A Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners’ Affective States

Creative Commons License

Hasnine M. N., Nguyen H. T., Tran T. T. T., Bui H. T. T., AKÇAPINAR G., Ueda H.

Sensors, cilt.23, sa.9, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23 Sayı: 9
  • Basım Tarihi: 2023
  • Doi Numarası: 10.3390/s23094243
  • Dergi Adı: Sensors
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, CAB Abstracts, Communication Abstracts, Compendex, EMBASE, INSPEC, MEDLINE, Metadex, Veterinary Science Database, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: affective states detection, AI in education, dashboard, emotion, learning analytics framework, lecture video analysis
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Students’ affective states describe their engagement, concentration, attitude, motivation, happiness, sadness, frustration, off-task behavior, and confusion level in learning. In online learning, students’ affective states are determinative of the learning quality. However, measuring various affective states and what influences them is exceedingly challenging for the lecturer without having real interaction with the students. Existing studies primarily use self-reported data to understand students’ affective states, while this paper presents a novel learning analytics system called MOEMO (Motion and Emotion) that could measure online learners’ affective states of engagement and concentration using emotion data. Therefore, the novelty of this research is to visualize online learners’ affective states on lecturers’ screens in real-time using an automated emotion detection process. In real-time and offline, the system extracts emotion data by analyzing facial features from the lecture videos captured by the typical built-in web camera of a laptop computer. The system determines online learners’ five types of engagement (“strong engagement”, “high engagement”, “medium engagement”, “low engagement”, and “disengagement”) and two types of concentration levels (“focused” and “distracted”). Furthermore, the dashboard is designed to provide insight into students’ emotional states, the clusters of engaged and disengaged students’, assistance with intervention, create an after-class summary report, and configure the automation parameters to adapt to the study environment.