Making Teaching and Learning Effective Using Analytics

Fuente: ERIC Institute of Education Sciences

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Bibliographic Details
Main Authors:	Besbes, Seifeddine, Twala, Bhekisipho, Besbes, Riadh
Format:	Recurso educativo Open Access
Language:	en
Published:	2022
Subjects:	Instructional Effectiveness Learning Analytics Adjustment (to Environment) Classroom Environment Predictor Variables Teacher Behavior Teacher Characteristics Teacher Effectiveness Observation Artificial Intelligence
Online Access:	Acceder al recurso
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author	Besbes, Seifeddine Twala, Bhekisipho Besbes, Riadh
author_facet	Besbes, Seifeddine Twala, Bhekisipho Besbes, Riadh Besbes, Seifeddine Twala, Bhekisipho Besbes, Riadh
collection	Education Resources Information Center
contents	Making Teaching and Learning Effective Using Analytics Besbes, Seifeddine Twala, Bhekisipho Besbes, Riadh Instructional Effectiveness Learning Analytics Adjustment (to Environment) Classroom Environment Predictor Variables Teacher Behavior Teacher Characteristics Teacher Effectiveness Observation Artificial Intelligence In this paper, an empirical comparison of three state-of-the-art classifier methods (artificial immune recognition systems, Lazy-K Star, and random tree) to predict teachers' ability to adapt in a classroom environment is carried out. Two educational databases are used for this task. First, measures collected in an academic context, especially from classroom visits, are used. Then, the three classifiers quantify the acts, behaviors, and characteristics of teaching effectiveness and the teacher's "ability to adapt in the classrooms." Professional classrooms visits to more than 200 teachers are used as the second database. An interactive grid gathering 63 educational acts and behaviors is conceived as an observation instrument for those visits. Within the Waikato Environment for Knowledge Analysis library environment, and with the progressive enhancement of the raw database, the utilization of state-of-the-art classification methods when predicting teaching effectiveness shows promising results, especially when data quality issues are considered.
format	Recurso educativo Open Access
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institution	ERIC Institute of Education Sciences
language	en
publishDate	2022
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spellingShingle	Making Teaching and Learning Effective Using Analytics Besbes, Seifeddine Twala, Bhekisipho Besbes, Riadh Instructional Effectiveness Learning Analytics Adjustment (to Environment) Classroom Environment Predictor Variables Teacher Behavior Teacher Characteristics Teacher Effectiveness Observation Artificial Intelligence Making Teaching and Learning Effective Using Analytics Besbes, Seifeddine Twala, Bhekisipho Besbes, Riadh Instructional Effectiveness Learning Analytics Adjustment (to Environment) Classroom Environment Predictor Variables Teacher Behavior Teacher Characteristics Teacher Effectiveness Observation Artificial Intelligence In this paper, an empirical comparison of three state-of-the-art classifier methods (artificial immune recognition systems, Lazy-K Star, and random tree) to predict teachers' ability to adapt in a classroom environment is carried out. Two educational databases are used for this task. First, measures collected in an academic context, especially from classroom visits, are used. Then, the three classifiers quantify the acts, behaviors, and characteristics of teaching effectiveness and the teacher's "ability to adapt in the classrooms." Professional classrooms visits to more than 200 teachers are used as the second database. An interactive grid gathering 63 educational acts and behaviors is conceived as an observation instrument for those visits. Within the Waikato Environment for Knowledge Analysis library environment, and with the progressive enhancement of the raw database, the utilization of state-of-the-art classification methods when predicting teaching effectiveness shows promising results, especially when data quality issues are considered.
title	Making Teaching and Learning Effective Using Analytics
topic	Instructional Effectiveness Learning Analytics Adjustment (to Environment) Classroom Environment Predictor Variables Teacher Behavior Teacher Characteristics Teacher Effectiveness Observation Artificial Intelligence
url	https://eric.ed.gov/?id=EJ1323862

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