%0 Conference Paper %1 dubovi2017follow %A Dubovi, Ilana %A Dagan, Efrat %A Nasar, Laila %A Mazbar, Ola Sader %A Levy, Sharona T %B Proc. of the 12th Chais Conf. for the Study of Innovation and Learning Technologies: Learning in the Technological Era %D 2017 %K diabities education health inquiry modelling multiagent %P 21-30 %T Follow the Glucose Molecule: Learning Pharmacology by Exploring Multi-Scale Agent-Based Computer Models of Cellular Biochemical Processes and their Interactions Between Organs %U http://www.openu.ac.il/innovation/chais2017/b2_2.pdf %X This paper presents the Pharmacology Inter-Leaved Learning-Cells (PILLCells) environment. This is a suite of multi-scale agent-based computer models that enable nursing students to investigate the biochemical processes of diabetes and its related medications. These range from the molecular to the cellular to the interactions between organs within a sick or healthy cell-organ. The participants were nursing students who learned about the pharmacology related to diabetes either with computer models (experimental group; n = 94) or via a lecture-based curriculum (comparison group; n = 54). The results revealed significantly higher conceptual learning gains following learning with the PILL-Cells environment compared to studying via the lecture-based curriculum (U = 940, p < 0.001). It was found that the highest conceptual learning gains were for the medication treatment subscale and the highest complex systems learning gains were at the micro-level. These results suggest that learning with the PILL-Cells is highly effective and enhances a micro-level molecular view of the biochemical phenomena, and that this understanding is then related to macro-level phenomena such as medication actions. Additionally, the scores of the course final exam were higher in the experimental group (unpaired t = –2.9, p < 0.05), which suggest that the environment continues to provide a more general reasoning scheme for biochemical processes, and thus enhances the pharmacology curriculum.

@inproceedings{dubovi2017follow, abstract = {This paper presents the Pharmacology Inter-Leaved Learning-Cells (PILLCells) environment. This is a suite of multi-scale agent-based computer models that enable nursing students to investigate the biochemical processes of diabetes and its related medications. These range from the molecular to the cellular to the interactions between organs within a sick or healthy cell-organ. The participants were nursing students who learned about the pharmacology related to diabetes either with computer models (experimental group; n = 94) or via a lecture-based curriculum (comparison group; n = 54). The results revealed significantly higher conceptual learning gains following learning with the PILL-Cells environment compared to studying via the lecture-based curriculum (U = 940, p < 0.001). It was found that the highest conceptual learning gains were for the medication treatment subscale and the highest complex systems learning gains were at the micro-level. These results suggest that learning with the PILL-Cells is highly effective and enhances a micro-level molecular view of the biochemical phenomena, and that this understanding is then related to macro-level phenomena such as medication actions. Additionally, the scores of the course final exam were higher in the experimental group (unpaired t = –2.9, p < 0.05), which suggest that the environment continues to provide a more general reasoning scheme for biochemical processes, and thus enhances the pharmacology curriculum. }, added-at = {2017-09-07T10:04:35.000+0200}, author = {Dubovi, Ilana and Dagan, Efrat and Nasar, Laila and Mazbar, Ola Sader and Levy, Sharona T}, biburl = {https://www.bibsonomy.org/bibtex/228f0f7237510a3005f40049188aba8bc/yish}, booktitle = {Proc. of the 12th Chais Conf. for the Study of Innovation and Learning Technologies: Learning in the Technological Era}, interhash = {f68b9ff7918f23db90502aa10183c67d}, intrahash = {28f0f7237510a3005f40049188aba8bc}, keywords = {diabities education health inquiry modelling multiagent}, pages = {21-30}, timestamp = {2017-09-07T10:04:35.000+0200}, title = {Follow the Glucose Molecule: Learning Pharmacology by Exploring Multi-Scale Agent-Based Computer Models of Cellular Biochemical Processes and their Interactions Between Organs}, url = {http://www.openu.ac.il/innovation/chais2017/b2_2.pdf}, year = 2017 }