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Bruno D’HulsterAlgemene coördinatie

Jo StubbeDigitaal platform

Debbie MejorAdministratie,

communicatie en begeleiding

Michael Van LieshoutProjecten en promotie

Elise Van der StichelenPraktijksessies,

begeleiding

De VZW SportaMundi brengt sportwetenchappen in de praktijk naar scholen, gemeenten, sportfederaties en-clubs

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Infosessie SportKompas

1. Wetenschappelijke basis2. I LIKE3. I DO4. I AM5. SportKompas Organiseren6. Aan de slag met SportKompas in je school7. Aan de slag met SportKompas in je gemeente

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SportKompasWetenschappelijke basis

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Een wetenschappelijk instrument voor detectie en oriëntatie

doelgroepkinderen 8 - 10 jaar

Wat is SportKompas?

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SportKompas & SportTalent

Sport oriëntatie tool kinderen 8 - 10 jaar

Basisscholen - Gemeenten

Sportspecifieke talentidentificatie toolkinderen en adolescenten

Sportclubs - Federaties

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2007 Ghent youth

HandballprojectGHYP

Historiek (projecten)

1996Ghent Youth

Soccer Project GYSP

2010UgentSpin-in

Activiteiten

2008Sportakus

2007 Vlaams

Sportkompas

2017SportKompas

Productontwikkeling

Samenwerking HAN NederlandNYSI Singapore

2019Implementatie

Vlaanderen

Andere landenI DO I LIKE I AM

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Historiek (onderzoek)

R. Vaeyens H. Mohammed B. Vandorpe S. Matthys J. Vandendriessche J. Fransen D. Deprez J. Pion

2007

2009

2011

2012

2013

2015

2019

2019 R. Norjali Wazir

2021 M. Mostaert

2022 F. Laureys

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Literatuurstudie - Karakteristieken van de sport

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Experten Bevragen

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Badminton

Basketbal

Gymnastiek

Judo

Schermen

Taekwondo

Volleybal

KLEIN GROOT

Voetbal

Tafeltennis

Quotiënt

Karakteristieken meten

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Meten en evalueren

Gestalte 135 cm

Minder'goed UitstekendMinder'goed Uitstekend Minder'goed Uitstekend Minder'goed Uitstekend

Minder'goed UitstekendMinder'goed Uitstekend Minder'goed Uitstekend Minder'goed Uitstekend

8993 94

8779 109 90

Jongen 10j

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Verspringen uit stand 147 cm

Minder'goed Uitstekend

Minder'goed Uitstekend

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70

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D.A. correctly classified

Badminton 96% - 83%

Basketball 91% - 80%

Gymnastics 98% - 92%

Handball 84% - 46%

Judo 98% - 71%

Soccer 93% - 86%

Table tennis 96% - 81%

Triathlon 90% - 82%

Volleyball 94% - 92%

Stat

ure

Sitt

ing

Heig

ht

Wei

ght

Fat%

BMI

Shou

lder

rota

tion

Sita

ndre

ach

Coun

ter m

ovem

entj

up

Shut

tle ru

n 1

0 x

5m

Sprin

t 5m

Sprin

t 30m

Situ

ps

Knee

Push

ups

Stan

ding

bro

adju

mp

Endu

ranc

esh

uttle

run

KTK

BB

KTK

JS

KTK

MS

Drib

ble

run

Drib

ble

Hand

s

Drib

ble

Feet

Thro

win

gsh

uttle

s

Generic anthropometric and performance characteristics among elite adolescent boys in nine different sports Johan Pion, Veerle Segers, Job Fransen, Gijs Debuyck, Dieter Deprez, Leen Haerens, Roel Vaeyens,

Renaat Philippaerts and Matthieu Lenoir European Journal of Sports Sciences (2014)

Testpakketen voor Oriënteren en Talent

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Breed ontwikkelen tot 12 jaar !

Breed ontwikkelen of specialiseren ?

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Instrumenten van het SportKompas

14 fysieke oefeningen om de sportente ontdekken die het best bij een

kind passen

Een interactieve webapplicatie waarmee kinderen kunnen

ontdekken welke sporten ze leuk vinden

Een vragenlijst om de intrinsieke motivatie van een kind en de

haalbaarheid voor een sport te toetsen

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Testbatterij (I DO)

AntropometrieLichaamslengteLichaamsgewicht

LenigheidZittend reikenSchouder flexibiliteitKrachtStaande vertesprongKnie push upsCurl-ups

CoördinatieAchterwaarts balancerenZijwaarts springenZijwaarts verplaatsenOog-Hand CoördinatieShuttle werpen

UithoudingShuttle Run

SnelheidShuttle Run (10x5m)

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Wat vinden kinderen leuk?

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On-line vragenlijst (40 vragen) in de klas:

• Peilen naar de sportparticipatie• Mogelijke barrières om te sporten• Mate van motivatie

Hoe gemotiveerd zijn kinderen om te sporten?

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Oriënteren

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Implementatie van een duurzame sportcultuur …

Beweegniveauherkennen

Oriënterenen faciliteren

Bewegen verderontwikkelen

Bewegingenverkennen

Plezierbeleving enontwikkeling

4-7 jaar

8-10 jaar

10 - … jaar

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… met verschillende actoren …

Beweegniveauherkennen

Oriënterenen faciliteren

Bewegen verderontwikkelen

Bewegingenverkennen

Plezierbeleving enontwikkelingSchool

Gemeente

Sportvereniging

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… ondersteund door een digitaal platform

Beweegniveauherkennen

Oriënterenen faciliteren

Bewegen verderontwikkelen

Bewegingenverkennen

Plezierbeleving enontwikkeling

Gerichte

Instroom

Digitaal volgsysteem

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Multimove

SportKompas

Bouwstenen voor een duurzame sportcultuur

Talent Ontwikkeling

Talent Selectie

Talent Identificatie

Talent Ontwikkeling

Talent Selectie

Talent Identificatie

Talent Ontwikkeling

Talent Selectie

Talent IdentificatieSport 1 Sport 2 Sport 3

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SportKompas & SportTalent

Sport orientatie tool kinderen 8 - 10 jaar

Basisscholen - Gemeenten

Sportspecifieke talentidentificatie toolkinderen en adolescenten

Sportclubs - Federaties

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Talent identificeren

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Invloed van maturiteit

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Positie-specifieke karakteristiekenPositions in elite basketball based on performance characteristics

97,3%correctlyclassified 99,9%

correctlyclassified

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Talent transfer

Transfer van sporten

Breed ontwikkelen in termen van karakteristieken

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Data Science

Data science: verbeteren algoritmes (Artificiële Intelligentie)

Kohonen Feature Maps

ceding layer, or even to themselves are termed recurrent networks.Feed forward networks can be seen as a particular case of recurrentnetwork. A schematic representation of a multilayer feed forwardneural network is shown in Fig. 1. Multilayer perceptrons are oftentrained using an algorithm known as error back-propagation(Rumelhart, Hinton, & Williams, 1986) and this has become so pop-ular that it is often referred to as a back-propagation network. Thisuses a gradient descent algorithm, which distributes the global er-ror over the various neurons as a ‘local error’ and updates theweights. Weights are changed according to the size and directionof negative gradient on the error surface, until the desired and ac-tual outputs converge and the network is said to have ‘learnt’ torepresent the function relating input and output. The error back-propagation algorithm has been a significant improvement in neu-ral network research, but there has always been strong interest inthe research for new and improved training methods. Modifica-tions of error back-propagation include error back-propagationwith adaptive learning rate and momentum, QuickProp and conju-gate gradient method. All these algorithms can be considered asvariations of the steepest descent method, because they only useinformation of the objective function and its gradient. It is possible,however, to estimate the Hessian matrix of the error function byusing only the values of the first derivatives of the network outputwith respect to the weights and, with this information, obtain bet-ter values for the variation of the network weights at each learningcycle. This is one of the observations that led to the development ofthe Levenberg–Marquardt method (Bishop, 1995; Levenberg,1944; Marquardt, 1963; Wilamowski, Iplikci, Kaynak, & Efe,2001). It is an advanced nonlinear optimization algorithm designedto minimize the sum-of-squares error function but its usage is re-stricted as it can only be used on networks with a single outputunit. The memory requirements are proportional to the square ofthe number of weights in the network and this precludes its usein networks of very large size. However, this algorithm is much fas-ter and has become increasingly popular within the neural net-works community.

3. Data analysis

A number of aspects are taken into consideration while makingadmission decisions in any academic program. Some commonadmission criteria considered generally in business schools areoverall undergraduate grade point average, admissions test score,work experience, age, sex, references, group discussion and per-sonal interview etc. In this study, we have collected data on variouscriteria from an Indian business school. Data was available for 5consecutive batches of management students graduated from thisbusiness school resulting in 244 records pertaining to 244 stu-dents. Information that was available for our study were under-graduate academic results (AP), test score (TS), group discussion

and interview (GDI) score, work experience (WE), cumulative per-formance index (CPI) and grades of 24 core subjects taken duringthe entire program. CPI refers to the numerical score obtained byconverting the grades to a 10 point scale using the Institute’s con-version system.

The problem of prediction of academic performance (CPI) withAP, TS, GDI and WE as predictors is carried out using regressionand neural networks. This facilitates comparison of these twotechniques by considering academic performance on continuousscale. In the next step, the academic performance is consideredon a categorical scale where students are classified into two cat-egories depending on their academic performance. Students hav-ing CPI greater than 7.5 on a scale of 10 are classified as‘‘Successful” resulting in 94 students belonging to this category.As a cutoff criterion exists for students to qualify the manage-ment program of this school, students having CPI between thiscutoff and a CPI value of 7.5 are classified as ‘‘Marginal” category.A total of 150 students are classified in this category of marginalstudents. The data points below the cutoff CPI were not completeas the students had to leave the program at different stages dueto this cutoff criterion and hence such records were not consid-ered in this study. A comparative study among logistic regression,discriminant analysis and neural networks is carried out for thisclassification problem. The robustness of each of these techniqueswith respect to sampling fluctuations is examined using 5-foldcross-validation method (Efron & Tibshirani, 1993). In cross-vali-dation, the original data is split into five mutually exclusive sub-sets of nearly equal size where four sub-samples are used formodeling and the remaining one is used for validation purpose.This process is repeated five times and the average of five runsis used in estimating generalization error to validate the perfor-mance of these techniques. Further, factor analysis is carriedout with the objective of identifying the underlying constructsin a traditional business school curriculum and the relevance ofthese constructs with various components of admission processare presented. The entire analysis was done using SAS 9.1 soft-ware package and Enterprise Miner Utility of this software wasused to carry out neural network analysis. The results obtainedare presented in subsequent subsections.

3.1. Comparative analysis

The results from all the models namely, neural networks,regression analysis, logistic regression and discriminant analysisand a comparison across all the models are presented in thissection.

3.1.1. Regression analysisRegression analysis has been carried out by considering CPI as

the dependent variable and AP, TS. GDI and WE as independentvariables and the results are presented here. The estimated regres-sion model is

CPI ¼ 4:21 þ 0:038 APð

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De toekomst

SportKompas CommunityWebsite voor de ouders

Matching met het sportaanbodAangepaste beweegprogramma’s

Link met de eindtermen LOAutomatiseren van de tesbatterijVerfijningen vanuit data analyses

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