Abstract
Most of the undergraduates couldn’t figure out a proper direction for their future careers since advising depends on the person and students show aversion in revealing their information to other humans. This research focused on creating a social robotic platform to interact with undergraduates in the field of computer science to realize possible career paths, as recent researches show social companion robots tend to form a stronger bond, which helps the addressee to share their information easily. The robot was created as a tabletop robot employing minimalistic design with a friendly view attached with speech synthesis for communicating purposes. Data was collected from 202 persons, who followed a degree in computer science. Data contains their experience, qualifications, and skills. Thereafter an artificial neural network was created using supervised learning to predict the career path with 95% performance accuracy. The experiment was performed by engaging 15 students from the final year who are a doing degree in computer science and they were asked to provide feedback on the interactions with the robot. The gathered responses highlighted robot animacy, interaction, technology and usefulness. Therefore, from the results it can be concluded that a majority of the students accepted the robot, interacted without hesitation and had friendly conversations with the robot where they valued the generated output from the robot.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016), pp. 265–283 (2016)
Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F.: Social robots for education: a review. Sci. Robot. 3(21), eaat5954 (2018)
Broadbent, E., et al.: How could companion robots be useful in rural schools? Int. J. Soc. Robot. 10(3), 295–307 (2018)
Brownlee, J.: A gentle introduction to the rectified linear unit (ReLU). Machine Learning Mastery (2019). https://machinelearningmastery.com/rectified-linear-activation-function-fordeep-learning-neural-networks
Dautenhahn, K.: Socially intelligent robots: dimensions of human-robot interaction. Philos. Trans. Roy. Soc. B Biol. Sci. 362(1480), 679–704 (2007)
Glas, D., Satake, S., Kanda, T., Hagita, N.: An interaction design framework for social robots. In: Robotics: Science and Systems. vol. 7, p. 89 (2012)
Google colab. https://colab.research.google.com/
Hodson, H.: The first family robot (2014)
Intel realsense technology. https://www.intel.com/content/www/us/en/architecture-and-technology/realsense-overview.html
Liu, Y., Zhang, L., Nie, L., Yan, Y., Rosenblum, D.S.: Fortune teller: predicting your career path. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Moote, J., Archer, L.: Failing to deliver? exploring the current status of career education provision in England. Res. Pap. Educ. 33(2), 187–215 (2018)
Ohshima, N., Ohyama, Y., Odahara, Y., De Silva, P.R.S., Okada, M.: Talking-ally: the influence of robot utterance generation mechanism on hearer behaviors. Int. J. Social Robot. 7(1), 51–62 (2015)
Perkins, J.: Python Text Processing with NLTK 2.0 Cookbook. Packt Publishing Ltd., Birmingham (2010)
Pollard, E., et al.: Understanding employers’ graduate recruitment and selection practices: main report (2015)
Rane, P., Mhatre, V., Kurup, L.: Study of a home robot: Jibo. Int. J. Eng. Res. Technol. 3(10), 490–493 (2014)
Setapen, A.A.M.: Creating robotic characters for long-term interaction. Ph.D. thesis, Massachusetts Institute of Technology (2012)
Stemming and lemmatization. https://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html
Subahi, A.F.: Data collection for career path prediction based on analysing body of knowledge of computer science degrees. JSW 13(10), 533–546 (2018)
New world of work: are universities preparing students for future careers? https://www.timeshighereducation.com/hub/pa-consulting/p/new-world-work-are-universities-preparing-students-future-careers. Accessed 20 August 2019
Youssef, K., Yamagiwa, K., Silva, R., Okada, M.: ROBOMO: towards an accompanying mobile robot. In: Beetz, M., Johnston, B., Williams, M.-A. (eds.) ICSR 2014. LNCS (LNAI), vol. 8755, pp. 196–205. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11973-1_20
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mithsara, W.K.M., Manawadu, U.A., De Silva, P.R.S. (2020). A Sociable Robotic Platform to Make Career Advices for Undergraduates. In: Stephanidis, C., Kurosu, M., Degen, H., Reinerman-Jones, L. (eds) HCI International 2020 - Late Breaking Papers: Multimodality and Intelligence. HCII 2020. Lecture Notes in Computer Science(), vol 12424. Springer, Cham. https://doi.org/10.1007/978-3-030-60117-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-60117-1_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60116-4
Online ISBN: 978-3-030-60117-1
eBook Packages: Computer ScienceComputer Science (R0)