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This meta-analysis examines the inconsistent findings across experimental studies that compared children's learning outcomes with digital and paper books. We quantitatively reviewed 39 studies reported in 30 articles (n = 1,812 children) and compared children's story comprehension and vocabulary learning in relation to medium (reading on paper versus on-screen), design enhancements in digital books, the presence of a dictionary, and adult support for children aged between 1 and 8 years. The comparison of digital versus paper books that only differed by digitization showed lower comprehension scores for digital books. Adults' mediation during print books' reading was more effective than the enhancements in digital books read by children independently. However, with story-congruent enhancements, digital books outperformed paper books. An embedded dictionary had no or negative effect on children's story comprehension but positively affected children's vocabulary learning. Findings are discussed in relation to the cognitive load theory and practical design implications.

Emerging research is highlighting the importance of fostering artificial intelligence (AI) literacy among educated citizens of diverse academic backgrounds. However, what to include in such literacy programmes and how to teach literacy is still under-explored. To fill this gap, this study designed and evaluated an AI literacy programme based on a multi-dimensional conceptual framework, which developed participants' conceptual understanding, literacy, empowerment and ethical awareness. It emphasised conceptual building, highlighted project work in application development and initiated teaching ethics through application development. Thirty-six university students with diverse academic backgrounds joined and completed this programme, which included 7 hours on machine learning, 9 hours on deep learning and 14 hours on application development. Together with the project work, the results of the tests, surveys and reflective writings completed before and after these courses indicate that the programme successfully enhanced participants' conceptual understanding, literacy, empowerment and ethical awareness. The programme will be extended to include more participants, such as senior secondary school students and the general public. This study initiates a pathway to lower the barrier to entry for AI literacy and addresses a public need. It can guide and inspire future empirical and design research on fostering AI literacy among educated citizens of diverse backgrounds.

IntroductionFast Healthcare Interoperability Resources (FHIR), is a standard for exchanging data. It is a common framework that makes it easier to read, write, and transfer medical data, such as patient information securely. SMART is a platform that builds upon the FHIR specification and provides developers with a set of APIs to create applications on top of FHIR. These applications range from retrieving a patient‘s medication history, to evaluating a patient‘s risk of cardiac arrest. The aim of this project was to help individual doctors, small teams of developers, or large medical organisations, who may not be familiar with FHIR, to discover the capabilities of SMART APIs and build applications for this next generation of digital healthcare.MethodAs part of a joint collaboration between GOSH and UCL computer science (CS), through the industry exchange network programme. CS Students developed a web application using Django, a framework written in Python that employs a model-template-view (MTV) pattern. The client-side templates were built using React. The application’s back-end encapsulates the app’s logic; and interacts with the data persistency layer- a SQLite database. The code snippets are run by querying the SMART STU3 Sandbox.ResultsA functional web application was developed that collates a catalogue of modular SMART functions that a developer can use to implement in their own application. The platform allows non-coders to explore SMART on FHIR components and develop prototype applications. It has a library of runnable code snippets that can act as a helpful tool and reference when building SMART applications.ConclusionThis application supports the development of SMART applications that adhere to FHIR standards in data interoperability, for developers who lack specific knowledge of FHIR standards. Such resources are likely to be of increasing importance as NHS organisations begin to develop customised local programmes using SMART on FHIR.

Self-regulated learning (SRL) is crucial for helping students attain high academic performance and achieve their learning objectives in the online learning context. However, learners often face challenges in properly applying SRL in online learning environments. Recent developments in artificial intelligence (AI) applications have shown promise in supporting learners’ self-regulation in online learning by measuring and augmenting SRL, but research in this area is still in its early stages. The purpose of this study is to explore students’ perceptions of the use of AI applications to support SRL and to identify the pedagogical and psychological aspects that they perceive as necessary for effective utilization of those AI applications. To explore this, a speed dating method using storyboards was employed as an exploratory design method. The study involved the development of 10 AI application storyboards to identify the phases and areas of SRL, and semi-structured interviews were conducted with 16 university students from various majors. The results indicated that learners perceived AI applications as useful for supporting metacognitive, cognitive, and behavioral regulation across different SRL areas, but not for regulating motivation. Next, regarding the use of AI applications to support SRL, learners requested consideration of three pedagogical and psychological aspects: learner identity, learner activeness, and learner position. The findings of this study offer practical implications for the design of AI applications in online learning, with the aim of supporting students’ SRL.

Network meta‐analysis (NMA) compares several interventions that are linked in a network of comparative studies and estimates the relative treatment effects between all treatments, using both direct and indirect evidence. NMA is increasingly used for decision making in health care, however, a user‐friendly system to evaluate the confidence that can be placed in the results of NMA is currently lacking. This paper is a tutorial describing the Confidence In Network Meta‐Analysis (CINeMA) web application, which is based on the framework developed by Salanti et al (2014, PLOS One, 9, e99682) and refined by Nikolakopoulou et al (2019, bioRxiv). Six domains that affect the level of confidence in the NMA results are considered: (a) within‐study bias, (b) reporting bias, (c) indirectness, (d) imprecision, (e) heterogeneity, and (f) incoherence. CINeMA is freely available and open‐source and no login is required. In the configuration step users upload their data, produce network plots and define the analysis and effect measure. The dataset should include assessments of study‐level risk of bias and judgments on indirectness. CINeMA calls the netmeta routine in R to estimate relative effects and heterogeneity. Users are then guided through a systematic evaluation of the six domains. In this way reviewers assess the level of concerns for each relative treatment effect from NMA as giving rise to “no concerns,” “some concerns,” or “major concerns” in each of the six domains, which are graphically summarized on the report page for all effect estimates. Finally, judgments across the domains are summarized into a single confidence rating (“high,” “moderate,” “low,” or “very low”). In conclusion, the user‐friendly web‐based CINeMA platform provides a transparent framework to evaluate evidence from systematic reviews with multiple interventions.

Aims‘This may take a while’ is the message in the bottom corner of the computer screen while waiting for applications to load. This is little comfort when attempting to be time efficient with stretched staffing resources. Does it take as long as we believe?We investigated time taken for computers to open the wanted application from shutdown or lock screen on a neonatal unit in a large hospital. We surveyed doctors perceptions of time spent waiting for computers. We estimated the number of times per shift doctors used computers.MethodsStaff recorded the time using their smart phones from shutdown or lock screen to having a computer application they required useably open. The date, time and computer application were recorded on data sheets attached to each computer. We collected how many times a doctor logged onto the computers during shifts of varying lengths. Questionnaires were distributed to doctors asking them to estimate the time spent per shift waiting for computers to log in and how they felt about the speed of login times.Results14 doctors answered the survey. Average (95% confidence intervals of the mean) estimated waiting during shifts was 24.1(16.9-31.4) minutes. Text responses found doctors felt ‘frustrated’ at the login times being ‘too slow’ and ‘unsafe during emergencies’.Data were recorded from 23 computers, on 127 occasions, for 13 applications. It took 147.9(128.7-165.1) seconds for the application to be usable. The fastest computer took 62.5(34.1-90.9) seconds, statistically significantly faster than 7 others. The slowest took 345.7(138.0-553.4) seconds, statistically significantly slower than 7 others. There were no differences between applications speeds.From shutdown it took longer than from lock screen: 179.6(140.4-218.8) versus 125.7(111.5-140.0) seconds (p=0.014). Time of day mattered: between 6am-10pm took 154.1(134.4-173.8) versus from 10pm-6am 84.0(64.5-103.5) seconds (p<0.001). Looking hourly slowest was 8-9am 247.9(192.2-303.7) and fastest 4-5pm 85.0(54.0-116.0) seconds. The day of the week made no difference.Over 12 shifts of varying lengths one doctor recorded using a computer 67 times, with an average of 0.53(0.35-0.71) per hour. Using that data, during a 13 hour shift the expected waiting time would be 17.0(9.9-25.6) minutes.ConclusionDoctors’ estimate of waiting was longer, but not statistically significantly so than calculated times. Waiting was longer from shutdown, about 3 minutes, than from lock screen, about 2 minutes. It took about 4 minutes to get a useable application between 8-9am. Some of the variations seem to be the specific computer. However, some of the delays may be due to the lag in the hospital network communicating to the computer at the start of the day, as many office staff simultaneously turn on computers. Having computers in clinical areas turned on, but to a lock screen prior to 8am could cut down waiting times. Our data suggests that non-urgent tasks should be completed after 9am to reduce time spent waiting. The frustration felt by doctors seems justified as up to 25.6 minutes of a shift is spent waiting, suggesting that new strategies to improve computer speeds need to be investigated.

Mobile-learning (M-learning) apps have grown in popularity and demand in recent years and have become a typical occurrence in modern educational systems, particularly with the deployment of M-learning initiatives. The key objective of this study was to reveal the key factors that impact university students’ behavioural intention and actual use of mobile learning in their education. The technology acceptance model (TAM) is used in this study to investigate the impacts of several factors found in the literature on students' adoption of M-learning systems in higher education. The data was gathered from 176 university students who completed a paper questionnaire. The data was analyzed using the SEM technique. The findings revealed that perceived mobile value (PMV), academic relevance (AR), and self-management of M-learning (SML) are the primary drivers of students' acceptance of M-learning and, as a result, the success of M-learning projects’ implementation. The findings of this study give crucial information on how higher education institutions may improve students' acceptance of M-learning in order to promote students' attitudes toward M-learning (ATT) it and their behavioural intentions (BIM) to use it in the teaching and learning process. These findings have significant implications for the acceptance and use of M-learning.

Purpose Online shopping has continued to grow in popularity, and the advance of internet technology has enhanced customers’ experiences. One technology online retailers have been using to increase sales is virtual try-on (VTO). The purpose of this paper is to investigate how such technology affects online consumers’ purchase decision process towards purchase intention, especially from an integration of utilitarian, hedonic and risk perspectives, by using advanced partial least square (PLS) approaches. Design/methodology/approach This study applied a web-based survey approach for data collection from online apparel retailing websites. The survey instrument was developed by adapting previously validated measurement items. The valid data collected were analysed using PLS with multi-group analyses. Advanced PLS techniques such as examination of discriminant validity using heterotrait-monotrait ratio, tests of out-of-sample prediction performance, and measurement invariance of composite models were applied. Findings The results of examining the proposed model reveal that customers’ attitude towards VTO technology can affect their intention to purchase a garment online, which is affected by perceived usefulness, perceived enjoyment and perceived privacy risk. Perceived ease of use is found to affect perceived usefulness and perceived helpfulness. The results also show no significant differences among age groups and genders in terms of the role of VTO technology in the full decision process towards online purchase intention. Originality/value This study enhances the understanding of the roles that VTO technology plays in consumers’ online purchase intention by providing an integrative view of its utilitarian value, hedonic value and risk. This study demonstrates the feasibility of applying advanced PLS techniques to investigate online consumer behaviour, particularly in the field of VTO application in online retailing. Implications for online retailers and designers of VTO technology are also derived from the findings.

PurposeThe engagement–addiction dilemma has been commonly observed in the information technology (IT) industry. However, this issue has received limited research attention in the information system (IS) discipline. Drawing on the stimulus–organism–response (SOR) framework, this study explores the engagement–addiction dilemma in the use of mobile games and highlights the impacts of game design features, namely, mobile user interface and mobile game affordance.Design/methodology/approachThe research model was empirically validated using a longitudinal survey data from 410 mobile game users in China.FindingsThe empirical results offer several key findings. First, mobile user interface and mobile game affordance positively affect telepresence and social presence, which lead to meaningful engagement and mobile game addiction. Second, a high-quality of mobile user interface positively moderates the effects of mobile game affordance on telepresence and social presence.Originality/valueThis study contributes to the literature by theorizing and empirically testing the impacts of game design features on the engagement-addiction dilemma.