In this talk, definition, examples, and a general framework of the adaptive instructional system (AIS) will be introduced within a four-components research and development framework. This framework is called Advanced Learning Theories, Technologies, Applications, and Impacts (ALTTAI). In this framework, theories refer to cognitive principles of human learning, including but not limited to cognitive psychology, human learning & memory, learning space theory, and computational behavior sciences; Technologies refers to enabling advancements, such as Big-Data and cloud computing, educational data mining (EDM), text analysis, semantic representation analysis (SRA), advanced learning environments and intelligent tutoring systems (ITS). Applications refer to those research-based implementations that deliver real learning to learners and produce significant learning gains in real educational settings. Impacts refer to the evaluation framework and large-scale implementations of effective and efficient applications. The example AIS in this talk will be a conversation-based intelligent tutoring system (ITS) called AutoTutor. AutoTutor is an ITS that hold conversations with human learners in natural language. AutoTutor has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). I will introduce three aspects of AutoTutor: human-inspired tutoring strategies, pedagogical agents, and technology that supports natural language tutoring. AutoTutor is a research product funded by US federal agencies for over $30M in the last 20 years. It is an exemplary AIS that exemplifies the ALTTAI research and development framework.
WorkshopConversation-Based Intelligent Tutoring SystemsJune 10, 1:00 to 4:00 - Workshop on CBTS (day 1)June 11, 9:00 to 4:00 - Workshop on CBTS (day 2)
We invite those with the time and the interest to continue the discussions and activities by joining a the two-day workshop that immediately follows the public lecture. This tutorial focuses on the authoring of AutoTutor lessons and Dataanalysis process of Tutoring data:
Authoring of AutoTutor lessons include a) implementing discourse strategies in AutoTutor dialogues and trialogues, b) creating conversation elements (such as media elements); c) conversation rules, and d) using existing well-made authoring templates.
Data analysis process of tutoring data include applying learning analytics methods, such as Bayesian Knowledge Tracing (BKT), Learning Factors Analysis (LFA), Intervention-Bayesian Knowledge Tracing (Intervention-BKT), Cognitive Diagnostic Model,…etc., to leverage the sequences of observations from student-ITS interaction log files to continually update the estimate of student latent knowledge.
Note that workshop attendees must attend the public lecture.
Public lecture only: FreePublic lecture and workshop: P500.00
We will issue payment procedures to attendees upon registration.