Affective Learning: Principles, Technologies, Practice

2017 ◽  
pp. 1-13 ◽  
Author(s):  
Panagiotis D. Bamidis
Keyword(s):  
Affective Learning ◽  
Learning Principles

'Those Twins of Learning'

2019 ◽  
Vol 31 (4) ◽  
pp. 54-64
Author(s):  
Sheila T. Cavanagh ◽  
Steve Rowland
Keyword(s):  
Writing Skills ◽  
Correctional Facility ◽  
Affective Learning ◽  
Washington State ◽  
Emory University ◽  
The Subject ◽  
Learning Principles

Sheila T. Cavanagh and Steve Rowland link Shakespeare classrooms in distinctive venues: Cavanagh is a professor at Emory University in Atlanta, whose students are enrolled in undergraduate degree programmes; Rowland teaches at Monroe Correctional Facility in Washington State, under the auspices of University Beyond Bars. This article describes some of the practical and theoretical challenges emerging through this collaboration, many of which result from the instructors’ desires to construct their classes with pedagogic goals and assignments drawn from both cognitive and affective learning principles. Geography precludes the students meeting in person and they are not currently able to employ videoconferencing in this endeavour, but regular exchanges of essays and responses to each other’s writing allows these disparate groups of Shakespeareans to expand their knowledge of the drama while sharpening their critical and writing skills and learning to develop their affective understandings of the subject.

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

Hearing Vocation Differently

2018 ◽  
Keyword(s):  
Undergraduate Students ◽  
Affective Learning ◽  
College Campuses ◽  
Deep Roots ◽  
Academic Field ◽  
Multiple Voices ◽  
Faith Traditions ◽  
Future Direction ◽  
The Relationship

Colleges and universities have begun using the language of vocation and calling to help undergraduates think about the future direction of their lives. This language has been employed in both secular and religious contexts, but it has deep roots in a specific theological tradition. Given the increasingly multi-faith context of undergraduate life, many have asked whether this originally Christian terminology can truly become a new vocabulary for higher education. This volume’s 13 contributing scholars identify with a wide variety of faith traditions, including Buddhism, Christianity, Hinduism, Islam, Judaism, and Sikhism. Some claim more than one tradition; others would claim none. Rather than seeking to “translate” Christian language into other perspectives, they reflect on various facets of vocation from the standpoint of their own traditions. Both individually and collectively, they seek to expand the range of vocational reflection and discernment well beyond its traditional Christian origins, addressing themes such as religious pluralism and difference, the importance of multiple voices, the role of affective learning, the relationship between process and result, and the development of an integrated life. The authors recognize that all undergraduate students—regardless of their academic field, religious background, or demographic identity—need to make space for reflection, to overcome obstacles to vocational discernment, and to consider the significance of their own narratives, beliefs, and practices. Accomplishing these goals will require college campuses to reimagine their curricular and co-curricular programming in order to support their students’ interfaith reflections on issues of meaning and purpose, as well as personal identity.

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.

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