ML-based intelligent real-time feedback system for blended classroom

被引:3
作者
Biswas, Ujjwal [1 ]
Bhattacharya, Samit [1 ]
机构
[1] Indian Inst Technol Guwahati, Dept Comp Sci & Engn, User Centr Comp & Networking Lab, Gauhati 781039, Assam, India
关键词
Blended learning; Classroom-centered feedback; E-learning tools; Machine learning; Predictive model; ALARMS;
D O I
10.1007/s10639-023-11949-5
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
The application of machine learning (ML) has grown and is now used to enhance learning outcomes. In blended classroom settings, ML, emerging smartphones and wearable technologies are commonly used to improve teaching and learning. The combination of these advanced technologies and ML plays a crucial role in enhancing real-time feedback quality. However, there are abundant scopes of improvement and strong need for further careful investigations in this area. We propose an ML-based intelligent real-time feedback system to address current research challenges for blended classrooms. The proposed system provides real-time feedback to students and teachers. We build an Android application for our intelligent feedback interfaces. The user interfaces use students' academic performance prediction models with real-time states and dynamic feedback timings based on historic feedback statistics. In addition, the feedback scheduling algorithms, choices of peripheral devices for real-time feedback, and feedback modalities to optimize fatigue make our system interfaces intelligent and novel. The end users well-received the intelligent features and technology of the proposed system. Our empirical findings indicate that unique design elements, such as dynamic timing, choice of peripheral devices, and modalities of real-time feedback, are crucial in integrating the system with blended classes. The intelligent characteristics of the proposed system have been appreciated by a large proportion of the end-users (90.90% of teachers and 84.21% of students) for use in real-time blended classroom environments. The higher comparative system usability scale (SUS) scores with benchmarks show real promise of the system design.
引用
收藏
页码:3923 / 3951
页数:29
相关论文
共 47 条
  • [1] Spatial Computing and Social Media in the Context of Disaster Management
    Adam, Nabil R.
    Shafiq, Basit
    Staffin, Robin
    [J]. IEEE INTELLIGENT SYSTEMS, 2012, 27 (06) : 90 - 97
  • [2] Albert W., 2013, Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics
  • [3] Educational data mining and learning analytics for 21st century higher education: A review and synthesis
    Aldowah, Hanan
    Al-Samarraie, Hosam
    Fauzy, Wan Mohamad
    [J]. TELEMATICS AND INFORMATICS, 2019, 37 : 13 - 49
  • [4] Augusto JC, 2010, HANDBOOK OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, P3, DOI 10.1007/978-0-387-93808-0_1
  • [5] Bangor A, 2009, J USABILITY STUD, V4, P114
  • [6] Educational data mining to predict students' academic performance: A survey study
    Batool, Saba
    Rashid, Junaid
    Nisar, Muhammad Wasif
    Kim, Jungeun
    Kwon, Hyuk-Yoon
    Hussain, Amir
    [J]. EDUCATION AND INFORMATION TECHNOLOGIES, 2023, 28 (01) : 905 - 971
  • [7] A Real-time Interactive Visualizer for Large Classroom
    Bhattacharya, Samit
    Shah, Viral Bharat
    Kumar, Krishna
    Biswas, Ujjwal
    [J]. ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS, 2021, 11 (01)
  • [8] Biswas U., 2022, ICT AN APPL P ICT4SD, P703
  • [9] Boucek G.P. Jr., 1977, HUMAN FACTORS GUIDEL, VII
  • [10] Investigating Multimodal Warnings for Distracted Smartphone Users on the Move in Potentially Dangerous Situations
    Braun, Melinda C.
    Beuck, Sandra
    Woelfel, Matthias
    Scheurer, Alexander
    [J]. TRANSACTIONS ON COMPUTATIONAL SCIENCE XXXII: SPECIAL ISSUE ON CYBERSECURITY AND BIOMETRICS, 2018, 10830 : 1 - 14