Hameedullah Kazi

Employing UMLS for generating hints in a tutoring system for medical problem-based learning

While problem-based learning has become widely popular for imparting clinical reasoning skills, the dynamics of medical PBL require close attention to a small group of students, placing a burden on medical faculty, whose time is over taxed. Intelligent tutoring systems (ITSs) offer an attractive means to increase the amount of facilitated PBL training the students receive. But typical intelligent tutoring system architectures make use of a domain model that provides a limited set of approved solutions to problems presented to students. Student solutions that do not match the approved ones, but are otherwise partially correct, receive little acknowledgement as feedback, stifling broader reasoning. Allowing students to creatively explore the space of possible solutions is exactly one of the attractive features of PBL. This paper provides an alternative to the traditional ITS architecture by using a hint generation strategy that leverages a domain ontology to provide effective feedback. The concept hierarchy and co-occurrence between concepts in the domain ontology are drawn upon to ascertain partial correctness of a solution and guide student reasoning towards a correct solution. We describe the strategy incorporated in METEOR, a tutoring system for medical PBL, wherein the widely available UMLS is deployed and represented as the domain ontology. Evaluation of expert agreement with system generated hints on a 5-point likert scale resulted in an average score of 4.44 (Spearmans ρ=0.80, p<0.01). Hints containing partial correctness feedback scored significantly higher than those without it (Mann Whitney, p<0.001). Hints produced by a human expert received an average score of 4.2 (Spearmans ρ=0.80, p<0.01).

Expanding the Plausible Solution Space for Robustness in an Intelligent Tutoring System

The knowledge acquisition bottleneck is a problem pertinent to the authoring of any intelligent tutoring system. Allowing students a broad scope of reasoning and solution representation whereby a wide range of plausible student solutions are accepted by the system, places additional burden on knowledge acquisition. In this paper we present a strategy to alleviate the burden of knowledge acquisition for building a tutoring system for medical problem-based learning (PBL). The Unified Medical Language System (UMLS) is deployed as domain ontology and information structure in the ontology is exploited to make intelligent inferences and expand the domain model. Using these inferences and expanded domain model, the tutoring system is able to accept a broader range of plausible student solutions that lie beyond the scope of explicitly encoded solutions. We describe the development of a tutoring system prototype and report the evaluation of system correctness in accepting such plausible solutions. The system evaluation indicates an average accuracy of 94.59 % when compared against human domain experts, who agreed among themselves with a statistical agreement based on Pearson Correlation Coefficient of 0.48 and p < 0.05.

Clinical reasoning gains in medical PBL: an UMLS based tutoring system

Problem based learning is becoming widely popular as an effective teaching method in medical education. Paying individual attention to a small group of students in medical problem-based learning (PBL) can place burden on the workload of medical faculty whose time is very costly. Intelligent tutoring systems offer a cost effective alternative in helping to train the students, but they are typically prone to brittleness and the knowledge acquisition bottleneck. Existing tutoring systems accept a small set of approved solutions for each problem scenario stored into the system. Plausible student solutions that lie outside the scope of the explicitly encoded ones receive little acknowledgment from the system. Tutoring hints are also confined to the knowledge space of the approved solutions, leading to brittleness in the tutoring approach. We report the clinical reasoning gains off a tutoring system for medical PBL that employs and represents the widely available medical knowledge source UMLS as the domain ontology. We exploit the structure of the concept hierarchy to expand the plausible solution space and generate hints based on the problem solving context. Evaluation of student learning outcomes led to highly significant learning gains (Mann-Whitney, p < 0.001).

Visibility statistics based unavailability events estimation of FSO link due to fog in four cities of Pakistan

Currently researchers are very interested in FSO (Free Space Optics) as compare to conventional fiber optic and RF transmission systems at last mile because FSO facilitates easy deployment, cost effective and high data rate with licensed free spectrum. Like other open air wireless systems local climate make performance inefficient due to interference of propagation impairments like rain, fog, mist, haze, snow etc. The effect of fog on narrowband (780 nm to 850nm & 1529 nm to 1600 nm) transmission of FSO system is high due to the comparable drop size of fog with operating wavelength of FSO link. This paper focused on the unavailability events estimation through Kim, Kruse, advection and convection fog attenuation models on the basis of available Visibility statistics of Karachi, Lahore, Islamabad and Sukkur.

METEOR: medical tutor employing ontology for robustness

Problem based learning is becoming widely popular as an effective teaching method in medical education. Paying individual attention to a small group of students in medical PBL can place burden on the workload of medical faculty whose time is very costly. Intelligent tutoring systems offer a cost effective alternative in helping to train the students, but they are typically prone to brittleness and the knowledge acquisition bottleneck. Existing tutoring systems accept a small set of approved solutions for each problem scenario stored into the system. Plausible student solutions that lie outside the scope of the explicitly encoded ones receive little acknowledgment from the system. Tutoring hints are also confined to the knowledge space of the approved solutions, leading to brittleness in the tutoring approach. We report a tutoring system for medical PBL that employs the widely available medical knowledge source UMLS as the domain ontology. We exploit the structure of the ontology to expand the plausible solution space and generate hints based on the problem solving context. Evaluation of student learning outcomes led to highly significant learning gains (Mann-Whitney, p<0.001).