Dataset Information

Computer Assisted Learning: Assessment of the Veterinary Virtual Anatomy Education Software IVALA™.

ABSTRACT: Although cadaveric dissection has historically been the cornerstone of anatomical education, it comes at the cost of some emotional, moral, safety, and environmental concerns. Computer assisted learning (CAL) programs are an increasingly common solution to these issues; however, research regarding the efficacy of high fidelity simulation is limited. The traditional first semester veterinary gross anatomy course curriculum at Ross University School of Veterinary Medicine (RUSVM) was supplemented with a web based virtual anatomy program, IVALA™ (www.ivalalearn.com). The purpose of this study was to assess the relationship between supplementary use of the IVALA™ program and student examination scores, and to measure student perception surrounding IVALA™. IVALA™ uses an interactive virtual canine specimen that enables students to identify, move, rotate, magnify, and remove individual anatomic structures while providing a text description of each selected anatomic point. Fifty-six first semester RUSVM students who supplemented their anatomic learning with the IVALA™ program performed significantly higher on examinations compared to students (n = 123) that did not (p = 0.003). Students’ overall perception toward IVALA™ was enjoyable (mean = 3.8 out of a 5-point Likert scale) and beneficial to their knowledge of anatomy (mean = 3.7); however, students did not support replacing cadaveric dissection with CAL (mean = 2.1). CAL can effectively supplement learning outcomes for veterinary anatomy.

SUBMITTER: Little WB

PROVIDER: S-EPMC6024603 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Computer Assisted Learning: Assessment of the Veterinary Virtual Anatomy Education Software IVALA™.

Little William Brady WB Artemiou Elpida E Conan Anne A Sparks Cathryn C

Veterinary sciences 20180619 2

Although cadaveric dissection has historically been the cornerstone of anatomical education, it comes at the cost of some emotional, moral, safety, and environmental concerns. Computer assisted learning (CAL) programs are an increasingly common solution to these issues; however, research regarding the efficacy of high fidelity simulation is limited. The traditional first semester veterinary gross anatomy course curriculum at Ross University School of Veterinary Medicine (RUSVM) was supplemented ...[more]

PMID: 29921803

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Project description:BackgroundOne of the largest challenges in chemistry today remains that of efficiently mining through vast amounts of data in order to elucidate the chemical structure for an unknown compound. The elucidated candidate compound must be fully consistent with the data and any other competing candidates efficiently eliminated without doubt by using additional data if necessary. It has become increasingly necessary to incorporate an in silico structure generation and verification tool to facilitate this elucidation process. An effective structure elucidation software technology aims to mimic the skills of a human in interpreting the complex nature of spectral data while producing a solution within a reasonable amount of time. This type of software is known as computer-assisted structure elucidation or CASE software. A systematic trial of the ACD/Structure Elucidator CASE software was conducted over an extended period of time by analysing a set of single and double-blind trials submitted by a global audience of scientists. The purpose of the blind trials was to reduce subjective bias. Double-blind trials comprised of data where the candidate compound was unknown to both the submitting scientist and the analyst. The level of expertise of the submitting scientist ranged from novice to expert structure elucidation specialists with experience in pharmaceutical, industrial, government and academic environments.ResultsBeginning in 2003, and for the following nine years, the algorithms and software technology contained within ACD/Structure Elucidator have been tested against 112 data sets; many of these were unique challenges. Of these challenges 9% were double-blind trials. The results of eighteen of the single-blind trials were investigated in detail and included problems of a diverse nature with many of the specific challenges associated with algorithmic structure elucidation such as deficiency in protons, structure symmetry, a large number of heteroatoms and poor quality spectral data.ConclusionWhen applied to a complex set of blind trials, ACD/Structure Elucidator was shown to be a very useful tool in advancing the computer's contribution to elucidating a candidate structure from a set of spectral data (NMR and MS) for an unknown. The synergistic interaction between humans and computers can be highly beneficial in terms of less biased approaches to elucidation as well as dramatic improvements in speed and throughput. In those cases where multiple candidate structures exist, ACD/Structure Elucidator is equipped to validate the correct structure and eliminate inconsistent candidates. Full elucidation can generally be performed in less than two hours; this includes the average spectral data processing time and data input.

Project description:Strong understanding of cardiac anatomy and function are essential components of veterinary medical education; however, the heart is considered challenging to comprehend due to its complexity. This study introduced and assessed a new learning resource, the IVALA® augmented reality (AR) heart program in a cohort of pre-veterinary students. Students were randomly divided into traditional textbook learning and AR learning groups. All students underwent a pre- and post-intervention testing assessing baseline cardiac anatomy knowledge, as well as pre-intervention evaluation of inherent spatial awareness. Teaching and learning included a 60-min cadaveric learning experience guided by either traditional learning resources or the IVALA® program. All students completed a participant survey about their learning experiences. Seventy-four students (36 in the control, and 38 in the IVALA® group) participated in the research. Overall, students improved in cardiac knowledge by an average of 24.5% after intervention regardless of study methodology. No significant difference in post-test improvement was noted between the two groups. On a 20-question assessment, students in the IVALA® group improved by an average of 4.9 questions correct over their pre-intervention test, and the control group improved by an average of 4.8 questions (p = 0.9). A positive correlation was found between spatial awareness scores and post-test improvement regardless of cohort group (p = 0.03). Sixty-two individuals (83.8%) completing the participant survey reported an overwhelming preference for learning with AR compared to traditional methods. This study illustrates student preference of IVALA® program in learning anatomy of the heart and supports its use is as effective as traditional methods of teaching with the benefit of increased enthusiasm and engagement.Supplementary informationThe online version contains supplementary material available at 10.1007/s40670-021-01260-8.

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Project description:BACKGROUND:The widening gap between innovations in the medical field and the dissemination of such information to doctors may affect the quality of care. Offline computer-based digital education (OCDE) may be a potential solution to overcoming the geographical, financial, and temporal obstacles faced by doctors. OBJECTIVE:The objectives of this systematic review were to evaluate the effectiveness of OCDE compared with face-to-face learning, no intervention, or other types of digital learning for improving medical doctors' knowledge, cognitive skills, and patient-related outcomes. Secondary objectives were to assess the cost-effectiveness (CE) of OCDE and any adverse effects. METHODS:We searched major bibliographic databases from 1990 to August 2017 to identify relevant articles and followed the Cochrane methodology for systematic reviews of intervention. RESULTS:Overall, 27 randomized controlled trials (RCTs), 1 cluster RCT (cRCT), and 1 quasi-RCT were included in this review. The total number of participants was 1690 in addition to the cRCT, which included 24 practices. Due to the heterogeneity of the participants, interventions, and outcomes, meta-analysis was not feasible, and the results were presented as narrative summary. Compared with face-to-face learning, the effect of OCDE on knowledge gain is uncertain (ratio of the means [RM] range 0.95-1.17; 8 studies, 495 participants; very low grade of evidence). From the same comparison, the effect of OCDE on cognitive skill gain is uncertain (RM range 0.1-0.9; 8 studies, 375 participants; very low grade of evidence). OCDE may have little or no effect on patients' outcome compared with face-to-face education (2 studies, 62 participants; low grade of evidence). Compared with no intervention, OCDE may improve knowledge gain (RM range 1.36-0.98; 4 studies, 401 participants; low grade of evidence). From the same comparison, the effect of OCDE on cognitive skill gain is uncertain (RM range 1.1-1.15; 4 trials, 495 participants; very low grade of evidence). One cRCT, involving 24 practices, investigated patients' outcome in this comparison and showed no difference between the 2 groups with low-grade evidence. Compared with text-based learning, the effect of OCDE on cognitive skills gain is uncertain (RM range 0.91-1.46; 3 trials with 4 interventions; 68 participants; very low-grade evidence). No study in this comparison investigated knowledge gain or patients' outcomes. One study assessed the CE and showed that OCDE was cost-effective when compared with face-to-face learning if the cost is less than or equal to Can $200. No trial evaluated the adverse effect of OCDE. CONCLUSIONS:The effect of OCDE compared with other methods of education on medical doctors' knowledge and cognitive skill gain is uncertain. OCDE may improve doctors' knowledge compared with no intervention but its effect on doctors' cognitive skills is uncertain. OCDE may have little or no effect in improving patients' outcome.

Dataset Information

Computer Assisted Learning: Assessment of the Veterinary Virtual Anatomy Education Software IVALA™.

Publications

Computer Assisted Learning: Assessment of the Veterinary Virtual Anatomy Education Software IVALA™.

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