TY - GEN
T1 - Towards Personalized Instruction
T2 - 10th International Conference on Learning and Collaboration Technologies, LCT 2023, held as part of the 25th International Conference on Human-Computer Interaction, HCII 2023
AU - Zhang, Han
AU - Sun, Xu
AU - Zhang, Yanhui
AU - Wang, Qingfeng
AU - Yao, Cheng
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Learning engagement is an important factor in academic success and instructional quality. In authentic settings, analyzing learner engagement status can be challenging due to the complexity of its multidimensional construct. It requires constant monitoring of multimodal engagement indicators (cognitive-level indicators, e.g., effort, emotional-level indicators, e.g., positive/negative emotions) and individualized pacing of learners, identifying when and who needs assistance, as well as making pedagogical strategy that matches the needs of the learners (or group of learners). This study combines user requirements with computational techniques to construct a multimodal learning engagement analysis framework in the blended setting. In addition, a teacher-faced dashboard prototype is developed to statistically summarize and visualize multimodal indicators in a way that enables teachers to deploy personalized instruction schemes. The teachers’ perspectives discussed in this study portray the great potential of introducing Artificial Intelligent (AI)-augmented models and visual analytics techniques aimed at deploying personalized instructions in the blended learning environment.
AB - Learning engagement is an important factor in academic success and instructional quality. In authentic settings, analyzing learner engagement status can be challenging due to the complexity of its multidimensional construct. It requires constant monitoring of multimodal engagement indicators (cognitive-level indicators, e.g., effort, emotional-level indicators, e.g., positive/negative emotions) and individualized pacing of learners, identifying when and who needs assistance, as well as making pedagogical strategy that matches the needs of the learners (or group of learners). This study combines user requirements with computational techniques to construct a multimodal learning engagement analysis framework in the blended setting. In addition, a teacher-faced dashboard prototype is developed to statistically summarize and visualize multimodal indicators in a way that enables teachers to deploy personalized instruction schemes. The teachers’ perspectives discussed in this study portray the great potential of introducing Artificial Intelligent (AI)-augmented models and visual analytics techniques aimed at deploying personalized instructions in the blended learning environment.
KW - Dashboard
KW - Human-centered Design
KW - Learning Engagement
KW - Multimodal Data
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85169014096&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-34411-4_38
DO - 10.1007/978-3-031-34411-4_38
M3 - Conference contribution
AN - SCOPUS:85169014096
SN - 9783031344107
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 563
EP - 574
BT - Learning and Collaboration Technologies - 10th International Conference, LCT 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Proceedings
A2 - Zaphiris, Panayiotis
A2 - Ioannou, Andri
A2 - Ioannou, Andri
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 July 2023 through 28 July 2023
ER -