Medium Altitude Long Endurance RPA Training: Evaluating Blended Learning

The Heads Down Display (HDD) Menu Trainer – a stand-alone software trainer – was developed to familiarize students in Remotely Piloted Aircraft training with the layout and manipulation of the HDD menus for either the MQ-1 or MQ-9. Preliminary work by Waller et al. (2016) established the efficacy of the HDD Menu Trainer in improving student performance from pretest to posttest scores across several modalities (i.e. traditional, blended, and distance). Recognizing that students holding pilot certification scored higher in some aspects of the HDD Menu Trainer, this study sampled students across a curriculum to assess whether performance with the HDD Menu Trainer would differ across modalities (i.e. traditional, blended, and distance) when FAA pilot certification was controlled. Results of a mixed factorial ANCOVA indicated the effectiveness of the HDD menu trainer once more through a main within-subjects effect of performance and performance was again higher for students holding an FAA pilot certificate than for those without. However, modality failed to demonstrate a significant interaction effect with student performance from pretest to posttest. These results affirm that even outside the variation which should be attributed to a student’s pilot certification, the HDD Menu Trainer demonstrates equal effectiveness when used in blended and distance modalities. These results support several prior works finding blended learning applications to be at least as effective as other modalities. As blended, flipped, and hybrid learning models are increasingly expected within higher education curriculums, future work is anticipated in the construct of student engagement (Borup et al., 2020; Halverson & Graham, 2019)

Medium Altitude Long Endurance Remotely Piloted Aircraft Training: a Pilot Study in Blended Learning

Since April of 2011, research and development efforts between the Air Force Research Laboratory (AFRL) and the University of North Dakota (UND) have progressed through the “Science and Technology for Warfighter Training and Aiding.” Cooperative Agreement. One product of these cooperative efforts has been a Heads Down Display (HDD) Menu Trainer. Designed to familiarize students with the layout and manipulation of the HDD menus for either the MQ-1 or MQ-9, a parallel pretest/posttest design was designed to examine the efficacy of this HDD menu trainer as training aid in traditional, blended, and distance pedagogies. Results of a mixed ANOVA indicated the trainer significantly improved performance from pretest to posttest scores across all groups (pp

Towards case-based medical learning in radiological decision making using content-based image retrieval

<p>Abstract</p> <p>Background</p> <p>Radiologists' training is based on intensive practice and can be improved with the use of diagnostic training systems. However, existing systems typically require laboriously prepared training cases and lack integration into the clinical environment with a proper learning scenario. Consequently, diagnostic training systems advancing decision-making skills are not well established in radiological education.</p> <p>Methods</p> <p>We investigated didactic concepts and appraised methods appropriate to the radiology domain, as follows: (i) Adult learning theories stress the importance of work-related practice gained in a team of problem-solvers; (ii) Case-based reasoning (CBR) parallels the human problem-solving process; (iii) Content-based image retrieval (CBIR) can be useful for computer-aided diagnosis (CAD). To overcome the known drawbacks of existing learning systems, we developed the concept of image-based case retrieval for radiological education (IBCR-RE). The IBCR-RE diagnostic training is embedded into a didactic framework based on the Seven Jump approach, which is well established in problem-based learning (PBL). In order to provide a learning environment that is as similar as possible to radiological practice, we have analysed the radiological workflow and environment.</p> <p>Results</p> <p>We mapped the IBCR-RE diagnostic training approach into the Image Retrieval in Medical Applications (IRMA) framework, resulting in the proposed concept of the IRMAdiag training application. IRMAdiag makes use of the modular structure of IRMA and comprises (i) the IRMA core, i.e., the IRMA CBIR engine; and (ii) the IRMAcon viewer. We propose embedding IRMAdiag into hospital information technology (IT) infrastructure using the standard protocols Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7). Furthermore, we present a case description and a scheme of planned evaluations to comprehensively assess the system.</p> <p>Conclusions</p> <p>The IBCR-RE paradigm incorporates a novel combination of essential aspects of diagnostic learning in radiology: (i) Provision of work-relevant experiences in a training environment integrated into the radiologist's working context; (ii) Up-to-date training cases that do not require cumbersome preparation because they are provided by routinely generated electronic medical records; (iii) Support of the way adults learn while remaining suitable for the patient- and problem-oriented nature of medicine. Future work will address unanswered questions to complete the implementation of the IRMAdiag trainer.</p