The Student Advice Recommender Agent: SARA

Abstract: SARA, the Student Advice Recommender Agent is a system somewhat like an early alert system, where predictive models of learners' success combined with incremental data on learners' activity in a course can be used to identify students in academic distress. With SARA, rather than give alerts to academic advisors or professors, we provide personalized advice directly to students. An advice string -"A note from SARA" is prepared for each student every week in a semester-long course. The system attempts to direct students to appropriate learning supports and resources according to their individual needs. We have observed a significant year over year improvement in unadjusted student grades after the SARA's advice recommender was implemented in a 1200-student freshman STEM course

Recognizing Plans in Instructional Systems Using Granularity

Granularity-based diagnosis has been used in the development of Intelligent Tutoring Systems. While such systems tend to understand student problem solving behaviour after the fact, they do not address the issue of recognizing evolving plans. On the other hand, granularitybased diagnosis does deal effectively with uncertain, incomplete, and inconsistent knowledge. Similar characteristics are desirable for instructional planrecognition systems. We have added a temporal dimension to the basic recognition and matching algorithms of granularity-based diagnosis, thus effectively extending the approach so that it can be used to build instructional planrecognition systems. Work is underway on incorporating this plan-recognition system into HYPHYS, an environment for discovery learning with particle scattering experiments. Introduction An Intelligent Tutoring System (ITS) should provide both an environment for discovery learning and some form of individualized instruction (McCalla & Greer, 19..

The Peculiarities of Plan Recognition for Intelligent Tutoring Systems

Educational applications of AI-based plan recognition offer some distinct challenges to formalized plan recognition methods. Difficulties with incomplete knowledge of all possible plans, inconsistent or noisy behaviour, possible unsound inference on the part of students, and possible uncooperative learners all contribute to severely challenging assumptions in plan recognition schemes. These challenges are raised along with a potential method for coping with the problems using granularity-based reasoning. Introduction Plan recognition research has been popularized in areas such as natural language understanding and user modelling. Surprisingly, there has been little work to date on formalizing methods for plan recognition in intelligent tutoring systems (ITS). While much effort in ITS has been devoted to student modelling, diagnosis and pedagogical planning, the problem of recognizing and reacting to students' plans has been addressed largely with ad hoc methods. The reasons for this la..