Exploring Semi-Supervised Learning for Audio-Based Automated Classroom Observations [Konferenzbericht] Paper presented at the International Conference on Cognition and Exploratory Learning in Digital Age (CELDA) (19th, 2022).

Autor/inn/en	Chanchal, Akchunya; Zualkernan, Imran
Institution	International Association for Development of the Information Society (IADIS)
Titel	Exploring Semi-Supervised Learning for Audio-Based Automated Classroom Observations [Konferenzbericht] Paper presented at the International Conference on Cognition and Exploratory Learning in Digital Age (CELDA) (19th, 2022).
Quelle	(2022), (10 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Audio Equipment; Classroom Observation Techniques; Cost Effectiveness; Educational Quality; Feedback (Response); Supervision; Learning Processes; Algorithms; Computer Software; Scores; Teacher Effectiveness; Teacher Evaluation; Classroom Environment; Classroom Assessment Scoring System + Suchen Sie Ihr Suchwort? Audio-CD; Kosten-Nutzen-Analyse; Kosten-Nutzen-Denken; Quality of education; Bildungsqualität; Learning process; Lernprozess; Algorithm; Algorithmus; Effectiveness of teaching; Instructional effectiveness; Lehrerleistung; Unterrichtserfolg; Teacher appraisal; Lehrerbeurteilung; Klassenklima; Unterrichtsklima
Abstract	Systematic classroom observation is often used in evaluating and enhancing the quality of classroom instruction. However, classroom observation can potentially suffer from human bias. In addition, the traditional classroom observation is too expensive for resource-constrained environments (e.g., Sub-Saharan Africa, South and Central Asia). A cost-effective automation of classroom observation could potentially enhance both quality and resolution of feedback to the teacher, and hence potentially result in enhancing quality of instruction. Audio-based automatic classroom observation using supervised deep learning techniques has yielded good results in limited contexts. However, one challenge when using supervised techniques is the high cost of collecting and labelling the classroom audio data. One solution for such data-starved scenarios is to use semi-supervised learning (SSL) which requires significantly lesser data and labels. This paper explores an audio-adaptation of the state-of-the-art SSL FixMatch algorithm to automate classroom observation. An adaptation of the FixMatch algorithm was proposed to automate the coding for the Stallings class observation system. The proposed system was trained on classroom audio data collected in the wild. The supervised approach had an F1-score of 0.83 on 100% labeled data. The proposed FixMatch adaptation achieved an impressive F1-score of 0.81 on 20% labeled data, 0.79 on 15% labeled data, 0.76 on 10% labeled data, and 0.72 using only 5% of labeled data. This suggests that algorithms like FixMatch that use consistency regularization and pseudo-labeling have a great potential for being used to automate classroom observation using a small labelled set of audio snippets. (As Provided).
Anmerkungen	International Association for the Development of the Information Society. e-mail: secretariat@iadis.org; Web site: http://www.iadisportal.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01