2 research outputs found
NEOLAF, an LLM-powered neural-symbolic cognitive architecture
This paper presents the Never Ending Open Learning Adaptive Framework
(NEOLAF), an integrated neural-symbolic cognitive architecture that models and
constructs intelligent agents. The NEOLAF framework is a superior approach to
constructing intelligent agents than both the pure connectionist and pure
symbolic approaches due to its explainability, incremental learning,
efficiency, collaborative and distributed learning, human-in-the-loop
enablement, and self-improvement. The paper further presents a compelling
experiment where a NEOLAF agent, built as a problem-solving agent, is fed with
complex math problems from the open-source MATH dataset. The results
demonstrate NEOLAF's superior learning capability and its potential to
revolutionize the field of cognitive architectures and self-improving adaptive
instructional systems
Predicting Students' Attention Level with Interpretable Facial and Head Dynamic Features in an Online Tutoring System (Student Abstract)
Engaged learners are effective learners. Even though it is widely recognized that engagement plays a vital role in learning effectiveness, engagement remains to be an elusive psychological construct that is yet to find a consensus definition and reliable measurement. In this study, we attempted to discover the plausible operational definitions of engagement within an online learning context. We achieved this goal by first deriving a set of interpretable features on dynamics of eyes, head and mouth movement from facial landmarks extractions of video recording when students interacting with an online tutoring system. We then assessed their predicative value for engagement which was approximated by synchronized measurements from commercial EEG brainwave headset worn by students. Our preliminary results show that those features reduce root mean-squared error by 29% compared with default predictor and we found that the random forest model performs better than a linear regressor