Image Reconstruction of Tablet Front Camera Recordings in Educational Settings [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).

Autor/inn/en	Wampfler, Rafael; Emch, Andreas; Solenthaler, Barbara; Gross, Markus
Titel	Image Reconstruction of Tablet Front Camera Recordings in Educational Settings [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).
Quelle	(2020), (12 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Photography; Handheld Devices; Student Behavior; Affective Behavior; Attention; Human Body; Learning Processes; Eye Movements; Mathematics Instruction; Problem Solving; Task Analysis; Emotional Response; Prediction; Classification; Undergraduate Students; Recognition (Psychology) + Suchen Sie Ihr Suchwort? Fotografie; Student behaviour; Schülerverhalten; Affective disturbance; Active behaviour; Affektive Störung; Aufmerksamkeit; Menschlicher Körper; Learning process; Lernprozess; Augenbewegung; Mathematics lessons; Mathematikunterricht; Problemlösen; Aufgabenanalyse; Emotionales Verhalten; Vorhersage; Classification system; Klassifikation; Klassifikationssystem; Recognition; Wiedererkennen
Abstract	Front camera data from tablets used in educational settings offer valuable clues to student behavior, attention, and affective state. Due to the camera's angle of view, the face of the student is partially occluded and skewed. This hinders the ability of experts to adequately capture the learning process and student states. In this paper, we present a pipeline and techniques for image reconstruction of front camera recordings. Our setting consists of a cheap and unobtrusive mirror construction to improve the visibility of the face. We then process the image and use neural inpainting to reconstruct missing data in the recordings. We demonstrate the applicability of our setting and processing pipeline on affective state prediction based on front camera recordings (i.e., action units, eye gaze, eye blinks, and movement) during math-solving tasks (active) and emotional stimuli from pictures (passive) shown on a tablet. We show that our setup provides comparable performance for affective state prediction to recordings taken with an external and more obtrusive GoPro camera. [For the full proceedings, see ED607784.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01