TY - JOUR
T1 - Unpacking help-seeking process through multimodal learning analytics
T2 - A comparative study of ChatGPT vs Human expert
AU - Chen, Angxuan
AU - Xiang, Mengtong
AU - Zhou, Junyi
AU - Jia, Jiyou
AU - Shang, Junjie
AU - Li, Xinyu
AU - Gašević, Dragan
AU - Fan, Yizhou
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2025/3
Y1 - 2025/3
N2 - Help-seeking is an active learning strategy tied to self-regulated learning (SRL), where learners seek assistance when facing challenges. They may seek help from teachers, peers, intelligent tu-tor systems, and more recently, generative artificial intelligence (AI). However, there is limited empirical research on how learners’ help-seeking process differs between generative AI and hu-man experts. To address this, we conducted a lab experiment with 38 university students tasked with essay writing and revising. The students were randomly divided into two groups: one seeking help from ChatGPT (AI Group) and the other from an experienced teacher (HE Group). To examine their help-seeking processes, we used a combination of statistical testing and process mining methods, analyzing multimodal data (e.g., trace data, eye-tracking data, and conversa-tional data). Our results indicated that the AI Group exhibited a nonlinear help-seeking process, such as skipping evaluation, differing significantly from the linear model observed in the HE Group which also aligned with classic help-seeking theory. Detailed analysis revealed that the AI Group asked more operational questions, showing pragmatic help-seeking activities, whereas the HE Group was more proactive in evaluating and processing received feedback. We discussed factors such as social pressure, metacognitive off-loading, and over-reliance on AI in these different help-seeking scenarios. More importantly, this study offers innovative insights and evidence, based on multimodal data, to better understand and scaffold learners learning with generative AI.
AB - Help-seeking is an active learning strategy tied to self-regulated learning (SRL), where learners seek assistance when facing challenges. They may seek help from teachers, peers, intelligent tu-tor systems, and more recently, generative artificial intelligence (AI). However, there is limited empirical research on how learners’ help-seeking process differs between generative AI and hu-man experts. To address this, we conducted a lab experiment with 38 university students tasked with essay writing and revising. The students were randomly divided into two groups: one seeking help from ChatGPT (AI Group) and the other from an experienced teacher (HE Group). To examine their help-seeking processes, we used a combination of statistical testing and process mining methods, analyzing multimodal data (e.g., trace data, eye-tracking data, and conversa-tional data). Our results indicated that the AI Group exhibited a nonlinear help-seeking process, such as skipping evaluation, differing significantly from the linear model observed in the HE Group which also aligned with classic help-seeking theory. Detailed analysis revealed that the AI Group asked more operational questions, showing pragmatic help-seeking activities, whereas the HE Group was more proactive in evaluating and processing received feedback. We discussed factors such as social pressure, metacognitive off-loading, and over-reliance on AI in these different help-seeking scenarios. More importantly, this study offers innovative insights and evidence, based on multimodal data, to better understand and scaffold learners learning with generative AI.
KW - 21st century abilities
KW - Data science applications in education
KW - Human-AI interaction
KW - Human-computer interface
KW - Information literacy
UR - http://www.scopus.com/inward/record.url?scp=85209570216&partnerID=8YFLogxK
U2 - 10.1016/j.compedu.2024.105198
DO - 10.1016/j.compedu.2024.105198
M3 - Article
AN - SCOPUS:85209570216
SN - 0360-1315
VL - 226
JO - Computers and Education
JF - Computers and Education
M1 - 105198
ER -