FLIP — Flexible learning in physics and mechanics

1996 ◽  
pp. 349-355
Author(s):  
I. Cohen ◽  
G. Edvinsson ◽  
R. Hsieh ◽  
C. Johannesson ◽  
K. E. Johansson ◽  
...  
Keyword(s):  
Flexible Learning

INDIVIDUALISATION IN FLEXIBLE LEARNING, FROM A POST-POST-FOUNDATIONALIST PERSPECTIVE

2015 ◽  
Vol 37 (2) ◽  
pp. 101-114
Author(s):  
J L Van der Walt
Keyword(s):  
Distance Learning ◽  
Blended Learning ◽  
Best Practice ◽  
Flexible Learning ◽  
Per Se

Most practitioners in the field of flexible learning seem to be sufficiently aware of the importance of catering to the needs of their students. However, it appears that many are rather more conscious of the needs of the students as a group than as individuals per se. Others seem to be rather more concerned about the technology involved. After touching on the foundationalist and non-, post- or anti-foundationalist approaches to the problem of individualisation in flexible learning, the article discusses a number of guidelines for individualisation from a post-post-foundationalist perspective. This is followed by a section in which these guidelines are presented in practical terms. This outline of guidelines reveals that attempting to individualise from this perspective is no simple and straightforward matter, but that there might be practitioners in the field of flexible learning (open distance learning and blended learning) who already are following this approach as a best practice. A post-post-foundationalist approach to individualisation in flexible learning offers practitioners in the field a whole new vocabulary.

scholarly journals Developing a flexible learning activity on biodiversity and spatial scale concepts using open‐access vegetation datasets from the National Ecological Observatory Network

2021 ◽  
Author(s):  
Diane M. Styers ◽  
Jennifer L. Schafer ◽  
Mary Beth Kolozsvary ◽  
Kristen M. Brubaker ◽  
Sara E. Scanga ◽  
...  
Keyword(s):  
Open Access ◽  
Spatial Scale ◽  
Learning Activity ◽  
Flexible Learning

The Flexible Learning Approach to Physics (FLAP)

1998 ◽  
Vol 18 (1-2) ◽  
pp. 115-125 ◽  
Author(s):  
M.H. Tinker ◽  
R.J.A. Lambourne
Keyword(s):  
Learning Approach ◽  
Flexible Learning

scholarly journals Flexible learning options for the degree MMedVet (Chir) : to the editor

2003 ◽  
Vol 74 (2) ◽  
Author(s):  
P. Bland-van den Berg ◽  
P. Stadler ◽  
P.H. Turner
Keyword(s):  
Flexible Learning ◽  
Learning Options

Flexible Learning in a Digital World

2002 ◽  
Vol 17 (3) ◽  
pp. 217-230 ◽  
Author(s):  
Betty Collis ◽  
Jef Moonen
Keyword(s):  
Flexible Learning ◽  
Digital World

ASSIST IN COOKING: ACTION SUPPORT SYSTEM FOR INTERACTIVE SELF-TRAINING

2008 ◽  
Vol 02 (02) ◽  
pp. 207-233
Author(s):  
SATORU MEGA ◽  
YOUNES FADIL ◽  
ARATA HORIE ◽  
KUNIAKI UEHARA
Keyword(s):  
Human Computer Interaction ◽  
Collaborative Filtering ◽  
Similarity Measure ◽  
Support System ◽  
Mixed Reality ◽  
Flexible Learning ◽  
Virtual Objects ◽  
Cooking Process ◽  
Real Objects ◽  
Transition Models

Human-computer interaction systems have been developed in recent years. These systems use multimedia techniques to create Mixed-Reality environments where users can train themselves. Although most of these systems rely strongly on interactivity with the users, taking into account users' states, they still lack the possibility of considering users preferences when they help them. In this paper, we introduce an Action Support System for Interactive Self-Training (ASSIST) in cooking. ASSIST focuses on recognizing users' cooking actions as well as real objects related to these actions to be able to provide them with accurate and useful assistance. Before the recognition and instruction processes, it takes users' cooking preferences and suggests one or more recipes that are likely to satisfy their preferences by collaborative filtering. When the cooking process starts, ASSIST recognizes users' hands movement using a similarity measure algorithm called AMSS. When the recognized cooking action is correct, ASSIST instructs the user on the next cooking procedure through virtual objects. When a cooking action is incorrect, the cause of its failure is analyzed and ASSIST provides the user with support information according to the cause to improve the user's incorrect cooking action. Furthermore, we construct parallel transition models from cooking recipes for more flexible instructions. This enables users to perform necessary cooking actions in any order they want, allowing more flexible learning.

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