A comparison of online versus face-to-face teaching delivery in statistics instruction for undergraduate health science students.

The objective of this study was to assess if online teaching delivery produces comparable student test performance as the traditional face-to-face approach irrespective of academic aptitude. This study involves a quasi-experimental comparison of student performance in an undergraduate health science statistics course partitioned in two ways. The first partition involves one group of students taught with a traditional face-to-face classroom approach and the other through a completely online instructional approach. The second partition of the subjects categorized the academic aptitude of the students into groups of higher and lower academically performing based on their assignment grades during the course. Controls that were placed on the study to reduce the possibility of confounding variables were: the same instructor taught both groups covering the same subject information, using the same assessment methods and delivered over the same period of time. The results of this study indicate that online teaching delivery is as effective as a traditional face-to-face approach in terms of producing comparable student test performance but only if the student is academically higher performing. For academically lower performing students, the online delivery method produced significantly poorer student test results compared to those lower performing students taught in a traditional face-to-face environment.

Intelligent clinical decision support systems based on SNOMED CT.

The decision support systems that have been developed to assist physicians in the diagnostic process often are based on static data which may be out of date. We present a comprehensive analysis of artificial intelligent methods which could be applied to documents encoded by SNOMED CT. By mining information directly from SNOMED CT encoded documents, a decision support system could contain timely updated diagnostic information, which is of significant value in fast changing situations such as minimally understood emerging diseases and epidemics. Through a high level comparison of many AI methods it is found that a TAN-Bayesian method could be the most suitable to apply to SNOMED CT data.