This study examines the effect of mobile learning applications on undergraduate students' academic achievement, attitudes toward mobile learning and animation development levels. Quasi-experimental design was used in the study. Participants of the study were students of the Buca Faculty of Education at Dokuz Eylul University in Turkey. The experiment was conducted during the first semester of 2013-2014 academic year. A mobile learning-based strategy was used in experimental group (n?=?15), while the control group participated in a lecture-based classroom (n?=?26). An attitude scale was used to measure the students’ attitudes toward mobile learning, and achievement test was used to examine the effect of mobile learning applications on the students’ achievement. In order to evaluate the animations developed by students, a rubric was used. For exploratory analysis, interviews were conducted with students. The findings suggest that mobile learning may promote students' academic achievement. Both groups had significantly high attitude scores toward mobile learning. Furthermore, the students appreciated mobile learning as an approach that may significantly increase their motivation. Researchers and practitioners should take into consideration that mobile learning can create positive impact on academic achievement and performance and increase the motivation of students.

Background It is unlikely that applications of artificial intelligence (AI) will completely replace physicians. However, it is very likely that AI applications will acquire many of their roles and generate new tasks in medical care. To be ready for new roles and tasks, medical students and physicians will need to understand the fundamentals of AI and data science, mathematical concepts, and related ethical and medico-legal issues in addition with the standard medical principles. Nevertheless, there is no valid and reliable instrument available in the literature to measure medical AI readiness. In this study, we have described the development of a valid and reliable psychometric measurement tool for the assessment of the perceived readiness of medical students on AI technologies and its applications in medicine. Methods To define medical students’ required competencies on AI, a diverse set of experts’ opinions were obtained by a qualitative method and were used as a theoretical framework, while creating the item pool of the scale. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied. Results A total of 568 medical students during the EFA phase and 329 medical students during the CFA phase, enrolled in two different public universities in Turkey participated in this study. The initial 27-items finalized with a 22-items scale in a four-factor structure (cognition, ability, vision, and ethics), which explains 50.9% cumulative variance that resulted from the EFA. Cronbach’s alpha reliability coefficient was 0.87. CFA indicated appropriate fit of the four-factor model (χ2/df = 3.81, RMSEA = 0.094, SRMR = 0.057, CFI = 0.938, and NNFI (TLI) = 0.928). These values showed that the four-factor model has construct validity. Conclusions The newly developed Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was found to be valid and reliable tool for evaluation and monitoring of perceived readiness levels of medical students on AI technologies and applications. Medical schools may follow ‘a physician training perspective that is compatible with AI in medicine’ to their curricula by using MAIRS-MS. This scale could be benefitted by medical and health science education institutions as a valuable curriculum development tool with its learner needs assessment and participants’ end-course perceived readiness opportunities.

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In this study, we aimed to adapt the Information and communication technology (ICT) Overload and social networking service (SNS) Fatigue Scale to measure the overload and SNSs fatigue experienced by individuals while using ICTs in the Turkish language and analyze the adapted scale based on various variables. The scale adaptation procedure was conducted by surveying 225 undergraduate-level university students. In addition to discriminant and convergent reliability, the general fitness index parameters were compared with confirmatory factor analysis (CFA), and the model results were found in accordance with the acceptable fitness index criteria, with clarification as a complete model in all sub-dimensions. The relationships between fear of missing out (FoMO), problematic smartphone use (PSU), and SNSs Fatigue levels of the participants were also investigated. The adapted scale was then applied to 469 participants. The findings demonstrated that there was a significant difference between PSU and SNS Fatigue levels of participants based on gender, favoring females. It was also revealed that the variables of interest FoMO and SNS Fatigue together predicted the PSU.

This present study was conducted with the purpose of developing an attitude scale towards mobile learning. Related literature was reviewed primarily in the process of developing the scale. Then by seven open-ended questions related with mobile learning were asked to 78 students. Taking the advantages of the data collected and the expert opinions, an item pool including 57 items was developed. In order to insure the content validity, the experts' opinions were used. In accordance with the recommendations and the opinions of the experts, the items were re-evaluated. The items which did not include attitude expression or which were alike were removed from the scale item pool. After making the revisions, the item pool included 52 items. These items created as five-point Likert-type and rated as totally agree (5), agree (4), partially agree (3), disagree (2), totally disagree (1). The pilot study was conducted with 326 undergraduate students, who studies in different departments in Dokuz Eylül University, Buca Faculty of Education and Anadolu University, Faculty of Education, in the first term of the 2013-2014 academic year. KMO value found as .936. 21 items of the scale grouped by four factors and explain the %51.116 of the total variance of the scale is determined in the result of factor analysis. 45 items which item load is higher than .40 were included to scale. The last version of the scale consists four factors and 45 items which loadings are between .82 and .40. The Cronbach's alpha internal consistency coefficient which belongs the last version of the scale was computed as .950 and was seen as very highly reliable. The item analysis based on the difference between the upper and lower group results of the all items in the scale found significantly distinguished (p<.05). This research is expected to contribute researchs which will be held in the field of mobile learning with undergraduate students.