IoTFLiP： IoT-based flipped learning platform for medical education

Case-Based Learning （CBL） has become an effective pedagogy for student-centered learning in medical education, which is founded on persistent patient cases. Flippped learning and Internet of Things （IoTs） concepts have gained significant attention in recent years. Using these concepts in conjunction with CBL can improve learning ability by providing real evolutionary medical eases. It also enables students to build confidence in their decision making, and efficiently enhances teamwork in the learning environment. We propose an IoT-based Flip Learning Platform, called IoTFLiP, where an IoT infrastrneture is exploited to support flipped case-based learning in a cloud environment with state of the art security and privacy measures for personalized medical data. It also provides support for application delivery in private, public, and hybrid approaches. The proposed platform is an extension of our Interactive Case-Based Flipped Learning Tool （ICBFLT）, which has been developed based on current CBL practices. ICBFLT formulates summaries of CBL cases through synergy between students＇ and medical expert knowledge. The low cost and reduced size of sensor device, support of IoTs, and recent flipped learning advancements can enhance medical students＇ academic and practical experiences. In order to demonstrate a working scenario for the proposed IoTFLiP platform, real-time data from IoTs gadgets is collected to generate a real-world case for a medical student using ICBFLT.

MET as Program Resources for College English Teaching and Learning：Application and Implications

This paper investigates the current situation of Modern Educational Technology （MET） as program resources for college English teaching, exploring the effectiveness of the integration of MET and language teaching and learning. The case study in this paper illustrates that MET does benefit college English teaching but there are many problems of its application. Pedagogical implications are then provided to enhance the effective integration of MET and language teaching and learning： （1） it is wise for the teachers and learners to reconcile what is desirable with what is acceptable and possible in MET application; （2） teachers should optimize other existing resources to solve the problems caused by inequality and unavailability of MET; （3） teachers should have a clear conception of the relationship of MET and pedagogy to design and apply courseware appropriately; （4） students have beliefs in the benefits of MET, resulting in enhancing their individual language learning and efficiency.

Gender Differences in Motivation and Learning Preferences of Pharmacists in Lifelong Learning

Objectives： To analyse motivation and preferences of pharmacists who participate in CE （continuing education） to develop suitable lifelong learning programmes for pharmacists. Methods： An online questionnaire, which explored the motivation and preferences of the pharmacists to lifelong learning, was sent to all members of the Royal Dutch Pharmaceutical Society （4321） in the Netherlands. The data were analysed using a non-hierarchical clustering technique. Key findings： Two clusters of pharmacists were discovered. Cluster A pharmacists （n = 474） were more motivated by credit points （63.5% vs. 47.2%）, personal interest （84.1% vs. 56.3%）, updating knowledge （73.8% vs. 56.8%） and topicality of CE courses （47.7% vs. 26.1%）. Cluster B pharmacists （n = 199） were predominantly motivated by the aspect ＂duty as a care-giver＂ （97.0% vs. 0 % in cluster A）. Pharmacists who belonged to cluster A tended to be women （60.5%）, often worked part-time （29.3%） and mostly preferred lectures （71.1%）. Cluster B pharmacists consisted of statistically significantly more male pharmacists （52.8%, p = 0.001）, worked more full time （77.4%, p = 0.009） and mostly preferred blended learning （62.3%, p = 0.047）. Conclusions： These results suggest the use of different education formats for different kinds of pharmacists to participate in CE activities.

Effective Face-to-faceTutorials of Online English Learning Lie in Interaction of Affective Domain and Higher Levels of Cognitive Domain of Bloom＇s Taxonomy

Online English learning as an outcome of the rapid development of the Internet has got a wider and wider market in China. However, problems of varieties have also occurred along its way. People never stop thinking of better strategies either in designing online course wares or tutorials to help smooth the learning process. My experience as a tutor is that interaction of affective domain and higher levels of cognitive domain of Bloom＇s Taxonomy plays an important role in face-to-face tutorials of online English learning.

Analysis of the effect of professional teaching staff construction in the training of low-level nurses in operating room

Objective:To explore the application effect of constructing professional teaching staff in low-level training in operating room,so as to further optimize the teaching strength of operating room in our hospital and improve the training effect of junior nurses.Methods:Thirty-eight low-level nurses in the operating room of a third-class hospital in Yantai were selected for half a year's nurse training.With theoretical scores,overall nursing performance,nurses'self-awareness evaluation system and nurses'satisfaction with tutors as evaluation criteria,and based on the selection of high-quality teachers,various evaluation indexes before and after the training of low-level nurses in the operating room were compared and evaluated through the cultivation of practical teaching teachers'ability and the application of a series of teaching methods based on the change of c ompetence-based education(CBE)teaching mode,the application of guided learning interactive canadian education(BOPPPS)teaching model and p roblem-b ased l earning(PBL)teaching method.Results:After the training,the examination scores of low-level nurses were significantly improved(P<0.05),the teaching quality was highly recognized by low-level nurses,the quality of low-level nurses was improved,and patients'satisfaction with nurses was improved.Conclusion:It is of great significance to assist the construction of professional teachers in evidence-based medicine.Through the training of practical teaching teachers'ability and the application of a series of teaching methods based on the change of CBE teaching model,the application of BOPPPS teaching model and PBL teaching method,the training results of nurses have been significantly improved and improved,which is worthy of clinical reference and promotion.

Student Performance Prediction via Attention-Based Multi-Layer Long-Short Term Memory

Online education has attracted a large number of students in recent years, because it breaks through the limitations of time and space and makes high-quality education at your fingertips. The method of predicting student performance is to analyze and predict the student’s final performance by collecting demographic data such as the student’s gender, age, and highest education level, and clickstream data generated when students interact with VLE in different types of specific courses, which are widely used in online education platforms. This article proposes a model to predict student performance via Attention-based Multi-layer LSTM (AML), which combines student demographic data and clickstream data for comprehensive analysis. We hope that we can obtain a higher prediction accuracy as soon as possible to provide timely intervention. The results show that the proposed model can improve the accuracy of 0.52% - 0.85% and the F1 score of 0.89% - 2.30% on the four-class classification task as well as the accuracy of 0.15% - 0.97% and the F1 score of 0.21% - 2.77% on the binary classification task from week 5 to week 25.

Modelling the Dropout Patterns of MOOC Learners

We conduct a survival analysis for the viewing durations of massive open online courses.The hazard function of the empirical duration data presents as a bathtub curve with the Lindy effect in its tail.To understand the evolutionary mechanisms underlying these features,we categorize learners into two classes based on their different distributions of viewing durations,namely lognormal distribution and power law with exponential cutoff.Two random differential equations are provided to describe the growth patterns of viewing durations for the two classes respectively.The expected duration change rate of the learners featured by lognormal distribution is supposed to be dependent on their past duration,and that of the remainder of learners is supposed to be inversely proportional to time.Solutions to the equations predict the features of viewing duration distributions,and those of the hazard function.The equations also reveal the features of memory and memorylessness for the respective viewing behaviors of the two classes.