00572nas a2200157   4500008004100000245007500041210006900116260000900185653001500194653000800209100002000217700001900237700001800256700002400274856011600298       2021                        eng d00aMachine Learning and Survey-based Predictors of InfoSec Non-Compliance0 aMachine Learning and Surveybased Predictors of InfoSec NonCompli  c202110aAccounting10aBIS1 aMarshall, Byron1 aCurry, Michael1 aCorreia, John1 aCrossler, Robert, E  uhttps://dev.business.oregonstate.edu/biblio/machine-learning-and-survey-based-predictors-infosec-non-compliance02601nas a2200193   4500008004100000245007500041210006900116260000900185300000900194490000700203520197500210653001502185653000802200100002002208700001902228700001802247700002402265856011802289       2021                        eng d00aMachine Learning and Survey-based Predictors of InfoSec Non-Compliance0 aMachine Learning and Surveybased Predictors of InfoSec NonCompli  c2021  a1-200 v133 aSurvey items developed in behavioral <b>Information Security (InfoSec)</b> research should be practically useful in identifying individuals who are likely to create risk by failing to comply with InfoSec guidance. The literature shows that attitudes, beliefs, and perceptions drive compliance behavior and has influenced the creation of a multitude of training programs focused on improving ones’ InfoSec behaviors. While automated controls and directly observable technical indicators are generally preferred by InfoSec practitioners, difficult-to-monitor user actions can still compromise the effectiveness of automatic controls. For example, despite prohibition, doubtful or skeptical employees often increase organizational risk by using the same password to authenticate corporate and external services. Analysis of network traffic or device configurations is unlikely to provide evidence of these vulnerabilities but responses to well-designed surveys might. Guided by the relatively new IPAM model, this study administered 96 survey items from the Behavioral InfoSec literature, across three separate points in time, to 217 respondents. Using systematic feature selection techniques, manageable subsets of 29, 20, and 15 items were identified and tested as predictors of non-compliance with security policy. The feature selection process validates IPAM's innovation in using nuanced self-efficacy and planning items across multiple time frames. Prediction models were trained using several ML algorithms. Practically useful levels of prediction accuracy were achieved with, for example, ensemble tree models identifying 69% of the riskiest individuals within the top 25% of the sample. The findings indicate the usefulness of psychometric items from the behavioral InfoSec in guiding training programs and other cybersecurity control activities and demonstrate that they are promising as additional inputs to AI models that monitor networks for security events.10aAccounting10aBIS1 aMarshall, Byron1 aCurry, Michael1 aCorreia, John1 aCrossler, Robert, E  uhttps://dev.business.oregonstate.edu/biblio/machine-learning-and-survey-based-predictors-infosec-non-compliance-000521nas a2200145   4500008004100000245006900041210006500110260001900175653001500194653000800209100002000217700001900237700001900256856010000275       2014                        eng d00aThe Moderating Power of IT Bias on User Acceptance of Technology0 aModerating Power of IT Bias on User Acceptance of Technology  aAucklandc201410aAccounting10aBIS1 aMarshall, Byron1 aCurry, Michael1 aKawalek, Peter  uhttps://dev.business.oregonstate.edu/biblio/moderating-power-it-bias-user-acceptance-technology01362nas a2200181   4500008004100000245008600041210006900127260000900196300001400205490000700219520077400226653001501000653000801015100002001023700001901043700002401062856009401086       2006                        eng d00aMatching Knowledge Elements in Concept Maps Using a Similarity Flooding Algorithm0 aMatching Knowledge Elements in Concept Maps Using a Similarity F  c2006  a1290-13060 v423 aConcept mapping systems used in education and knowledge management emphasize flexibility of representation to enhance learning and facilitate knowledge capture. Collections of concept maps exhibit terminology variance, informality, and organizational variation. These factors make it difficult to match elements between maps in comparison, retrieval, and merging processes. In this work, we add an element anchoring mechanism to a similarity flooding (SF) algorithm to match nodes and substructures between pairs of simulated maps and student-drawn concept maps. Experimental results show significant improvement over simple string matching with combined recall accuracy of 91% for conceptual nodes and concept ¨ link ¨ concept propositions in student-drawn maps.10aAccounting10aBIS1 aMarshall, Byron1 aChen, Hsinchun1 aMadhusudan, Therani  uhttp://people.oregonstate.edu/~marshaby/Papers/MatchKnowledgeElements_PrePrintVersion.pdf01741nas a2200181   4500008004100000245008600041210006900127260000900196490000600205520119200211653001501403653000801418100002001426700001901446700001401465700002001479856006001499       2006                        eng d00aMoving Digital Libraries into the Student Learning Space: the GetSmart Experience0 aMoving Digital Libraries into the Student Learning Space the Get  c20060 v63 aThe GetSmart system was built to support theoretically sound learning processes in a digital library environment by integrating course management, digital library, and concept mapping components to support a constructivist, six-step, information search process. In the fall of 2002 more than 100 students created 1400 concept maps as part of selected computing classes offered at the University of Arizona and Virginia Tech. Those students conducted searches, obtained course information, created concept maps, collaborated in acquiring knowledge, and presented their knowledge representations. This article connects the design elements of the GetSmart system to targeted concept-map-based learning processes, describes our system and research testbed, and analyzes our system usage logs. Results suggest that students did in fact use the tools in an integrated fashion, combining knowledge representation and search activities. After concept mapping was included in the curriculum, we observed improvement in students' online quiz scores. Further, we observed that students in groups collaboratively constructed concept maps with multiple group members viewing and updating map details.10aAccounting10aBIS1 aMarshall, Byron1 aChen, Hsinchun1 aShen, Rao1 aFox, Edward, A.  uhttp://portal.acm.org/citation.cfm?doid=1217862.1217864