A Comparison of Video-Based and Interaction-Based Affect Detectors in Physics Playground [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Kai, Shiming; Paquette, Luc; Baker, Ryan S.; Bosch, Nigel; D'Mello, Sidney; Ocumpaugh, Jaclyn; Shute, Valerie; Ventura, Matthew
Institution	International Educational Data Mining Society
Titel	A Comparison of Video-Based and Interaction-Based Affect Detectors in Physics Playground [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Video Technology; Interaction; Physics; Affective Behavior; Educational Technology; Technology Uses in Education; Science Instruction; Nonverbal Communication; Student Behavior; Games; Measurement Techniques; Prediction; Grade 8; Grade 9; Pretests Posttests; Observation; Coding; Scientific Principles; Concept Formation; Scientific Concepts; Statistical Analysis + Suchen Sie Ihr Suchwort? Interaktion; Physik; Affective disturbance; Active behaviour; Affektive Störung; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Non-verbal communication; Nonverbale Kommunikation; Student behaviour; Schülerverhalten; Game; Spiel; Spiele; Messtechnik; Vorhersage; School year 08; 8. Schuljahr; Schuljahr 08; School year 09; 9. Schuljahr; Schuljahr 09; Beobachtung; Codierung; Programmierung; Concept learning; Begriffsbildung; Statistische Analyse
Abstract	Increased attention to the relationships between affect and learning has led to the development of machine-learned models that are able to identify students' affective states in computerized learning environments. Data for these affect detectors have been collected from multiple modalities including physical sensors, dialogue logs, and logs of students' interactions with the learning environment. While researchers have successfully developed detectors based on each of these sources, little work has been done to compare the performance of these detectors. In this paper, we address this issue by comparing interaction-based and video-based affect detectors for a physics game called Physics Playground. Specifically, we report on the development and detection accuracy of two suites of affect and behavioral detectors. The first suite of detectors applies facial expression recognition to video data collected with webcams, while the second focuses on students' interactions with the game as recorded in log-files. Ground-truth affect and behavior annotations for both face- and interaction-based detectors were obtained via live field observations during game-play. We first compare the performance of these detectors in predicting students' affective states and off-task behaviors, and then proceed to outline the strengths and weakness of each approach. [For complete proceedings, see ED560503.] (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	2020/1/01