Background and AimsPhubbing can be described as an individual looking at his or her mobile phone during a conversation with other individuals, dealing with the mobile phone and escaping from interpersonal communication. In this research, determinants of phubbing behavior were investigated; in addition, the effects of gender, smart phone ownership and social media membership were tested as moderators.MethodsTo examine the cause–effect relations among the variables of the theoretical model, the research employs a correlational design. Participants were 409 university students who were selected via random sampling. Phubbing was obtained via the scales featuring mobile phone addiction, SMS addiction, internet addiction, social media addiction and game addiction. The obtained data were analyzed using a correlation analysis, multiple linear regression analysis and structural equation model.ResultsThe results showed that the most important determinants of phubbing behavior are mobile phone, SMS, social media and internet addictions.DiscussionAlthough the findings show that the highest correlation value explaining phubbing is a mobile phone addiction, the other correlation values reflect a dependency on the phone.ConclusionsThere is an increasing tendency towards mobile phone use, and this tendency prepares the basis of phubbing.

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The aim of this work is to analyze both the reasons that young adults exhibit phubbing behaviors and the effects of these behaviors on their lives. Following a qualitative research approach, this study has been designed using a case study method. Selected using the criterion sampling method, participants consisted of a total of 9 university students studying in a School of Education in Turkey. The criterion used to include individuals into the sample group was 'using a smartphone instead of actively participating in an on-going discussion being had by one' s surrounding peers' as well as a high score on the Phubbing Scale form. Data were obtained using a semi-structured interview form composed of 18 open-ended questions. Both a descriptive and content analysis were used to evaluate the data. The study' s findings revealed that not only did smartphones negatively affect interpersonal relations, but also that phubbers lacked communication skills, experienced difficulty establishing and maintaining eye contact while using a smartphone, and misunderstood what was being discussed. Some participants even stated that they would become completely disconnected from the social environment while using a smartphone. It is argued in the discussion section that since smartphones have all the same features and abilities as traditional computers, they carry the same potential addictions as computers. Yet, instead of being restricted to a table, these addictions are now able to manifest in every aspect, environment, and moment of one' s life. As such, since phubbing carries with it a type of addiction that is much more devious and pervasion than most previous virtual reality related addictions, it is recommended that further research be conducted before serious psychopathological and sociological problems begin to manifest themselves throughout the population.

Bu çalışmada, Türkiye'de eğitim yönetimi alanının önemli yayın organlarından bir olan Kuram ve Uygulamada Eğitim Yönetimi Dergisi'nde 2003-2013 yıllarında yayınlanmış makalelerin tematik, metodolojik ve istatistikî çözümleme teknikleri açısından analizi yoluyla bir durum değerlendirmesi yapılmaya çalışılmıştır. Çalışma metodolojik değerlendirme çalışması olarak düzenlenmiştir. Araştırma, Kuram ve Uygulamada Eğitim Yönetimi Dergisi'nde 2003-2013 yılları arasında yayınlanmış 266 makale üzerinde yürütülmüştür. Doküman analizi yoluyla toplanan veriler frekans ve kategorik analizler yardımıyla çözümlenmiştir. Bulgular, yayımlanan makalelerde ele alınan temaların kısmen dar kapsamlı olduğunu, araştırma modeli olarak daha çok tarama modelinin kullanıldığını ve istatistiksel veri çözümleme işlemlerinde t-testi ve ANOVA analizlerinin ağırlıklı olarak kullanıldığını göstermektedir.

The new coronavirus (COVID-19) infection reported in China in December 2019 has become a pandemic in a few weeks, affecting the entire world. In this respect, it is crucial to determine the case-increase, case-fatality, and case-recovery rates to control COVID-19. In this study, the case-increase, case-fatality, and case-recovery rates of COVID-19 in 36 European countries were analyzed with the meta-analysis method using data released by the health organizations and WHO. The data were obtained from the website of health organizations of 36 European countries and the website of WHO until 11 May 2020. The analyses were carried out on 1 744 704 COVID-19-diagnosed cases in 36 European countries. The case-increase, casefatality and case-recovery rates of COVID-19 were calculated using 95% confidence intervals (95% CI), single-arm meta-analysis, cross-temporal meta-analysis, and meta-regression random-effects model. The standardized case-increase rate of COVID-19 is 5% (95% CI [0.040, 0.063]) and the average case-increase rate in European countries has started to decline by around 3% (95% CI [0.047, 0.083])weekly. The countries with the highest rate of case increase are Belgium, Sweden, Russia, the Netherlands and the United Kingdom. Although the case-fatality rate of COVID-19 patients was 4.5% as of May 11 (95% CI [0.037-0.055]), this rate is 6.3% (95% CI [0.047, 0.083]) in standardized time (6th week). The case-recovery rates of patients are 46% (95% ). This study presents important results regarding the COVID-19 pandemic in Europe. Although the rate of increase in new COVID-19 cases has dropped, there is not much decline in the case-fatality rates and no increase in case-recovery rates. The case-fatality rate of COVID-19 in Europe was estimated to be in the range of 4% to 4.5% and a minimum of 4 weeks (as of 11 May) is expected to have the figure below 1% in a country with an average caseincrease rate. Monitoring case fatalities in Belgium, the Netherlands and Sweden, and treatment successes in Germany and Austria play a role of utmost importance.

H H igher education in India has expanded very fast since the early 1990s. Compared to 3.6 million students enrolled in 5,227 institutions of higher education in 1985-86 (University Grants Commission [UGC], 1987), the system has grown to nearly one thousand universities, 39 thousand colleges with nearly 37 million students in 2017-18 (Ministry of Human Resource Development [MHRD], 2018). Almost all branches of higher education have experienced high growth. Among the many branches, engineering education has grown relatively fast. In 1985-86, there were 180,000 enrolments in engineering and technology, constituting 3.4% of the total enrolments in higher education. By 2017-18, the student numbers increased to 4.8 million, and the number of students in engineering education increased more than four-fold, to 16% of the total (UGC, 2018). But all streams of engineering education have not grown fast. There are as many as 17 streams (or sub-streams) of engineering education being offered in Indian institutions. The top five sub-streams offered at first degree level in Indian institutes of engineering education in 2017-18 were mechanical engineering with 880,000 students, computer engineering with 830,000 students, electronics engineering with 650,000 students, civil engineering with 590,000 Hindistan'da mühendislik e¤itimi son otuz y›lda h›zl› bir ivme kaydetmifltir. Bununla birlikte, mühendislik e¤itiminin tüm dallar› ayn› h›zda büyümemifltir. Makine, inflaat ve elektrik mühendisli¤i gibi geleneksel alanlar uzun zamand›r pek ra¤bet görmemekte, ancak elektronik mühendisli¤i, bilgisayar bilimleri mühendisli¤i ve bilgi teknolojisi ile ilgili mühendislik alanlar› ise son y›llarda h›zla geliflmektedir. Ortaokul mezunlar› ileride uzmanlaflacaklar› alanlar› seçme konusunda ak›lc› bir seçim yapmak zo-rundad›r. Bu çal›flma, Hindistan'daki dört farkl› eyalette bulunan 40 mühendislik fakültesine kay›tl› yaklafl›k 7.000 ö¤rencinin cevaplad›¤› anketle toplanan verileri kullanarak, ö¤rencilerin 'geleneksel' ve 'modern' / 'bilgi teknolojileri ile ilgili' mühendislik dallar› aras›ndaki seçimlerini aç›klayan belirleyicileri probit regresyon denklemi yoluyla incelemeyi amaçlam›flt›r. Ö¤rencilerin bireysel ve hane halk› özellikleri, akademik geçmifli, mevcut e¤itimin özellikleri, gelecekteki istihdam beklentileri ve e¤itim hedefleri gibi baz› temel faktörler belirlenmifl, probit analizinde kullan›lm›fl ve sonuçlar ayr›nt›l› olarak tart›fl›lm›flt›r.

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