scholarly journals Financial competence as a key competence in lifelong learning. A new framework

2021 ◽  
Vol 9 (2) ◽  
pp. 155-183
Author(s):  
Olav Aarna ◽  
Leonore Riitsalu ◽  
Urve Venesaar
Keyword(s):  
Lifelong Learning ◽  
New Framework

Inimestelt oodatakse varasemast suurema vastutuse võtmist oma rahalise heaolu eest kogu elukaare jooksul ja keerukate valikute langetamist mitmesuguste finantsteenuste hulgast. See nõuab elukestvat finantspädevuse arendamist. Eestis tegeletakse sellega aga eelkõige üldhariduskoolis ning sealgi on see teiste ainevaldkondade osa ja selgelt defineerimata. Finantspädevust kajastab vaid ettevõtlusõppe raamistiku finantskirjaoskuse alapädevus. Elukestva õppe jaoks on suuniseid antud riiklikus rahatarkuse strateegias, kuid sealgi pole finantspädevust defineeritud. Et seda lünka täita ja võimaldada süstemaatilisemat finantspädevuse edendamist, loome finantspädevuse kui elukestva õppe võtmepädevuse raamistiku. Selleks analüüsime teaduskirjandust, teiste riikide määratlusi ja OECD suuniseid. Finantspädevuse täiuslikkuse tasanditena käsitleme rahalise heaolu komponente. Tulemused võiksid kasutust leida poliitikakujundajate ja finantshariduse pakkujate töös ning anda ainest edasisteks uurimusteks.  Summary

scholarly journals Learning with Selective Forgetting

2021 ◽  
Author(s):  
Takashi Shibata ◽  
Go Irie ◽  
Daiki Ikami ◽  
Yu Mitsuzumi
Keyword(s):  
Lifelong Learning ◽  
Original Data ◽  
Ethical Considerations ◽  
The Past ◽  
Synthetic Signal ◽  
Training Samples ◽  
Benchmark Datasets ◽  
Privacy Issues ◽  
New Framework

Lifelong learning aims to train a highly expressive model for a new task while retaining all knowledge for previous tasks. However, many practical scenarios do not always require the system to remember all of the past knowledge. Instead, ethical considerations call for selective and proactive forgetting of undesirable knowledge in order to prevent privacy issues and data leakage. In this paper, we propose a new framework for lifelong learning, called Learning with Selective Forgetting, which is to update a model for the new task with forgetting only the selected classes of the previous tasks while maintaining the rest. The key is to introduce a class-specific synthetic signal called mnemonic code. The codes are "watermarked" on all the training samples of the corresponding classes when the model is updated for a new task. This enables us to forget arbitrary classes later by only using the mnemonic codes without using the original data. Experiments on common benchmark datasets demonstrate the remarkable superiority of the proposed method over several existing methods.

Is It Time to Let Successful Aging Die? A New Framework for Understanding Human Development

2019 ◽  
Author(s):  
Lucas J. Hamilton ◽  
Michael T. Vale ◽  
Michelle L. Hughes ◽  
Paige M. Pasta ◽  
Katherine Judge
Keyword(s):  
Human Development ◽  
Successful Aging ◽  
New Framework
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