An Interactive Assessment Framework for Visual Engagement: Statistical Analysis of a TEDx Video

Autor/inn/en	Farhan, Muhammad; Aslam, Muhammad
Titel	An Interactive Assessment Framework for Visual Engagement: Statistical Analysis of a TEDx Video
Quelle	In: EURASIA Journal of Mathematics, Science & Technology Education, 13 (2017) 4, S.1107-1119 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1305-8223
Schlagwörter	Learner Engagement; Visual Perception; Attention; Video Technology; Lecture Method; Statistical Analysis; Eye Movements; Case Studies; Electronic Learning; Measurement; Educational Quality; Online Courses; Statistical Inference; Instructional Design; Teaching Methods + Suchen Sie Ihr Suchwort? Visuelle Wahrnehmung; Aufmerksamkeit; Statistische Analyse; Augenbewegung; Case study; Fallstudie; Case Study; Messverfahren; Quality of education; Bildungsqualität; Online course; Online-Kurs; Inferential statistics; Schließende Statistik; Lesson concept; Lessonplan; Unterrichtsentwurf; Teaching method; Lehrmethode; Unterrichtsmethode
Abstract	This study aims to assess the visual engagement of the video lectures. This analysis can be useful for the presenter and student to find out the overall visual attention of the videos. For this purpose, a new algorithm and data collection module are developed. Videos can be transformed into a dataset with the help of data collection module. The dataset is prepared by extracting the image frames from the video and marking them with a number of faces, the number of eyes, the status of eyes and the engagement score along with nominal values of engagement level. This data is transformed into time-based data items by using the attribute number of frames processed per second (PFPS). A case study for the assessment of TEDx video (length 8 minutes and 53 seconds) is included to validate the results and to extract statistical information from the dataset. Frames in the video are 16047 and they are transformed into 2675 keyframes. Machine learning classifiers are applied for the analysis of the dataset. The findings of this analysis help the presenter and the student to measure the quality of the visual content of the videos without actually going through it. (As Provided).
Anmerkungen	EURASIA. Sehit Osman Avci Mah. Malazgirt 1071 Cad. No:48/30, Etimesgut, Ankara, TR06796, Turkey. Tel: +90-312-202-8192; Fax: +90-312-222-8483; e-mail: editor.eurasiajournal@gmail.com; Web site: http://www.ejmste.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01