Abstract
Designing peripheral warnings and notifications to support teaching-learning is progressively increasing. However, existing work usually fails to effectively integrate real-time alerts and tackle poor students in a blended classroom. We present an in-class multimodal alert method for teachers and students to address the challenges. The system utilizes performance prediction and classification of students for real-time alert. The classification of students based on course performance helped in optimizing the number of alerts. The peripheral device selection aided in preventing the disruption in the lecture follow. Moreover, alert content delivery timing (start, during, and end of the class session) is used to reduce alert fatigue. We reported the design and the initial study results. The results show that 25 teachers and students reacted positively to the system design, technology, and features.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Choudhury N, Venkatesh T, Bhattacharya S, Sarma S (2016) Avabodhaka: a system to analyse and facilitate interactive learning in an JCT based system for large classroom. Proc Comput Sci 84:160–8
Choudhury N, Tamarapalli V, Bhattacharya S (2015) An ICT-based system to improve the learning experience in a large classroom. In: 2015 IEEE seventh international conference on technology for education (T4E). IEEE, pp 27–30
Tikadar S, Bhattacharya S, Tamarapalli V (2018) A blended learning platform to improve teaching-learning experience. In: 2018 IEEE 18th international conference on advanced learning technologies (ICALT). IEEE, pp 87–89
Martinez-Maldonado R, Clayphan A, Yacef K, Kay J (2014) MTFeedback: providing notifications to enhance teacher awareness of small group work in the classroom. IEEE Trans Learn Technol 8(2):187–200
Wang Z, Zhu C, Ying Z, Zhang Y, Wang B, Jin X, Yang H (2018) Design and implementation of early warning system based on educational big data. In: 2018 5th international conference on systems and informatics (ICSAI). IEEE, pp 549–553
Rodríguez-Triana MJ, Prieto LP, Vozniuk A, Boroujeni MS, Schwendimann BA, Holzer A, Gillet D (2017) Monitoring, awareness and reflection in blended technology enhanced learning: a systematic review. Int J Technol Enhanced Learn 9(2–3):126–150
An P, Bakker S, Ordanovski S, Taconis R, Paffen CL, Eggen B (2019) Unobtrusively enhancing reflection-in-action of teachers through spatially distributed ambient information. In: Proceedings of the 2019 CHI conference on human factors in computing systems, pp 1–14
Abugohar M, Yunus K, Rabab’ah G, Ahmed T (2019) Integrating cloud world synergy in ELT to adults: perspectives of handheld technologies
Singley MK, Lam RB (2005) The classroom sentinel: supporting data-driven decision-making in the classroom. In: Proceedings of the 14th international conference on World Wide Web, pp 315–321
Fortin PE (2019) Methods and interfaces for closed loop smartphone notifications. In: Proceedings of the 21st international conference on human-computer interaction with mobile devices and services, pp 1–5
Kushlev K, Proulx J, Dunn EW (2016) Silence your phones. Smartphone notifications increase inattention and hyperactivity symptoms. In: Proceedings of the 2016 CHI conference on human factors in computing systems, pp 1011–1020
Ketabdar H, Moghadam P, Roshandel M (2012) Pingu: a new miniature wearable device for ubiquitous computing environments. In: 2012 sixth international conference on complex, intelligent, and software intensive systems. IEEE, pp 502–506
Braun MC, Beuck S, Wölfel M, Scheurer A (2018) Investigating multimodal warnings for distracted smartphone users on the move in potentially dangerous situations. Transactions on computational science XXXII. Springer, Berlin, Heidelberg, pp 1–14
Holstein K, McLaren BM, Aleven V (2017) Intelligent tutors as teachers’ aides: exploring teacher needs for real-time analytics in blended classrooms. In: Proceedings of the seventh international learning analytics and knowledge conference, pp 257–266
Aldowah H, Al-Samarraie H, Fauzy WM (2019) Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telemat Inf 37:13–49
Talwar J, Ranjani S, Aras A, Bedekar M (2013) Intelligent classroom system for qualitative analysis of students’ conceptual understanding. In: 2013 6th international conference on emerging trends in engineering and technology. IEEE, pp 25–29
Augusto JC, Nakashima H, Aghajan H (2010) Ambient intelligence and smart environments: a state of the art. Handbook of ambient intelligence and smart environments, pp 3–31
Mathioudakis G, Leonidis A, Korozi M, Margetis G, Ntoa S, Antona M, Stephanidis C (2014) Real-time teacher assistance in technologically-augmented smart classrooms. Int J Adv Life Sci 6(1):62–73
Bhattacharya S, Shah VB, Kumar K, Biswas U (2021) A real-time interactive visualizer for large classroom. ACM Trans Interact Intell Syst (TiiS) 11(1):1–26
Tikadar S, Bhattacharya S (2021) Detection of affective states of the students in a blended learning environment comprising of smartphones. Int J Human-Comput Int 37(10):963–980
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 Singapore Pte Ltd.
About this paper
Cite this paper
Biswas, U., Bhattacharya, S. (2023). Multimodal Peripheral Alert to Improve Teaching-Learning for Blended Classroom. In: Fong, S., Dey, N., Joshi, A. (eds) ICT Analysis and Applications. Lecture Notes in Networks and Systems, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-19-5224-1_70
Download citation
DOI: https://doi.org/10.1007/978-981-19-5224-1_70
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5223-4
Online ISBN: 978-981-19-5224-1
eBook Packages: EngineeringEngineering (R0)