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This was conducted to investigate the views of preservice teachers about learning Web 2.0 tools and using these tools to create educational digital content. The study was designed in qualitative research method. Semi-structured interviews were carried out to collect data from 18 preservice teachers studying in various programs in the faculty of education. The data were analyzed using content analysis. Findings showed that students emphasized the effortlessness of learning Web 2.0 tools and the convenience of designing and integrating educational digital content into the teaching and learning process. Although they indicated their concerns about utilizing these tools, they found these tools useful in terms of attracting attention, increasing learning retention, improving creative thinking and facilitating learning well as practical in terms of saving time, accessing resources and sharing information. They were very contented with using these tools that were supportive to improve the instructional practices in their own field even though very few of them thought differently. Finally, they were willing to utilize these tools in their future classroom settings.

Introduction: The Cognitive Distortions Questionnaire (CD-Quest) is a self-report questionnaire that assesses common cognitive distortions. Although the CD-Quest has excellent psychometric properties, its length may limit its use. Methods: We attempted to develop short-forms of the CD-Quest using RiskSLIM - a machine learning method to build short-form scales that can be scored by hand. Each short-form was fit to maximize concordance with the total CD-Quest score for a specified number of items based on an objective function, in this case R2, by selecting an optimal subset of items and an optimal set of small integer weights. The models were trained in a sample of US undergraduate students (N = 906). We then validated each short-form on five independent samples: two samples of undergraduate students in Brazil (Ns = 182, 183); patients with depression in Brazil (N = 62); patients with social anxiety disorder in the US (N = 198); and psychiatric outpatients in Turkey (N = 269). Results: A 9-item short-form with integer scoring was created that reproduced the total 15-item CD-Quest score in all validation samples with excellent accuracy (R2 = 90.4-93.6%). A 5-item ultra-short-form had good accuracy (R2 = 78.2-85.5%). Discussion: A 9-item short-form and a 5-item ultra-short-form of the CD-Quest both reproduced full CD-Quest scores with excellent to good accuracy. These shorter versions of the full CD-Quest could facilitate measure-ment of cognitive distortions for users with limited time and resources.

Importance A substantial proportion of the 40 million people in the US who present to emergency departments (EDs) each year after traumatic events develop posttraumatic stress disorder (PTSD) or major depressive episode (MDE). Accurately identifying patients at high risk in the ED would facilitate the targeting of preventive interventions. Objectives To develop and validate a prediction tool based on ED reports after a motor vehicle collision to predict PTSD or MDE 3 months later. Design, Setting, and Participants The Advancing Understanding of Recovery After Trauma (AURORA) study is a longitudinal study that examined adverse posttraumatic neuropsychiatric sequalae among patients who presented to 28 US urban EDs in the immediate aftermath of a traumatic experience. Enrollment began on September 25, 2017. The 1003 patients considered in this diagnostic/prognostic report completed 3-month assessments by January 31, 2020. Each patient received a baseline ED assessment along with follow-up self-report surveys 2 weeks, 8 weeks, and 3 months later. An ensemble machine learning method was used to predict 3-month PTSD or MDE from baseline information. Data analysis was performed from November 1, 2020, to May 31, 2021. Main Outcomes and Measures The PTSD Checklist forDSM-5was used to assess PTSD and the Patient Reported Outcomes Measurement Information System Depression Short-Form 8b to assess MDE. Results A total of 1003 patients (median [interquartile range] age, 34.5 [24-43] years; 715 [weighted 67.9%] female; 100 [weighted 10.7%] Hispanic, 537 [weighted 52.7%] non-Hispanic Black, 324 [weighted 32.2%] non-Hispanic White, and 42 [weighted 4.4%] of non-Hispanic other race or ethnicity were included in this study. A total of 274 patients (weighted 26.6%) met criteria for 3-month PTSD or MDE. An ensemble machine learning model restricted to 30 predictors estimated in a training sample (patients from the Northeast or Midwest) had good prediction accuracy (mean [SE] area under the curve [AUC], 0.815 [0.031]) and calibration (mean [SE] integrated calibration index, 0.040 [0.002]; mean [SE] expected calibration error, 0.039 [0.002]) in an independent test sample (patients from the South). Patients in the top 30% of predicted risk accounted for 65% of all 3-month PTSD or MDE, with a mean (SE) positive predictive value of 58.2% (6.4%) among these patients at high risk. The model had good consistency across regions of the country in terms of both AUC (mean [SE], 0.789 [0.025] using the Northeast as the test sample and 0.809 [0.023] using the Midwest as the test sample) and calibration (mean [SE] integrated calibration index, 0.048 [0.003] using the Northeast as the test sample and 0.024 [0.001] using the Midwest as the test sample; mean [SE] expected calibration error, 0.034 [0.003] using the Northeast as the test sample and 0.025 [0.001] using the Midwest as the test sample). The most important predictors in terms of Shapley Additive Explanations values were symptoms of anxiety sensitivity and depressive disposition, psychological distress in the 30 days before motor vehicle collision, and peritraumatic psychosomatic symptoms. Conclusions and Relevance The results of this study suggest that a short set of questions feasible to administer in an ED can predict 3-month PTSD or MDE with good AUC, calibration, and geographic consistency. Patients at high risk can be identified in the ED for targeting if cost-effective preventive interventions are developed.

The aim of this research is to examine the structural relationships between metacognitive thinking skills, critical thinking standards and academic self-efficacy of teacher candidates. The research was carried out according to the relational survey method and structural equation modelling was done in the analysis of the data. The data of the study were obtained from 244 teacher candidates. Personal information form, Critical Thinking Standards Scale, Metacognitive Thinking Scale and Academic Self-Efficacy Scale were used as data collection tools in the study. The findings of the research show that the teacher candidates have a positive moderate relationship between critical thinking standards and metacognitive thinking skills. There appears to be a positive low level of relationship between critical thinking standards and academic self-efficacy. There appears to be a positive moderate relationship between metacognitive thinking skills and academic self-efficacy. Various suggestions have been made to investigators and researchers in the findings obtained from the research.

The purpose of this study was to investigate the structural relationships between university students’ acceptance of a learning management system (LMS), sense of community and engagement in a blended learning (BL) environment. This study was conducted in an introductory computer course with students who participated in a Moodle learning community that was constituted for the BL course. Three self-report instruments were employed to gather data from 168 university students studying at a public university. The path analysis with structural equation modeling (SEM) confirmed that students’ LMS acceptance was related to their sense of community and engagement in BL. The findings revealed that students’ LMS acceptance significantly and positively affected their sense of community and engagement in BL. Moreover, perceived usefulness and perceived ease of use of LMS were uncovered as influential factors in increasing their sense of community and engagement. The findings of the study imply that instructors should ensure students’ acceptance of LMS to establish learning communities in which they have opportunities for collaborative, interactive and engaging learning activities in the delivery of BL.

Mobil uygulamaları daha hızlı, daha kolay ve daha ucuz geliştirebilmek için yeni yöntemler ve araçlar kullanılmaya başlanmıştır. Bu bağlamda, Çapraz Platform (Cross-Platform) ile ayrı ayrı uygulama geliştirmek yerine tek bir uygulamanın farklı işletim sistemlerine kolaylıkla uyarlanabilmesini sağlamaktadır. Xamarin köklü programlama dilleri arasında yer alan C# ile Çapraz Platform uygulamalar geliştirmemize imkân vermektedir. Ayrıca, günümüzde bulut teknolojileri, bilişim sektörüne yeni bir boyut kazandırmış, bu durum geliştiricilere kendi veri tabanı sistemlerini kurmak yerine bulut hizmetlerini veri tabanı olarak kullanmaya yönlendirmiştir. Firebase bulut veri tabanı yapısı, kullanıcılara gerçek zamanlı veri ve depolamayla iOS, Android, Windows ve hatta web tabanlı uygulamalar arasında iletişim ortamını sağlamaktadır. Özellikle Nesnelerin İnterneti (Internet of Things) uygulamalarının eşzamanlı olarak verimli bir şekilde çalışabilmesi için gerçek zamanlı veri haberleşmesi son derece hayatidir. Son yıllarda, Nesnelerin İnterneti yapılarının mobil uygulamalarla kontrolü ile farklı ölçek ve hedeflere sahip akıllı nesneleri birbirine bağlayan birçok çözüm üretilmiştir. Bu çalışmada, akıllı bir ev sistemini oluşturan farklı nesneler tek yapı haline getirilerek bütünleşik bir akıllı sistem oluşturulmuştur. Xamarin çapraz platform kullanarak tasarlanan mobil uygulama ile bulut veri tabanının gerçek zamanlı veri özelliğini kullanarak bütünleşik sisteminin kontrolünü; IOS, Android ve Windows platformlarda çalışabilecek ortak bir ara yüz tasarımı ve arka plan kod yapısı ile gerçekleştirmiştir., The use of new methods and tools have been begun in order to develop mobile applications faster, easier and cheaper. Cross-Platform makes an application possible to be easily adapted to different operating systems instead of developing applications separately. Xamarin enables us to build Cross-Platform applications with the rooted programming language of C#. In addition, today’s cloud technologies have brought a new dimension to the IT sector and this has directed developers to utilize cloud services as a database instead of implementing their own database systems. Firebase cloud database structure offering real-time data and storage provides users with a communication environment among iOS, Android, Windows and even web-based applications. Specifically, real-time data communication is necessary to simultaneously run the applications of the Internet of Things (IoT) in an efficient manner. In recent years, many solutions that connect smart objects having different scales and targets have been developed by controlling the Internet of Things structures with mobile applications. In this study, an integrated smart system was built by combining different objects that form a smart home system unified into a single structure. The control of the mobile application designed using Xamarin Cross-Platform and the integrated system using the real-time data feature of the cloud database has been accomplished with a joint interface design and background code structure that runs on IOS, Android and Windows operating systems.

Within higher education, the use of blended learning (BL) is exponentially increased in the 21st century,which poses a number of challenges in designing the process of BL for educators. The aim of this designbasedresearch (DBR) study was to assist an inexperienced educator in teaching and designing a BL coursein higher education to convert a face-to-face (F2F) course into a BL course. During the design and deliveryof the BL course, what appropriate practices were needed to achieve creating an efficient and effective BLcourse were determined and the educator’s reflections on the first experience of teaching the BL course weredocumented over three iterative design cycles. Mixed methods including learning environment observations,educator interviews, student surveys were employed. The results demonstrated that BL enabled the educatorto adopt active learning approaches, engage students in critical thinking and promote the quality ofinteractive and collaborative learning assignments. Although the educator indicated the time limitation asan obstacle to teach a more efficient BL course, the educator was contented with teaching and designingthe BL course and found it useful and supportive overall. Findings were presented and discussed for futurestudies and implications.

The aim of this research is to examine student acceptance and use of virtual reality technologies in medical education. Within the scope of the research, a questionnaire consisting of 4 sub-dimensions and 21 items was developed by the researchers. This questionnaire consists of sub-dimensions of performance expectancy, effort expectancy, facilitating conditions, and social influence. The study was conducted on 421 university students who participated in courses and activities related to the use of virtual reality applications in medical education. The findings of the research demonstrated that the students' acceptance and use of virtual reality applications were high in medical education. Various suggestions were made for researchers and educators in accordance with the findings.

Online learning is common in higher education, but has its drawbacks. As a result, blended learning (BL) has emerged as an alternative to alleviate the challenges of online learning. The purpose of this design-based research study was to determine what elements were needed to assist a higher education instructor inexperienced in designing and teaching a BL course to successfully create and implement it, and to document the instructor’s perceptions about the first experience of teaching a BL course. The BL course was designed, implemented and redesigned to make the BL course an effective and efficient learning environment through the three phases of this design-based research. Qualitative and quantitative research methods including instructor interviews, learning environment observations and student surveys were employed to collect data. Results indicated that iterative analysis, design and evaluation of the created BL course provided an opportunity for the researchers to find applicable solutions to any real-world problems that the instructor faced in the course. Besides, the design and implementation of BL led the instructor to shift from a passive teaching approach to an active teaching approach and allowed the students to become active and interactive learners through the process of three iterative design cycles. Although challenges were identified, she had an overall positive perception toward teaching the BL course.

The authors developed and implemented transparent machine-learning models that call into question the use of black-box machine-learning models in healthcare and criminal justice applications.

Background Although several short-forms of the posttraumatic stress disorder (PTSD) Checklist (PCL) exist, all were developed using heuristic methods. This report presents the results of analyses designed to create an optimal short-form PCL for DSM-5 (PCL-5) using both machine learning and conventional scale development methods. Methods The short-form scales were developed using independent datasets collected by the Army Study to Assess Risk and Resilience among Service members. We began by using a training dataset (n = 8,917) to fit short-form scales with between 1 and 8 items using different statistical methods (exploratory factor analysis, stepwise logistic regression, and a new machine learning method to find an optimal integer-scored short-form scale) to predict dichotomous PTSD diagnoses determined using the full PCL-5. A smaller subset of best short-form scales was then evaluated in an independent validation sample (n = 11,728) to select one optimal short-form scale based on multiple operating characteristics (area under curve [AUC], calibration, sensitivity, specificity, net benefit). Results Inspection of AUCs in the training sample and replication in the validation sample led to a focus on 4-item integer-scored short-form scales selected with stepwise regression. Brier scores in the validation sample showed that a number of these scales had comparable calibration (0.015-0.032) and AUC (0.984-0.994), but that one had consistently highest net benefit across a plausible range of decision thresholds. Conclusions The recommended 4-item integer-scored short-form PCL-5 generates diagnoses that closely parallel those of the full PCL-5, making it well-suited for screening.

We investigate a long-debated question, which is how to create predictive models of recidivism that are sufficiently accurate, transparent, and interpretable to use for decision-making. This question is complicated as these models are used to support different decisions, from sentencing, to determining release on probation, to allocating preventative social services. Each use case might have an objective other than classification accuracy, such as a desired true positive rate (TPR) or false positive rate (FPR). Each (TPR, FPR) pair is a point on the receiver operator characteristic (ROC) curve. We use popular machine learning methods to create models along the full ROC curve on a wide range of recidivism prediction problems. We show that many methods (SVM, Ridge Regression) produce equally accurate models along the full ROC curve. However, methods that designed for interpretability (CART, C5.0) cannot be tuned to produce models that are accurate and/or interpretable. To handle this shortcoming, we use a new method known as SLIM (Supersparse Linear Integer Models) to produce accurate, transparent, and interpretable models along the full ROC curve. These models can be used for decision-making for many different use cases, since they are just as accurate as the most powerful black-box machine learning models, but completely transparent, and highly interpretable., 45 pages, 17 figures

Machine learning models are increasingly used to automate decisions that affect humans - deciding who should receive a loan, a job interview, or a social service. In such applications, a person should have the ability to change the decision of a model. When a person is denied a loan by a credit score, for example, they should be able to alter its input variables in a way that guarantees approval. Otherwise, they will be denied the loan as long as the model is deployed. More importantly, they will lack the ability to influence a decision that affects their livelihood. In this paper, we frame these issues in terms of recourse, which we define as the ability of a person to change the decision of a model by altering actionable input variables (e.g., income vs. age or marital status). We present integer programming tools to ensure recourse in linear classification problems without interfering in model development. We demonstrate how our tools can inform stakeholders through experiments on credit scoring problems. Our results show that recourse can be significantly affected by standard practices in model development, and motivate the need to evaluate recourse in practice., Extended version. ACM Conference on Fairness, Accountability and Transparency [FAT2019]

In the context of machine learning, disparate impact refers to a form of systematic discrimination whereby the output distribution of a model depends on the value of a sensitive attribute (e.g., race or gender). In this paper, we propose an information-theoretic framework to analyze the disparate impact of a binary classification model. We view the model as a fixed channel, and quantify disparate impact as the divergence in output distributions over two groups. Our aim is to find a correction function that can perturb the input distributions of each group to align their output distributions. We present an optimization problem that can be solved to obtain a correction function that will make the output distributions statistically indistinguishable. We derive closed-form expressions to efficiently compute the correction function, and demonstrate the benefits of our framework on a recidivism prediction problem based on the ProPublica COMPAS dataset.