interviewees suggested several strategies to introduce and gain community acceptance of VMHR, including community education, one-on-one conversations with community members, and coupling the machine with safe disposal of syringes and information to link individuals to treatment. CONCLUSIONS: Stakeholders were generally receptive to VMHR. The current study findings are consistent with qualitative analyses from outside of the USA and contribute new ideas regarding the anticipated community response and best methods for introducing these machines to a community. With thoughtful planning and design, VMHR could be a feasible and acceptable modality to reduce death and disease transmission associated with the opioid and HIV epidemics in cities like Philadelphia.

Correction: Elliptic percolation model for predicting the electrical conductivity of graphene-polymer composites.

Correction for 'Elliptic percolation model for predicting the electrical conductivity of graphene-polymer composites' by Asghar Aryanfar et al., Soft Matter, 2021, 17, 2081-2089, DOI: 10.1039/D0SM01950J.

Elliptic percolation model for predicting the electrical conductivity of graphene-polymer composites.

Graphene-based polymers exhibit a conductive microstructure formed by aggregates in a matrix which drastically enhances their transmitting properties. We develop a new numerical framework for predicting the electrical conductivity based on continuum percolation theory in a two dimensional stochastically-generated medium. We analyze the role of the flake shape and its aspect ratio and consequently predict the onset of percolation based on the particle density and the domain scale. Simultaneously, we have performed experiments and have achieved very high electrical conductivity for such composites compared to other film fabrication techniques, which have verified the results of computing the homogenized electrical conductivity. As well, the proximity to and a comparison with other analytical models and other experimental techniques are presented. The numerical model can predict the composite transmitting conductivity in a larger range of particle geometry. Such quantification is exceedingly useful for effective utilization and optimization of graphene filler densities and their spatial distribution during manufacturing.

Customer experiences in the age of artificial intelligence.

Artificial intelligence (AI) is revolutionising the way customers interact with brands. There is a lack of empirical research into AI-enabled customer experiences. Hence, this study aims to analyse how the integration of AI in shopping can lead to an improved AI-enabled customer experience. We propose a theoretical model drawing on the trust-commitment theory and service quality model. An online survey was distributed to customers who have used an AI- enabled service offered by a beauty brand. A total of 434 responses were analysed using partial least squares-structural equation modelling. The findings indicate the significant role of trust and perceived sacrifice as factors mediating the effects of perceived convenience, personalisation and AI-enabled service quality. The findings also reveal the significant effect of relationship commitment on AI-enabled customer experience. This study contributes to the existing literature by revealing the mediating effects of trust and perceived sacrifice and the direct effect of relationship commitment on AI-enabled customer experience. In addition, the study has practical implications for retailers deploying AI in services offered to their customers.

Non-Transfusion-Dependent Thalassemia: An Update on Complications and Management.

Patients with non-transfusion-dependent thalassemia (NTDT) experience many clinical complications despite their independence from frequent transfusions. Morbidities in NTDT stem from the interaction of multiple pathophysiological factors: ineffective erythropoiesis, iron overload (IOL), and hypercoagulability. Ineffective erythropoiesis and hemolysis are associated with chronic hypoxia and a hypercoagulable state. The latter are linked to a high prevalence of thromboembolic and cerebrovascular events, as well as leg ulcers and pulmonary hypertension. IOL in NTDT patients is a cumulative process that can lead to several iron-related morbidities in the liver (liver fibrosis), kidneys, endocrine glands (endocrinopathies), and vascular system (vascular disease). This review sheds light on the pathophysiology underlying morbidities associated with NTDT and summarizes the mainstays of treatment and some of the possible future therapeutic interventions.