In this paper, we present a technique to automatically synthesize dancing motions for arbitrary songs with dance beats. Our technique is based on analysing a musical tune (can be a song or melody) and synthesizing a motion for the virtual character where the character's movement synchronizes to the musical beats. In order to analyse beats of the tune, we developed a fast algorithm. Our motion synthesis algorithm analyses library of stock motions and generates new sequences of movements that were not described in the library. We show that our motion synthesis algorithm is better than previous dance generation techniques. We also present two algorithms to synchronize dance moves and musical beats: a fast greedy algorithm, and a genetic algorithm. Our experimental results show that we can generate new sequences of dance figures in which the dancer reacts to music and dances in synchronization with the music.
Öz. Çevrimiçi öğrenme ortamları, öğrencilerin içeriklerle ve forum, viki vb. etkinliklerle ilgili her türlü etkileşimine ilişkin (bakma, silme, ekleme, güncelleme vb.) bilgileri veri tabanlarında kayıt etmektedir. Bu veriler, öğrenme süreçlerinin daha iyi anlaşılması ve eğitsel problemlerin çözümü konusunda öğrenme analitiği ve eğitsel veri madenciliği araştırmacılarının başvurduğu önemli veri kaynaklarıdır. Çevrimiçi öğrenme ortamlarında video tabanlı öğrenme materyallerinin kullanımının artması ile birlikte bu etkileşimlerin önemli bir bölümü videolar üzerinde gerçekleşmeye başlamıştır. Ancak, mevcut öğrenme yönetim sistemleri video izleme davranışlarının kayıt edilmesine ve analiz edilmesine olanak sağlamamaktadır ya da sınırlı analizler sunmaktadır. Yapılan çalışmalar ise bu verilerin analizi ile öğrencilerin video izleme davranışlarının anlaşılması ve video tabanlı ders materyallerinin geliştirilmesi konusunda önemli bilgilerin elde edilebileceğini göstermektedir. Bu çalışmada, araştırmacılar tarafından, öğrencilerin video etkileşimlerini kaydetmeye olanak sağlayacak bir video oynatıcı geliştirilmesi amaçlanmıştır. Çalışma kapsamında, geliştirilen video oynatıcının teknik özelliklerine, araç sayesinde elde edilen etkileşim verilerine ve aracın uygulaması sonucu elde edilen verilerin analizine ilişkin bilgilere yer verilmiştir. Anahtar Sözcükler: Video tabanlı öğrenme, öğrenme analitikleri, video analitik, çevrimiçi öğrenme, MoodleAbstract. Online learning environments, record all kinds of information including interactions of students with content and various activities related to forum, wiki, etc. (viewing, deleting, adding, updating, etc.) in databases. This data is an important source for research analysts and educational data mining researchers for better understanding of learning processes and solving educational problems. Along with the increasing use of video-based learning materials in online learning environments, significant amount of these interactions have begun to take place on videos, however, existing learning management systems do not allow or allow limited analysis of video viewing behaviors. On the other hand, analyzing these data will provide important information to the researchers in understanding the video viewing behavior of their students and in using video-based course material more effectively. The aim of this study is to develop a video player that allows researchers to record video interactions of their students. The technical specifications of the developed video player, the interaction data obtained by the tool and the analysis of the data obtained after the application of the Video Analytics Tool explained in detail.
The deep and surface learning approaches are closely related to the students' interaction with learning content and learning outcomes. While students with a surface approach have a tendency to acquire knowledge without questioning and to try to pass courses with minimum effort, students with a deep learning approach tend to use more skills such as problem-solving, questioning, and research. Studies show that learning approaches of students can change depending on subject, task and time. Therefore, it is important to identify students with a surface learning approach in online learning environments and to plan interventions that encourage them to use deep learning approaches. In this study, video viewing behaviors of students with deep and surface learning approaches are analyzed using video analytics. Video viewing patterns of students with different learning approaches are also compared. For this purpose, students (N=31) are asked to study a 10minutes-long video material related to Computer Hardware course. Video interactions in this process were also recorded using video player developed by the authors. At the end of the lab session, students were asked to fill in the Learning Approach Scale by taking into account their learning approaches to the course. As a result of the study, it was observed that the students with surface approach made a statistically significant forward seek over to the students used deep learning approach while watching the video. Moreover, an investigation on the time series graphs of two groups revealed that surface learners watched the video more linearly and had fewer interactions with it. These interaction data can be modeled with machine learning techniques to predict students with surface approach and can be used to identify design problems in video materials.
Background The performance of a clinical task depends on an individual’s skills, knowledge, and beliefs. However, there is no reliable and valid tool for measuring self-efficacy beliefs toward clinical skills in the Turkish language. This research work aims to study the linguistic equivalence, validity, and reliability of a Self-Efficacy Scale for Clinical Skills (L-SES). Materials and methods After reaching the original item pool of the scale, applying both forward and backward translation processes, and collecting responses of 11 experts from health professional sciences and educational sciences, the translation and adoption processes were completed. We randomly divided 651 medical students’ responses to a 15-item questionnaire into two datasets and conducted exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) analyses. Results CFA validated the three-factor model, and the model fit indexes were found to have acceptable values. The item factor loads ranged from .34 to .84, and items in the scale explained 47% of the total variance. Cronbach’s alpha (.91), Spearman-Brown (.88), and Guttman Split-Half (.88) coefficients obtained within the scope of internal consistency reliability demonstrated that the scale had the desired internal consistency. Conclusion The Turkish version of the short and universal learning self-efficacy scale for clinical skills questionnaire is a valid and reliable scale for measuring medical students’ self-efficacy for clinical skills. Adopted questionnaires may have different factor structures when applied to two different cultures. We also discussed this issue as a hidden pattern in our study.
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