Student induction experiences: Through the lens of gamification

Student induction serves as the first step of the learning journey, helping students understand the resources, facilities, and supporting infrastructures in the learning environment. A positive induction experience helps improve better learning efficacy and boost performance later on. However, students nowadays complain induction as boring, time-wasting and useless. Given the importance of induction, scholars have called for new research, finding a new way to deliver better-quality and more engaging induction. To respond to this call, the current research aims to investigate whether gamification offers better induction experiences to the students. Gamification is the use of game design techniques, game thinking, and game mechanics in a non-game context. Drawing on the student-centred learning theory, we propose that, through the game-play process, students shall feel less stressed but more confident in learning, leading to a more positive learning experience and outcome. Following the same logic, we hypothesise that gamification is positively correlated with the experiences of induction. That is, gamification-empowered induction brings better experiences to the new students. To examine the research hypothesis, we plan to recruit 200 students (research participants) through flyers and noticeboards during the university induction period in September 2023 (Ethics Approval Ref: ETH2223-0198). The recruitment is operated on a voluntary basis and participants can drop out at any time. Participant Information Letter, Consent Form, and other participant protection measures are arranged in line with the guidance of institutional ethics committee. The participants will be randomly assigned into two conditions. In Condition A, participants will receive a conventional induction through a regular teaching classroom. All documents and instructions are communicated through paper-based handouts. Participants will receive a campus map, explaining the location of buildings and respective services. The induction will be completed inside the classroom. In Condition B, participants will receive gamification-empowered induction. All documents and instructions are communicated through a gamification APP (to be installed in participants’ mobiles). To complete the induction, participants must visit the designated locations in the campus, exploring the services in person. To further understand participants' views and experiences of the induction, we plan to collect data through anonymous questionnaires surveys at the end of induction. Condition A will receive questions through web-based surveys, where Condition B will receive questions through APP-based surveys. Both conditions will receive the same survey questions, and Condition B will receive additional questions of APP-user experiences (A copy of the survey questions is enclosed in appendix). The data collected will be analysed and compared through SPSS and Excel software. Research findings will first and foremost examine whether gamification-empowered induction offers better induction experiences to the students. The answers will bring new insights to the gamification-induction literatures. Research findings will be important to the teaching practitioners and policy makers, particularly for those who wish to create better induction programmes through innovative strategies. Implications on induction design and delivery will be clarified. Research limitation and suggestions for future research will also be discussed

Azithromycin resistance in Escherichia coli and Salmonella from food-producing animals and meat in Europe.

OBJECTIVES To characterize the genetic basis of azithromycin resistance in Escherichia coli and Salmonella collected within the EU harmonized antimicrobial resistance (AMR) surveillance programme in 2014-18 and the Danish AMR surveillance programme in 2016-19. METHODS WGS data of 1007 E. coli [165 azithromycin resistant (MIC > 16 mg/L)] and 269 Salmonella [29 azithromycin resistant (MIC > 16 mg/L)] were screened for acquired macrolide resistance genes and mutations in rplDV, 23S rRNA and acrB genes using ResFinder v4.0, AMRFinder Plus and custom scripts. Genotype-phenotype concordance was determined for all isolates. Transferability of mef(C)-mph(G)-carrying plasmids was assessed by conjugation experiments. RESULTS mph(A), mph(B), mef(B), erm(B) and mef(C)-mph(G) were detected in E. coli and Salmonella, whereas erm(C), erm(42), ere(A) and mph(E)-msr(E) were detected in E. coli only. The presence of macrolide resistance genes, alone or in combination, was concordant with the azithromycin-resistant phenotype in 69% of isolates. Distinct mph(A) operon structures were observed in azithromycin-susceptible (n = 50) and -resistant (n = 136) isolates. mef(C)-mph(G) were detected in porcine and bovine E. coli and in porcine Salmonella enterica serovar Derby and Salmonella enterica 1,4, [5],12:i:-, flanked downstream by ISCR2 or TnAs1 and associated with IncIγ and IncFII plasmids. CONCLUSIONS Diverse azithromycin resistance genes were detected in E. coli and Salmonella from food-producing animals and meat in Europe. Azithromycin resistance genes mef(C)-mph(G) and erm(42) appear to be emerging primarily in porcine E. coli isolates. The identification of distinct mph(A) operon structures in susceptible and resistant isolates increases the predictive power of WGS-based methods for in silico detection of azithromycin resistance in Enterobacterales

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Semantic annotation of high resolution TerraSAR-X images using Information Mining

This paper addresses the problem of High Resolution Synthetic Aperture Radar (SAR) image semantic annotation using a Knowledge Based Information Mining (KIM) System. The authors propose the assessment of the capabilities of KIM to perform an automatic urban classification on TerraSAR-X data. Four test sites have been used in the experiment to prove that the system is generic and data independent

Knowledge based information mining for urban classification using multispectral high resolution images

Space imagery offers great support in various types of applications. The huge amount of information provided in a remote sensed manner facilitates the analysis of Earth surface. The image content classification is one of the first steps to follow in the data mining process

M4: The Mining Mart Meta Model

Deliverable D8/9 EU Funded MINING MART Projec