student profile: Mr Augusto Dias Pereira Dos Santos


Map

Thesis work

Thesis title: Smart Technology for Supporting Dance Education

Supervisors: Lian LOKE , Kalina YACEF , Judy KAY , Roberto MARTINEZ MALDONADO

Thesis abstract:

Social dance is an activity that provides mental and physical benefits for people. Although, acquiring rhythmic skills in dance classes requires, from the student, instruction, practice, feedback and much repetition of the previous steps. Rhythm is a sophisticated ability that involves the development of cognitive and motor skills aligned with constant auditory stimuli. Teachers have a short time during the classes to provide feedback to students and students have little to no access to feedback outside the classroom. Recent improvement and lower price of wearable technology offer new opportunities for creating tools to support dance teachers and students.

This thesis proposes and investigates a technological solution that uses wearables devices and machine learning to extracts information from students' movement performance, validates the accuracy of the solution and evaluate the benefits of such information for teachers and students. This thesis uses as a case study a Brazilian partner dance style called Forró. The research questions are generated around two aspects 1) the technology required to support dance learning and its accuracy, and 2) how dance teachers develop rhythm skills in students and how the technology proposed supports this development.

The research questions were explored using a mixed-methods approach. At both ends of the thesis, user-centred research was used to understand the context of dance learning and to evaluate the implications of using the proposed solution. In the middle of the thesis, an exploratory design guided the development of the algorithms, and statistical methods evaluated its accuracy when compared to human annotators. Four studies compose this thesis, involving dance teachers and volunteers participants as dance students.

The principal contributions of this thesis are the following:

  • Improves the understanding of how partner dance teachers approach to rhythm teaching.
  • RiMoDe v1 and v2, the algorithms that extract features from motion sensors data;
  • Modelling four performance metrics related to rhythm skills, named: Rhythm, Pause, Step Size and Weight Transfer, with an average accuracy of 80\%.
  • Explores the first attempts on providing automatically generated movement performance metrics to support dance teachers and students.

While the studies presented in this thesis were explored in the context of a specific dance style and dance movements, the findings are likely to be relevant to other dance styles and other dance movements as well as other motor skills related to rhythm.

Selected publications

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Conferences

  • Martinez-Maldonado, R., Echeverria, V., Santos, O., Dias Pereira dos Santos, A., Yacef, K. (2018). Physical learning analytics: A multimodal perspective. 8th International Learning Analytics & Knowledge Conference (LAK 2018), New York: Association for Computing Machinery (ACM). [More Information]
  • Dias Pereira dos Santos, A., Tang, L., Loke, L., Martinez-Maldonado, R. (2018). You Are Off The Beat!: Is Accelerometer Data Enough for Measuring Dance Rhythm? 5th International Conference on Movement Computing (MOCO '18), New York: Association for Computing Machinery (ACM). [More Information]
  • Dias Pereira dos Santos, A., Yacef, K., Martinez-Maldonado, R. (2017). Forro Trainer: Automated Feedback for Partner Dance Learning. 25th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2017), New York: Association for Computing Machinery (ACM). [More Information]
  • Martinez Maldonado, R., Echeverria, V., Yacef, K., Dias Pereira dos Santos, A., Pechenizkiy, M. (2017). How to capitalise on mobility, proximity and motion analytics to support formal and informal education? Joint 6th Multimodal Learning Analytics Workshop and the Second Cross-LAK Workshop, MMLA-CrossLAK 2017. CEUR-WS.
  • Dias Pereira dos Santos, A., Yacef, K., Martinez-Maldonado, R. (2017). Let's Dance: How to build a user model for dance students using wearable technology. 25th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2017), New York: Association for Computing Machinery (ACM). [More Information]
  • Dias Pereira dos Santos, A. (2017). Smart technology for supporting dance education. 25th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2017), New York: Association for Computing Machinery (ACM). [More Information]
  • Martinez-Maldonado, R., Yacef, K., Dias Pereira dos Santos, A., Buckingham-Shum, S., Echeverria, V., Santos, O., Pechenizkiy, M. (2017). Towards Proximity Tracking and Sensemaking for Supporting Teamwork and Learning. 17th IEEE International Conference on Advanced Learning Technologies (ICALT 2017), Piscataway, NJ: Institute of Electrical and Electronics Engineers Inc. [More Information]
  • Dias Pereira dos Santos, A., Yacef, K., Martinez Maldonado, R. (2016). Visualizing Individual Profiles and Grouping Conditions in Collaborative Learning Activities. 33rd International Conference of Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education (ASCILITE 2016), Adelaide: University Of South Australia.

2018

  • Martinez-Maldonado, R., Echeverria, V., Santos, O., Dias Pereira dos Santos, A., Yacef, K. (2018). Physical learning analytics: A multimodal perspective. 8th International Learning Analytics & Knowledge Conference (LAK 2018), New York: Association for Computing Machinery (ACM). [More Information]
  • Dias Pereira dos Santos, A., Tang, L., Loke, L., Martinez-Maldonado, R. (2018). You Are Off The Beat!: Is Accelerometer Data Enough for Measuring Dance Rhythm? 5th International Conference on Movement Computing (MOCO '18), New York: Association for Computing Machinery (ACM). [More Information]

2017

  • Dias Pereira dos Santos, A., Yacef, K., Martinez-Maldonado, R. (2017). Forro Trainer: Automated Feedback for Partner Dance Learning. 25th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2017), New York: Association for Computing Machinery (ACM). [More Information]
  • Martinez Maldonado, R., Echeverria, V., Yacef, K., Dias Pereira dos Santos, A., Pechenizkiy, M. (2017). How to capitalise on mobility, proximity and motion analytics to support formal and informal education? Joint 6th Multimodal Learning Analytics Workshop and the Second Cross-LAK Workshop, MMLA-CrossLAK 2017. CEUR-WS.
  • Dias Pereira dos Santos, A., Yacef, K., Martinez-Maldonado, R. (2017). Let's Dance: How to build a user model for dance students using wearable technology. 25th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2017), New York: Association for Computing Machinery (ACM). [More Information]
  • Dias Pereira dos Santos, A. (2017). Smart technology for supporting dance education. 25th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2017), New York: Association for Computing Machinery (ACM). [More Information]
  • Martinez-Maldonado, R., Yacef, K., Dias Pereira dos Santos, A., Buckingham-Shum, S., Echeverria, V., Santos, O., Pechenizkiy, M. (2017). Towards Proximity Tracking and Sensemaking for Supporting Teamwork and Learning. 17th IEEE International Conference on Advanced Learning Technologies (ICALT 2017), Piscataway, NJ: Institute of Electrical and Electronics Engineers Inc. [More Information]

2016

  • Dias Pereira dos Santos, A., Yacef, K., Martinez Maldonado, R. (2016). Visualizing Individual Profiles and Grouping Conditions in Collaborative Learning Activities. 33rd International Conference of Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education (ASCILITE 2016), Adelaide: University Of South Australia.

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