Three studies were conducted to better inform how instructional design of educational programming for children impacts learning. In these studies, we focused on how unplugged debugging activities, which require correction of coding errors, affect skills related to computational thinking and personal attributes of children. Study 1 observed debugging performance across varying degrees of embodiment (full and low) with a control group. To identify and rectify coding errors, children in the full embodiment group walked on a floor maze whereas low embodiment group manipulated a paper character using their fingers. Study 2 examined the effects of different degrees of embodiment when combined with either coding or narrative based language on computational thinking and self-efficacy. Children fixed coding errors on a worksheet using coding language or narratives, then performed their revised code using full or low embodiment. Study 3 explored whether congruent or incongruent hand gestures incorporated with either direct or surrogate embodiment enhanced children’s graphic and text programming, self-efficacy, and persistence. In the congruent gesture group, participants placed coding blocks in the same direction that the programming character moves whereas incongruent gesture placed coding blocks in a linear fashion. Direct embodiment is where the participant uses their finger to move a character whereas surrogate embodiment is where the researcher is controlled by the participant through verbal commands. The results on computational thinking skills were: 1) Children performed better in debugging and problem solving using low embodiment; 2) Programming efficiency increased with the use of coding language; 3) Higher performance on graphic programming was found with incongruent gesture while transfer from graphic to text programming improved with surrogate embodiment. In personal attributes: 1) Significant interaction effect was found between hand gesture and embodiment on self-efficacy; 2) Higher persistence was exhibited from direct embodiment. These findings between embodiment and development of computational thinking skills and personal attributes may be utilized in the unplugged learning environment. This is particularly relevant in supporting students to acquire basic computational thinking skills where relevant technology resources are not available.

Traditional Instructional Systems Design (ISD) has focused upon measurable learning, but the emerging emphasis on informal methods of learning, combined with powerful mobile computing devices, is turning the spotlight to mobile performance support (mPS) in lieu of formal instruction. However, there is thus far little research into how to design effective mPS, what design principles should be brought to bear, how mPS tools should be evaluated, and so on (Martin, Díaz, Martínez-Mediano, Sancristobal, Oliva, Peire, & Castro, 2010; Paino & Rossett, 2008), leaving designers of mPS without evidence-based guidance for their practice. To capture a picture of methodologies presently being used to design mPS, as well as the perceived effectiveness of those methods, three recent examples of mPS were examined and the individuals responsible for them were interviewed. The first case was the "Bedsider", created by the National Campaign to Prevent Teen and Unplanned Pregnancy, a private, nonprofit organization. The second case was a "Turbulence App", created by Delta Air Lines, Inc., a publicly traded for-profit organization. The final case was the "Prescribed Fire Smoke Management Pocket Guide", or "Smoke App", created through the collaboration of the U.S. Department of Defense’s Advanced Distributed Learning Initiative and the Southeastern Regional Partnership for Planning And Sustainability. Each case study focused upon process employed, results, and concerns. Cross-case analysis examined similarities and differences between the methodologies, as well as apparent importance of the variation. The individual mPS design approaches were compared with traditional ISD methodology, and the three mPS design models were aggregated into a consensus mPS design model (CDM) and compared with traditional ISD methodology to identify any significant differences. This study sought to answer two major questions and two related secondary questions. The first major question was, what mPS design methodologies are organizations actually using? The individual design models in use are described in each case study. The second major question was, how do the methodologies in use compare with traditional instructional design methodology? Although similar in many ways, there were important divergences, particularly with regard to analysis and evaluation activities. The answer to the third questions, how the mPS methodologies in actual use are perceived in terms of effectiveness, was unequivocal. The sponsors of all three cases expressed complete satisfaction with the resulting mPS tools and with the outcomes. The fourth question was, how might the mPS design methodologies in actual use be improved? Several interviewees shared notions of what they "would do differently next time", and the consensus model provided the basis for suggestions for improvement in the front-end and back-end design activities. The significance of this study lies in the documentation of design procedures presently used to create mPS, and the description of how instructional designers might wish to approach the design of mPS in the future. This study helped identify and describe models in actual use in three organizations representing for-profit, not-for-profit, and government sectors, and examined the implications of differences between those models and traditional ISD methodology. This research also makes recommendations for improvement in mPS design practices. Those who follow this advice may find that they design better mPS applications / A Dissertation submitted to the Department of Educational Psychology and Learning Systems in partial fulfillment of the Doctor of Philosophy. / Fall Semester 2016. / November 10, 2016. / ADDIE, EPSS, instructional design, mLearning, mobile, performance support / Includes bibliographical references. / Robert A. Reiser, Professor Directing Dissertation; Stephen D. McDowell, University Representative; Vanessa P. Dennen, Committee Member; Marcy P. Driscoll, Committee Member; James D. Klein, Committee Member.

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A Knowledge-Based Interval Modeling (KBIM) Method is introduced for global optimization and process tuning. A novel feature of the KBIM Method is its ability to take advantage of the a priori knowledge of the system by incorporating the linear/nonlinear sensitivity information between the objective function/constraints and the system variables in the form of an interval model. The interval model is then used to estimate the feasible/plausible region within the input space as the basis of search for the global optimum. The noted features of the KBIM Method are that (1) initial trials are not required to construct the interval model, (2) the interval model produces bounds for the objective function and constraints so that it may not be trapped into the local optima, and (3) learning is incorporated to update the interval model based on new input-output data that become available during the search. The updated interval model is shown to lead to more accurate estimates of the feasible/plausible region. The utility of the KBIM Method is demonstrated in three different fields: global optimization, injection molding process tuning, and helicopter track and balance. In global optimization, the KBIM Method is used to search for the global optimum of both unconstrained and constrained benchmark problems. In tuning of injection molding, the method is used as an on-line tuning method to define the feasible region (process window) of the process and to search for a set of feasible machine setpoints in order to improve the production yield. In helicopter track and balance, the KBIM Method selects a set of blade modifications so as to reduce the vibration of the aircraft within the specification limits. The application results indicate that the method provides a viable means of incorporating the a priori knowledge for global optimization and process tuning.

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