Learning Principles and the Secure Programming Clinic

2019 ◽  
pp. 16-29
Author(s):  
Matt Bishop ◽  
Melissa Dark ◽  
Lynn Futcher ◽  
Johan Van Niekerk ◽  
Ida Ngambeki ◽  
...  
Keyword(s):  
Secure Programming ◽  
Learning Principles

A Response Evocation Program for /ɝ/

1975 ◽  
Vol 40 (1) ◽  
pp. 92-105 ◽  
Author(s):  
Lawrence D. Shriberg
Keyword(s):  
Service Delivery ◽  
Motor Skills ◽  
Task Analysis ◽  
Professional Issues ◽  
Clinical Experiences ◽  
Program Outcomes ◽  
Program Step ◽  
Skills Learning ◽  
Learning Principles

A response evocation program, some principles underlying its development and administration, and a review of some clinical experiences with the program are presented. Sixty-five children with developmental articulation errors of the /ɝ/ phoneme were administered the program by one of 19 clinicians. Approximately 70% of program administrations resulted in a child emitting a good /ɝ/ within six minutes. Approximately 10% of children who were given additional training on program step failures emitted good /ɝ/'s in subsequent sessions. These preliminary observations are discussed in relation to the role of task analysis and motor skills learning principles in response evocation, clinician influences in program outcomes, and professional issues in service delivery to children with developmental articulation errors.

Learning Principles and the Real World: A Difficult Union

2001 ◽  
Vol 46 (1) ◽  
pp. 76-77
Author(s):  
Donald F. Dansereau ◽  
Sandra M. Dees
Keyword(s):  
Real World ◽  
The Real ◽  
Learning Principles

Learning Principles in the Design of Internet-Based Statistics Teaching Tools

2006 ◽  
Author(s):  
Amanda T. Saw ◽  
Dale E. Berger ◽  
Chris L. Aberson ◽  
Michael R. Healy ◽  
Giovanni Sosa
Keyword(s):  
Teaching Tools ◽  
Learning Principles

Structural control using connectionist learning principles

1992 ◽  
Author(s):  
L. MONTGOMERY ◽  
K. KRISHNAKUMAR ◽  
G. WEEKS
Keyword(s):  
Structural Control ◽  
Learning Principles ◽  
Connectionist Learning

scholarly journals The future is coming: promising perspectives regarding the use of machine learning in renal transplantation

2019 ◽  
Vol 41 (2) ◽  
pp. 284-287
Author(s):  
Pedro Guilherme Coelho Hannun ◽  
Luis Gustavo Modelli de Andrade
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Graft Function ◽  
Statistical Technique ◽  
Daily Routine ◽  
Learning Approaches ◽  
Post Transplantation ◽  
Chronic Allograft Rejection ◽  
Institutional Experience ◽  
Learning Principles

Abstract Introduction: The prediction of post transplantation outcomes is clinically important and involves several problems. The current prediction models based on standard statistics are very complex, difficult to validate and do not provide accurate prediction. Machine learning, a statistical technique that allows the computer to make future predictions using previous experiences, is beginning to be used in order to solve these issues. In the field of kidney transplantation, computational forecasting use has been reported in prediction of chronic allograft rejection, delayed graft function, and graft survival. This paper describes machine learning principles and steps to make a prediction and performs a brief analysis of the most recent applications of its application in literature. Discussion: There is compelling evidence that machine learning approaches based on donor and recipient data are better in providing improved prognosis of graft outcomes than traditional analysis. The immediate expectations that emerge from this new prediction modelling technique are that it will generate better clinical decisions based on dynamic and local practice data and optimize organ allocation as well as post transplantation care management. Despite the promising results, there is no substantial number of studies yet to determine feasibility of its application in a clinical setting. Conclusion: The way we deal with storage data in electronic health records will radically change in the coming years and machine learning will be part of clinical daily routine, whether to predict clinical outcomes or suggest diagnosis based on institutional experience.

scholarly journals Adult Learning Principles and Peer Delivery Improve Satisfaction of Electronic Medical Record Onboarding Education

ACI Open ◽  
2020 ◽  
Vol 04 (02) ◽  
pp. e114-e118
Author(s):  
Joanna Lawrence ◽  
Sharman Tan Tanny ◽  
Victoria Heaton ◽  
Lauren Andrew
Keyword(s):  
Adult Learning ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Junior Doctors ◽  
Free Text ◽  
Organizational Safety ◽  
Adult Learning Principles ◽  
Learning Practices ◽  
Learning Principles ◽  
The Impact

Abstract Objectives Given the importance of onboarding education in ensuring the safety and efficiency of medical users in the electronic medical record (EMR), we re-designed our EMR curriculum to incorporate adult learning principles, informed and delivered by peers. We aimed to evaluate the impact of these changes based on their satisfaction with the training. Methods A single site pre- and post-observational study measured satisfaction scores (four questions) from junior doctors attending EMR onboarding education in 2018 (pre-implementation) compared with 2019 (post-implementation). An additional four questions were asked in the post-implementation survey. All questions employed a Likert scale (1–5) with an opportunity for free-text. Raw data were used to calculate averages, standard deviations and the student t-test was used to compare the two cohorts where applicable. Results There were a total of 98 respondents in 2018 (pre-implementation) and 119 in 2019 (post-implementation). Satisfaction increased from 3.8/5 to 4.5/5 (p < 0.0001) following implementation of a peer-delivered curriculum in line with adult learning practices. The highest-rated factors were being taught by other doctors (4.9/5) and doctors having the appropriate knowledge to deliver training (4.9/5). Ninety-two percent of junior doctors were motivated to engage in further EMR education and 90% felt classroom support was adequate. Conclusion EMR onboarding education for medical users is a critical ingredient to organizational safety and efficiency. An improvement in satisfaction ratings by junior doctors was demonstrated after significant re-design of the curriculum was informed and delivered by peers, in line with adult learning principles.

scholarly journals Motor learning-based activities may improve functional ability in adults with severe cerebral palsy: A controlled pilot study

2021 ◽  
pp. 1-11
Author(s):  
Helle Hüche Larsen ◽  
Rasmus Feld Frisk ◽  
Maria Willerslev-Olsen ◽  
Jens Bo Nielsen
Keyword(s):  
Cerebral Palsy ◽  
Motor Learning ◽  
Motor Function ◽  
Functional Ability ◽  
Active Group ◽  
Control Group ◽  
Gross Motor ◽  
Gross Motor Function ◽  
Learning Principles

BACKGROUND: Cerebral palsy (CP) is a neurodevelopmental disturbance characterized by impaired control of movement. Function often decreases and 15% of adults are classified as severely affected (Gross Motor Function Classification Scale III-V). Little is known about interventions that aim to improve functional abilities in this population. OBJECTIVE: To evaluate a 12-week intervention based on motor learning principles on functional ability in adults with severe CP. METHODS: 16 adults (36±10 years, GMFCS III-V) were enrolled and divided into an intervention group (Active group) and a standard care group (Control group). Primary outcome measure was Gross Motor Function Measure (GMFM-88). Secondary measures were neurological status. The Active group were measured at baseline, after the intervention and at one-month follow-up. The Control group were measured at baseline and after one month. RESULTS: Analysis showed statistically significant improvement in GMFM-88 for the Active group from baseline to post assessment compared with the Control group (group difference: 5 points, SE 14.5, p = 0.008, CI: 1.2 to 8.7). Improvements were maintained at follow-up. Results from the neurological screening showed no clear tendencies. CONCLUSIONS: The study provides support that activities based on motor learning principles may improve gross motor function in adults with severe CP.

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