scholarly journals Assessing Transfer of Learning in Problem Solving from the Perspective of Preparation for Future Learning

2014 ◽  
Author(s):  
Dehui Hu ◽  
Joshua Von Korff ◽  
N. Sanjay Rebello
Keyword(s):  
Problem Solving ◽  
Transfer Of Learning ◽  
Preparation For Future Learning ◽  
Future Learning

scholarly journals Responsive Teaching Toward Responsive Teachers: Mediating Transfer through Intentionality, Enactment, and Articulation

2005 ◽  
Vol 37 (4) ◽  
pp. 459-492 ◽  
Author(s):  
Cheryl L. Dozier ◽  
Ilene Rutten
Keyword(s):  
Transfer Of Learning ◽  
Teacher Educators ◽  
Theoretical Foundation ◽  
Data Sources ◽  
Primary Data ◽  
Responsive Teaching ◽  
Instructional Tools ◽  
Preparation For Future Learning ◽  
Intensive Teaching ◽  
Future Learning

Using a framework for responsive teaching as the theoretical foundation, this study examines ways teacher educators mediate teachers' transfer of responsive teaching. Guided by Bransford & Schwartz (1999) in defining transfer as preparation for future learning, our research explored teachers' transfer of learning as the teachers developed self-extending systems (Clay, 1991, 2001) as responsive literacy specialists. The intensive teaching of one student and the accompanying instructional tools used in the Literacy Lab fostered the development of responsive teaching in a variety of ways. Primary data sources included videotaped lessons, community conversations, and reflective essays. Analysis of the data revealed three elements that mediated transfer: intentionality, enactment, and articulation. Since transfer of responsive teaching does not happen automatically for all teachers, this study discusses the centrality of intentionally creating a range of spaces to enact and articulate transfer.

scholarly journals John Senior and Éva Gyarmathy: AI and Developing Human Intelligence, Future Learning and Educational Innovation

2021 ◽  
Vol 28 (3) ◽  
pp. 442-446
Author(s):  
Valentin Kuleto ◽  
Milena Ilić
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Problem Solving ◽  
Computer Science ◽  
Image Recognition ◽  
Language Processing ◽  
Educational Innovation ◽  
Human Intelligence ◽  
Intelligent Machines ◽  
Future Learning

AI is a branch of computer science that emphasises the development of intelligent machines that think and work like humans. Examples of AI applications are speech recognition, natural language processing, image recognition etc. The term ML represents the application of AI to enable systems’ ability to learn and improve based on experience, without the explicit need for programming, using various problem-solving algorithms. For example, in machine learning, computers learn based on the data they process, not program instructions

scholarly journals Transfer for Future Learning of Fractions within Cignition’s Microtutoring Approach

2020 ◽  
Author(s):  
Jeremy Roschelle ◽  
Britte Haugan Cheng ◽  
Nicola Hodkowski ◽  
Lina Haldar ◽  
Julie Neisler
Keyword(s):  
Transfer Of Learning ◽  
Exploratory Research ◽  
Research Project ◽  
Mathematics Understanding ◽  
Future Learning ◽  
Measurement Approach ◽  
Assessment Research

In this exploratory research project, our team’s goal was to design and begin validation of a measurement approach that could provide indication of a student’s ability to transfer their mathematics understanding to future, more advanced mathematical topics. Assessing transfer of learning in mathematics and other topics is an enduring challenge. We sought to invent and validate an approach to transfer that would be relevant to improving Cignition’s product, would leverage Cignition’s use of online 1:1 tutoring, and would pioneer an approach that would contribute more broadly to assessment research.

Preparation for Future Teaching

2017 ◽  
pp. 293-305 ◽  
Author(s):  
Michael P. Downton
Keyword(s):  
Teaching Assistant ◽  
Teacher Education Programs ◽  
Learning And Development ◽  
Education Programs ◽  
Preparation For Future Learning ◽  
Development Course ◽  
Pre Service Teacher ◽  
Future Learning ◽  
University Setting

Teacher education programs are tasked with a myriad of responsibilities to help improve the quality of future teachers. Despite the constraints of using non-licensed teachers in a public classroom setting in order for the pre-service teacher to gain experience, a Teaching Assistant (TA) program has been implemented in a university setting and thus removing the logistical obstacles pre-service teachers face. Using a preparation for future learning model, pre-service teachers enrolled in a human learning and development course engaged in activities that ranged from mentoring students, developing grading rubrics, and grading student assignments. Triangulation of qualitative data showcases the importance of assessment (i.e., feedback) and the pre-service teacher's identity as emergent themes. Discussion of possible design iterations to the TA program and its potential impact are discussed.

PREPARATION FOR FUTURE LEARNING:

2015 ◽  
pp. 223-238
Author(s):  
Karen Swan ◽  
Phil Vahey ◽  
Ken Rafanan ◽  
Tina Stanford ◽  
Louise Yarnall ◽  
...  
Keyword(s):  
Preparation For Future Learning ◽  
Future Learning

Preparation for future learning: a missing competency in health professions education?

2015 ◽  
Vol 50 (1) ◽  
pp. 115-123 ◽  
Author(s):  
Maria Mylopoulos ◽  
Ryan Brydges ◽  
Nicole N Woods ◽  
Julian Manzone ◽  
Daniel L Schwartz
Keyword(s):  
Health Professions ◽  
Health Professions Education ◽  
Preparation For Future Learning ◽  
Future Learning

Development of concept-based physiology lessons for biomedical engineering undergraduate students

2013 ◽  
Vol 37 (2) ◽  
pp. 176-183 ◽  
Author(s):  
Regina K. Nelson ◽  
Naomi C. Chesler ◽  
Kevin T. Strang
Keyword(s):  
Undergraduate Students ◽  
Biomedical Engineering ◽  
Engineering Students ◽  
Organ System ◽  
Instructional Model ◽  
Engineering Curriculum ◽  
Engineering Knowledge ◽  
Undergraduate Engineering ◽  
Preparation For Future Learning ◽  
Future Learning

Physiology is a core requirement in the undergraduate biomedical engineering curriculum. In one or two introductory physiology courses, engineering students must learn physiology sufficiently to support learning in their subsequent engineering courses and careers. As preparation for future learning, physiology instruction centered on concepts may help engineering students to further develop their physiology and biomedical engineering knowledge. Following the Backward Design instructional model, a series of seven concept-based lessons was developed for undergraduate engineering students. These online lessons were created as prerequisite physiology training to prepare students to engage in a collaborative engineering challenge activity. This work is presented as an example of how to convert standard, organ system-based physiology content into concept-based content lessons.

Sign in / Sign up

Export Citation Format

Share Document