Motivating Study Before Classes on Flipped Learning

Bibliographic Details
Main Author: Descalço, L.
Publication Date: 2018
Other Authors: Carvalho, P., Oliveira, P.
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10773/23905
Summary: For the flipped learning environment to be effective, it is essential that students use the learning materials before the classes, where they are supposed to practice, collaborating with colleagues with the help and guidance of the teacher. Students can easily get lost and unmotivated if the teacher simply gives them the learning material for studying before the classes. There is a need for some level of guidance and orientation in this autonomous learning for many students or they will go to the classes without having done the previous work required. We have developed a computer system with the main goal of motivating and guiding students to do this work before the classes. The computer system implements a Bayesian student model together with a learning trajectories model and makes use of a parameterized question generator, without any limitations on the parameterization we have also helped to develop. In practice, the computer system for learning presents the materials in an appealing and appropriate way, individually according to each student progress, including videos and other materials. Students can answer some of the many existing questions, generated among many parameterized templates, see detailed solutions of questions, also generated, and finally obtain a feedback about their progress in the learning contents required to next class. In the first semester of academic year 2017/2018, we have used our computer system with students in some of the topics of a course about calculus with several variables. The system keeps track of all activity and allows us to investigate student behaviour during the semester. We present an analysis of the system usage data on the topics in the experience comparing to those on traditional teaching, and relate with the results in final assessment, with the main goal of investigating the effectiveness of the use of this kind of computer systems in a flipped learning environment for motivating and guiding autonomous work before the classes.
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spelling Motivating Study Before Classes on Flipped LearningMotivationCalculusFlipped learningFor the flipped learning environment to be effective, it is essential that students use the learning materials before the classes, where they are supposed to practice, collaborating with colleagues with the help and guidance of the teacher. Students can easily get lost and unmotivated if the teacher simply gives them the learning material for studying before the classes. There is a need for some level of guidance and orientation in this autonomous learning for many students or they will go to the classes without having done the previous work required. We have developed a computer system with the main goal of motivating and guiding students to do this work before the classes. The computer system implements a Bayesian student model together with a learning trajectories model and makes use of a parameterized question generator, without any limitations on the parameterization we have also helped to develop. In practice, the computer system for learning presents the materials in an appealing and appropriate way, individually according to each student progress, including videos and other materials. Students can answer some of the many existing questions, generated among many parameterized templates, see detailed solutions of questions, also generated, and finally obtain a feedback about their progress in the learning contents required to next class. In the first semester of academic year 2017/2018, we have used our computer system with students in some of the topics of a course about calculus with several variables. The system keeps track of all activity and allows us to investigate student behaviour during the semester. We present an analysis of the system usage data on the topics in the experience comparing to those on traditional teaching, and relate with the results in final assessment, with the main goal of investigating the effectiveness of the use of this kind of computer systems in a flipped learning environment for motivating and guiding autonomous work before the classes.IATED2018-08-01T16:26:43Z2018-07-01T00:00:00Z2018-07conference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10773/23905eng978-84-09-02709-5Descalço, L.Carvalho, P.Oliveira, P.info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-06T04:16:43Zoai:ria.ua.pt:10773/23905Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T14:02:58.764297Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Motivating Study Before Classes on Flipped Learning
title Motivating Study Before Classes on Flipped Learning
spellingShingle Motivating Study Before Classes on Flipped Learning
Descalço, L.
Motivation
Calculus
Flipped learning
title_short Motivating Study Before Classes on Flipped Learning
title_full Motivating Study Before Classes on Flipped Learning
title_fullStr Motivating Study Before Classes on Flipped Learning
title_full_unstemmed Motivating Study Before Classes on Flipped Learning
title_sort Motivating Study Before Classes on Flipped Learning
author Descalço, L.
author_facet Descalço, L.
Carvalho, P.
Oliveira, P.
author_role author
author2 Carvalho, P.
Oliveira, P.
author2_role author
author
dc.contributor.author.fl_str_mv Descalço, L.
Carvalho, P.
Oliveira, P.
dc.subject.por.fl_str_mv Motivation
Calculus
Flipped learning
topic Motivation
Calculus
Flipped learning
description For the flipped learning environment to be effective, it is essential that students use the learning materials before the classes, where they are supposed to practice, collaborating with colleagues with the help and guidance of the teacher. Students can easily get lost and unmotivated if the teacher simply gives them the learning material for studying before the classes. There is a need for some level of guidance and orientation in this autonomous learning for many students or they will go to the classes without having done the previous work required. We have developed a computer system with the main goal of motivating and guiding students to do this work before the classes. The computer system implements a Bayesian student model together with a learning trajectories model and makes use of a parameterized question generator, without any limitations on the parameterization we have also helped to develop. In practice, the computer system for learning presents the materials in an appealing and appropriate way, individually according to each student progress, including videos and other materials. Students can answer some of the many existing questions, generated among many parameterized templates, see detailed solutions of questions, also generated, and finally obtain a feedback about their progress in the learning contents required to next class. In the first semester of academic year 2017/2018, we have used our computer system with students in some of the topics of a course about calculus with several variables. The system keeps track of all activity and allows us to investigate student behaviour during the semester. We present an analysis of the system usage data on the topics in the experience comparing to those on traditional teaching, and relate with the results in final assessment, with the main goal of investigating the effectiveness of the use of this kind of computer systems in a flipped learning environment for motivating and guiding autonomous work before the classes.
publishDate 2018
dc.date.none.fl_str_mv 2018-08-01T16:26:43Z
2018-07-01T00:00:00Z
2018-07
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dc.relation.none.fl_str_mv 978-84-09-02709-5
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