Objective This study determined the efficacy of a novel point-of-care immunoflow device (POCID) for detecting matrix metalloproteinase (MMP)-8 concentrations in oral fluids in comparison with a gold-standard laboratory-based immunoassay. Methods Oral rinse fluid and whole expectorated saliva samples were collected from 41 participants clinically classified as periodontally healthy or diseased. Samples were analyzed for MMP-8 by Luminex immunoassay and POCID. Photographed POCID results were assessed by optical scan and visually by two examiners. Data were analyzed by Pearson correlation and receiver operator characteristics. Results MMP-8 was readily detected by the POCID, and concentrations correlated well with Luminex for both saliva and rinse fluids (r=0.57–0.93). Thresholds that distinguished periodontitis from health were delineated from both the optical scans and visual reads of the POCID (sensitivity 0.7–0.9, specificity 0.5–0.7; p < 0.05). Conclusions Performance of this POCID for detecting MMP-8 in oral rinse fluid or saliva was excellent. These findings help demonstrate the utility of salivary biomarkers for distinguishing periodontal disease from health using a rapid point-of-care approach.

Previous studies for menu and Web search tasks have suggested differing advice on the optimal number of selections per page. In this paper, we examine this discrepancy through the use of a computational model of information navigation that simulates users navigating through a Web site. By varying the quality of the link labels in our simulations, we find that the optimal structure depends on the quality of the labels and are thus able to account for the results in the previous studies. We present additional empirical results to further validate the model and corroborate our findings. Finally we discuss our findings' implications for the information architecture of Web sites. CONTENTSModeling Information Navigation 1

We explored the consequences for learning through interaction with an educational microworld called Electric Field Hockey (EFH). Like many microworlds, EFH is intended to help students develop a qualitative understanding of the target domain, in this case, the physics of electrical interactions. Through the development and use of a computer model that learns to play EFH, we analyzed the knowledge the model acquired as it applied the game-oriented strategies we observed physics students using. Through learning-by-doing on the standard sequence of tasks, the model substantially improved its EFH playing ability; however, it did so without acquiring any new qualitative physics knowledge. This surprising result led to an experiment that compared students' use of EFH with standard-goal tasks against two alternative instructional conditions, specific-path and no-goal, each justified from a different learning theory. Students in the standard-goal condition learned less qualitative physics than did those in the two alternative conditions, which was consistent with the model. The implication for instructional practice is that careful selection and analysis of the tasks that frame microworld use is essential if these programs are to lead to the learning outcomes imagined for them. Theoretically, these results suggest a new interpretation for numerous empirical findings on the effectiveness of no-goal instructional tasks. The standing ''reduced cognitive load'' interpretation is contradicted by the success of the specific-path condition, and we offer an alternative knowledge-dependent interpretation.

No abstract

We explored the consequences for learning through interaction with an educational microworld called Electric Field Hockey (EFH). Like many microworlds, EFH is intended to help students develop a qualitative understanding of the target domain, in this case, the physics of electrical interactions. Through the development and use of a computer model that learns to play EFH, we analyzed the knowledge the model acquired as it applied the game-oriented strategies we observed physics students using. Through learning-by-doing on the standard sequence of tasks, the model substantially improved its EFH playing ability; however, it did so without acquiring any new qualitative physics knowledge. This surprising result led to an experiment that compared students' use of EFH with standard-goal tasks against two alternative instructional conditions, specific-path and no-goal, each justified from a different learning theory. Students in the standard-goal condition learned less qualitative physics than did those in the two alternative conditions, which was consistent with the model. The implication for instructional practice is that careful selection and analysis of the tasks that frame microworld use is essential if these programs are to lead to the learning outcomes imagined for them. Theoretically, these results suggest a new interpretation for numerous empirical findings on the effectiveness of no-goal instructional tasks. The standing ''reduced cognitive load'' interpretation is contradicted by the success of the specific-path condition, and we offer an alternative knowledge-dependent interpretation.