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13 results on '"Isotani, Seiji"'

1. Predicting Students' Flow Experience through Behavior Data in Gamified Educational Systems

Author

Oliveira, Wilk, Tenório, Kamilla, Hamari, Juho, Pastushenko, Olena, and Isotani, Seiji

Abstract

The flow experience (i.e., challenge-skill balance, action-awareness merging, clear goals, unambiguous feedback, concentration, sense of control, loss of self-consciousness, transformation of time, and "autotelic" experience) is an experience highly related to the learning experience. One of the current challenges is to identify whether students are managing to achieve this experience in educational systems. The methods currently used to identify students' flow experience are based on self-reports or equipment (e.g., eye trackers or electroencephalograms). The main problem with these methods is the high cost of the equipment and the impossibility of applying them massively. To address this challenge, we used behavior data logs produced by students during the use of a gamified educational system to predict the students' flow experience. Through a data-driven study (N = 23) using structural equation modeling, we identified possibilities to predict the students' flow experience through the speed of students' actions. With this initial study, we advance the literature, especially contributing to the field of student experience analysis, by bringing insights showing how to step towards automatic students' flow experience identification in gamified educational systems.

Published
2021
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2. Contextual Features for Automatic Essay Scoring in Portuguese

Author

Galhardi, Lucas, Herculano, Maria Fernanda, Rodrigues, Luiz, Miranda, Péricles, Oliveira, Hilário, Cordeiro, Thiago, Bittencourt, Ig Ibert, Isotani, Seiji, Mello, Rafael Ferreira, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Olney, Andrew M., editor, Chounta, Irene-Angelica, editor, Liu, Zitao, editor, Santos, Olga C., editor, and Bittencourt, Ig Ibert, editor

Published
2024
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3. Learning and Gamification Dashboards: A Mixed-Method Study with Teachers

Author

Tenório, Kamilla, Lemos, Bruno, Nascimento, Pedro, Santos, Rodrigo, Machado, Alexandre, Dermeval, Diego, Paiva, Ranilson, Isotani, Seiji, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cristea, Alexandra I., editor, and Troussas, Christos, editor

Published
2021
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4. Discovery of Study Patterns that Impacts Students’ Discussion Performance in Forum Assignments

Author

Penteado, Bruno Elias, Isotani, Seiji, Paiva, Paula Maria Pereira, Morettin-Zupelari, Marina, Ferrari, Deborah Viviane, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Isotani, Seiji, editor, Millán, Eva, editor, Ogan, Amy, editor, Hastings, Peter, editor, McLaren, Bruce, editor, and Luckin, Rose, editor

Published
2019
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5. Motivating Students to Ask More Questions

Author

Wang, Yuan, Bohlen, Turner, Elkins-Tanton, Linda, Tanton, James, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Isotani, Seiji, editor, Millán, Eva, editor, Ogan, Amy, editor, Hastings, Peter, editor, McLaren, Bruce, editor, and Luckin, Rose, editor

Published
2019
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6. Detection of Collaboration: Relationship Between Log and Speech-Based Classification

Author

Viswanathan, Sree Aurovindh, Vanlehn, Kurt, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Isotani, Seiji, editor, Millán, Eva, editor, Ogan, Amy, editor, Hastings, Peter, editor, McLaren, Bruce, editor, and Luckin, Rose, editor

Published
2019
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7. Orchestrating Class Discussion with Collaborative Kit-Build Concept Mapping

Author

Hayashi, Yusuke, Nomura, Toshihiro, Hirashima, Tsukasa, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Isotani, Seiji, editor, Millán, Eva, editor, Ogan, Amy, editor, Hastings, Peter, editor, McLaren, Bruce, editor, and Luckin, Rose, editor

Published
2019
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8. Early Dropout Prediction for Programming Courses Supported by Online Judges

Author

Pereira, Filipe D., Oliveira, Elaine, Cristea, Alexandra, Fernandes, David, Silva, Luciano, Aguiar, Gene, Alamri, Ahmed, Alshehri, Mohammad, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Isotani, Seiji, editor, Millán, Eva, editor, Ogan, Amy, editor, Hastings, Peter, editor, McLaren, Bruce, editor, and Luckin, Rose, editor

Published
2019
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9. Collaboration Detection that Preserves Privacy of Students’ Speech

Author

Viswanathan, Sree Aurovindh, VanLehn, Kurt, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Isotani, Seiji, editor, Millán, Eva, editor, Ogan, Amy, editor, Hastings, Peter, editor, McLaren, Bruce, editor, and Luckin, Rose, editor

Published
2019
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10. Analyzing Students’ Design Solutions in an NGSS-Aligned Earth Sciences Curriculum

Author

Zhang, Ningyu, Biswas, Gautam, Chiu, Jennifer L., McElhaney, Kevin W., Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Isotani, Seiji, editor, Millán, Eva, editor, Ogan, Amy, editor, Hastings, Peter, editor, McLaren, Bruce, editor, and Luckin, Rose, editor

Published
2019
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11. Evaluating Machine Learning Approaches to Classify Pharmacy Students’ Reflective Statements

Author

Liu, Ming, Shum, Simon Buckingham, Mantzourani, Efi, Lucas, Cherie, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Isotani, Seiji, editor, Millán, Eva, editor, Ogan, Amy, editor, Hastings, Peter, editor, McLaren, Bruce, editor, and Luckin, Rose, editor

Published
2019
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12. Evaluating machine learning approaches to classify pharmacy students’ reflective statements

Author

Efi Mantzourani, Ming Liu, Simon Buckingham Shum, Cherie Lucas, Isotani, Seiji, Millán, Eva, Ogan, Amy, Hastings, Peter, McLaren, Bruce, and Luckin, Rose

Subjects
business.industry, Computer science, 05 social sciences, Word count, Reflective writing, Learning analytics, 050301 education, Machine learning, computer.software_genre, 050105 experimental psychology, Binary classification, Critical thinking, Analytics, Academic writing, Rhetorical question, 0501 psychology and cognitive sciences, Artificial Intelligence & Image Processing, Artificial intelligence, business, 0503 education, computer
Abstract

© Springer Nature Switzerland AG 2019. Reflective writing is widely acknowledged to be one of the most effective learning activities for promoting students’ self-reflection and critical thinking. However, manually assessing and giving feedback on reflective writing is time consuming, and known to be challenging for educators. There is little work investigating the potential of automated analysis of reflective writing, and even less on machine learning approaches which offer potential advantages over rule-based approaches. This study reports progress in developing a machine learning approach for the binary classification of pharmacy students’ reflective statements about their work placements. Four common statistical classifiers were trained on a corpus of 301 statements, using emotional, cognitive and linguistic features from the Linguistic Inquiry and Word Count (LIWC) analysis, in combination with affective and rhetorical features from the Academic Writing Analytics (AWA) platform. The results showed that the Random-forest algorithm performed well (F-score = 0.799) and that AWA features, such as emotional and reflective rhetorical moves, improved performance.

Published
2019

13. Uso de um m??todo preditivo para inferir a zona de aprendizagem de alunos de programa????o em um ambiente de corre????o autom??tica de c??digo

Author

Pereira, Filipe Dwan, secretariappgi@icomp.ufam.edu.br, Oliveira, Elaine Harada Teixeira de, Oliveira, David Braga Fernandes de, Cristo, Marco Antonio Pinheiro de, and Isotani, Seiji

Subjects
Aaprendizagem de m??quina, Alunos de programa????o, CI??NCIA DA COMPUTA????O [CI??NCIAS EXATAS E DA TERRA], Machine learning, Ju??zes online, Data-driven - M??tricas de software, Learning analytics, Genetic programming, Programming students
Abstract

Submitted by Divis??o de Documenta????o/BC Biblioteca Central (ddbc@ufam.edu.br) on 2018-06-04T13:02:42Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Filipe Dwan.pdf: 3617202 bytes, checksum: 21261ba9c1db7a40af29004bd0bb6f52 (MD5) Approved for entry into archive by Divis??o de Documenta????o/BC Biblioteca Central (ddbc@ufam.edu.br) on 2018-06-04T13:02:58Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Filipe Dwan.pdf: 3617202 bytes, checksum: 21261ba9c1db7a40af29004bd0bb6f52 (MD5) Made available in DSpace on 2018-06-04T13:02:58Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Filipe Dwan.pdf: 3617202 bytes, checksum: 21261ba9c1db7a40af29004bd0bb6f52 (MD5) Previous issue date: 2018-03-29 CS1 (first year programming) classes are known to have a high dropout and non-pass rate. Thus, there have been many studies attempting to predict and alleviate CS1 student performance. Knowing about student performance in advance can be useful for many reasons. For example, teachers can apply specific actions to help learners who are struggling, as well as provide more challenging activities to high-achievers. Initial studies used static factors, such as: high school grades, age, gender. However, student behavior is dynamic and, as such, a data-driven approach has been gaining more attention, since many universities are using web-based environments to support CS1 classes. Thereby, many researchers have started extracting student behavior by cleaning data collected from these environments and using them as features in machine learning (ML) models. Recently, the research community has proposed many predictive methods available, even though many of these studies would need to be replicated, to check if they are context-sensitive. Thus, we have collected a set of successful features correlated with the student grade used in related studies, compiling the best ML attributes, as well as adding new features, and applying them on a database representing 486 CS1 students. The set of features was used in ML pipelines which were optimized with two approaches: hyperparameter-tuning with random search and genetic programming. As a result, we achieved an accuracy of 74.44%, using data from the first two weeks to predict student final grade, which outperforms a state-of-the-art research applied to the same dataset. It is also worth noting that from the eighth week of class, the method achieved accuracy between 85% and 90.62%. Em m??dia, um ter??o dos alunos no mundo reprova em disciplinas de introdu????o ?? programa????o de computadores (IPC). Assim, muitos estudos v??m sendo conduzidos a fim de inferir o desempenho de estudantes de turmas de IPC. Inicialmente, pesquisadores investigavam a rela????o das notas dos alunos com fatores est??ticos como: notas no ensino m??dio, g??nero, idade e outros. Entretanto, o comportamento dos estudantes ?? din??mico e, dessa forma, abordagens orientadas aos dados v??m ganhando aten????o, uma vez que muitas universidades utilizam ambientes web para turmas de programa????o como ju??zes online. Com efeito, muitos pesquisadores v??m extraindo e tratando os dados dos estudantes a partir desses ambientes e usando-os como atributos de algoritmos de aprendizagem de m??quina para a constru????o de modelos preditivos. No entanto, a comunidade cient??fica sugere que tais estudos sejam reproduzidos a fim de investigar se eles s??o generaliz??veis a outras bases de dados educacionais. Neste sentido, neste trabalho apresentou-se um m??todo que emprega um conjunto de atributos correlacionados com as notas dos estudantes, sendo alguns baseados em trabalhos relacionados e outros propostos nesta pesquisa, a fim de realizar a predi????o do desempenho dos alunos nas avalia????es intermedi??rias e nas m??dias finais. Tal m??todo foi aplicado a uma base de dados com 486 alunos de IPC. O conjunto de atributos chamado de perfil de programa????o foi empregado em algoritmos de aprendizagem de m??quina e otimizado utilizando duas abordagens: a) ajuste de hiperpar??metros com random search e b) constru????o do pipeline de aprendizagem de m??quina utilizando algoritmos evolutivos. Como resultado, atingiu-se 74,44% de acur??cia na tarefa de identificar se os alunos iriam ser reprovados ou aprovados usando os dados das duas semanas de aula em uma base de dados balanceada. Esse resultado foi estatisticamente superior ao baseline. Destaca-se ainda que a partir da oitava semana de aula, o m??todo atingiu acur??cias entre 85% e 90,62%.

Published
2018

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