Abstract
Agile estimation is performed by teams to predict relative effort needed to finish project tasks. Estimating in diverse teams may be challenging due to different subjective perspectives. Planning poker is a game-based technique applied by agile teams to empower all team members to jointly estimate and reach a consensus about the predicted effort. This paper presents results from two studies on learning agile estimation in which student teams were supported by the humanoid robot NAO acting as facilitator of the game. The paper describes the design of the robot-assisted planning poker simulation, the programming of the application, and the evaluation results from two pilot studies with 29 bachelor and master students in different study programs and with different cultural backgrounds. The evaluation aimed to investigate students' perceptions of the robotic facilitator, self-assessment of learning outcomes related to agile estimation, and possible effects of different cultural dimensions on the perceptions of the robot-assisted simulations and the learning outcomes. The results show that both bachelor and master students, independent of their cultural background, perceived the NAO robot as a trustworthy, friendly and likeable facilitator of the planning poker game. Students with a European background rated the possibility of befriending NAO slightly higher compared to non-European students. Students also reported that playing planning poker in teams with support of the robot helped them to understand agile estimation. The participants recommended to use the simulation game “Planning poker with NAO" in future classes on project management and estimation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sudarmaningtyas, P., Mohamed, R.B.: Extended planning poker: a proposed model. In: 2020 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), pp. 179–184 (2020)
Digital.ai: 15th State of Agile Report (2022). https://info.digital.ai/rs/981-LQX-968/images/AR-SA-2022-16th-Annual-State-Of-Agile-Report.pdf. Accessed 15 Jan 2023
Alhamed, M., Storer, T.: Playing planning poker in crowds: human computation of software effort estimates. In: 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), pp. 1–12 (2021)
Mahnic, V., Hovelja, T.: On using planning poker for estimating user stories. J. Syst. Softw. 85(9), 2086–2095 (2012)
Digital.ai: 15th State of Agile Report (2021). https://info.digital.ai/rs/981-LQX-968/images/SOA15.pdf. Accessed 18 Dec 2022
Rojas Puentes, M.P., Mora Méndez, M.F., Bohórquez Chacón, L.F., Romero, S.M.: Estimation metrics in software projects. J. Phys. Conf. Ser. 1126 (2018)
Chatzipetrou, P., Ouriques, R.A., Gonzalez-Huerta, J.: Approaching the Relative Estimation Concept with Planning Poker. CSERC 2018 (2018)
Zhang, Z.: The Benefits and Challenges of Planning Poker in Software Development: Comparison Between Theory and Practice. Auckland University of Technology (2017). https://openrepository.aut.ac.nz/handle/10292/10557. Accessed 18 Dec 2022
Lopez-Martinez, J., Ramirez-Noriega, A., Juarez-Ramirez, R., Licea, G., Martinez-Ramirez, Y.: Analysis of Planning Poker Factors between University and Enterprise. pp. 54–60 (2017)
Ceha, J., Law, E., Kulić, D., Oudeyer, P.-Y., Roy, D.: Identifying functions and behaviours of social robots for in-class learning activities: teachers’ perspective. Int. J. Soc. Robot. 14, 1–15 (2021). https://doi.org/10.1007/s12369-021-00820-7
Ekström, S., Pareto, L.: The dual role of humanoid robots in education: as didactic tools and social actors. Educ. Inf. Technol. 27, 12609–12644 (2022)
Spatola, N., Kühnlenz, B., Cheng, G.: Perception and evaluation in human-robot interaction: the human-robot interaction evaluation scale (HRIES)—a multicomponent approach of anthropomorphism. Int. J. Soc. Robot. 13, 1517–1539 (2021)
Buchem, I., Baecker, N.: NAO robot as scrum master: results from a scenario-based study on building rapport with a humanoid robot in hybrid higher education settings. In: Salman Nazir (eds) Training, Education, and Learning Sciences. AHFE (2022) International Conference. AHFE Open Access, vol. 59, pp. 65–73 (2022)
Dolnicar, S.: 5/7-point “Likert scales” aren’t always the best option: their validity is undermined by lack of reliability, response style bias, long completion times and limitations to permissible statistical procedures. Ann. Tour. Res. 91, 103297 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Buchem, I., Christiansen, L., Glissmann-Hochstein, S., Sostak, S. (2023). Learning Agile Estimation in Diverse Student Teams by Playing Planning Poker with the Humanoid Robot NAO. Results from Two Pilot Studies in Higher Education. In: Zaphiris, P., Ioannou, A. (eds) Learning and Collaboration Technologies. HCII 2023. Lecture Notes in Computer Science, vol 14041. Springer, Cham. https://doi.org/10.1007/978-3-031-34550-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-34550-0_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34549-4
Online ISBN: 978-3-031-34550-0
eBook Packages: Computer ScienceComputer Science (R0)