Evaluating Classroom Interaction with the iPad®: An Updated Stalling's Tool
Notice bibliographique
Résumé
INTRODUCTION: ASSESSING CLASSROOM INTERACTIONSClassroom interactions have been studied at length from the perspective of both teacher-student interaction and student-student interactions (Cazden & Beck 2003, Fairclough 2013.) Most recently the complex role of technology in mediating learning between teacher and student has also been articulated (Mishra & Koehler 2006, Rosenberg & Koehler, 2015) in the TPACK model. As teachers are encouraged to engage action research in their classrooms as reflective practitioners (Robinson & Lai 2005) quality mixed methodologies for classroom observations have become increasingly important.The notion of student-centred learning has been promoted for some time through the works of Dewey (1938), Piaget (1977) and Vygotsky (1989). Nonetheless, the ideal of constructivist classrooms (Brooks & Brooks 1999) continues to be hampered by the pressures of standardized assessment (Popham 2001; Ravitch 2011). Widespread assessment trends have been shown (PISA, 2014) to support passive versus active learning. Recognizing the danger of departure from authentic, situated cognition in schools, some Caribbean and Latin American countries have undertaken studies that assess the level of active learning (Vegas, & Petrow 2008). The following research study sought to measure active learning in Barbadian public school classrooms using a valid instrument.In undertaking the research described herein, a variety of observation tools were considered and discounted for reasons of 1) lengthy and cumbersome recording formats, 2) specialized software required, 3) specialized populations observed and 4) extensive observer training or certification.The tools considered included: The Framework for Teaching Evaluation Instrument created by Charlotte Danielson (2011) utilized by the Bill and Melinda Gates Foundation as one of the instruments in their Measures of Effective Teaching (MET) project, Pianta, La Paro & Hamre's (2008) Classroom Assessment Scoring System (CLASS) system which requires proprietary software, a lengthy observation guide that accompanies VanTasselBaska, Avery, Struck, Feng, Bracken, Drummond, & Stambaugh's, (2003) William and Mary Classroom Observation Scales and a range of population-specific instruments (Cassady, Speirs Neumeister, Adams, Cross, Dixon, & Pierce,2004; Sawada, Turley, Falconer, Benford, & Bloom, 2002; Weiss, Pasley, Smith, Banilower, & Heck, 2003). A simple tool with a manageable learning curve was chosen as best suited for the observation of Barbadian classrooms, the description of which follows.As early as the mid 1970s, an instrument was designed (Stallings & Kaskowitz 1974; Stallings & Giesin 1977; Stallings 1980) to give a valid measure of active instruction in the classroom. The Stallings Instrument represents a sophisticate three dimensional matrix involving (1) teacher approach, (2) teaching materials used and (3) the size of the teaching and learning groups (i.e. T=teacher, l=student; number of persons 1=single, S=small group, L=large group & E=everyone). This coding instrument was intended to be used in multiple snapshots during a classroom period so as to further differentiate the interaction activity as a function of the class time continuum. For each of 10 snapshots one paper sheet was used to code the teacher and the student activity. The instrument was modified by the World Bank in 2007 to assist in their studies of classrooms in South and Latin America (see: www.eddataglobal.org/embedded/stallings_snapshot.doc). More recently, Bando and Li (2014) have accessed the Stallings tool for a study of teacher training in the context of teaching English as a second language. The grid for scoring classroom interactions is shown in Figure 1. Developers supplemented this instrument with a systematic description of the definitions that scorers would use for assigning appropriate codes. This inherently improved the inter-rater reliability of the instrument. …
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».