Perceptible learning: monitoring motor learning by individual learning curves

Perceptible learning: monitoring motor learning by individual learning curves - Motor learning curves

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

NL-OMON

Registry ID

NL-OMON30961

Enrollment

Registered

2007-09-26

Start date

2007-09-01

Completion date

Unknown

Last updated

2024-05-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

gezonde controle groep normal healthy children

Interventions

None listed

Eligibility

Age

2 Years to 11 Years

Inclusion criteria

Inclusion criteria: Age: 4-8 years old- No known health problems, influencing motor performance

Exclusion criteria

Exclusion criteria: Health problems of influence on motor performance parents who are not able to guide their children adequately with their every day practicing no permission through Informed Consent

Design outcomes

Primary

Measure	Time frame
Scores for placing pegs (amount per minute), beanbag toss (amount of 25 correctly tossed) and long-jump (distance in centimeters) will be used to map the learning curves. The steepness and ceiling levels of the curves and the decrement in variability will be determined. Differences between pre- and post training measurements of the M-ABC will be compared. The factors possible of influence on the individual learning curve, such as personal factors (age, height, weight and gender), the baseline scores on the related M-ABC task and the amount of sport (questionnaire) will be determined. For each task a curve-fit procedure will be done to find the most appropriate model for norm-referenced learning curves using a statistical model that contains two phases: modeling from the reference values out of the norm population by state-space modeling (Standard Error of Measurement state-space model) and on the other site the optimal estimation from individual curves using a Kalman filter and smoother. To determine whether personal factors (age, gender, length and weight) or the baseline scores on de M-ABC related task have a relationship with the steepness and ceiling values of the learning curve, these factors will be used in the model to support the estimation. To determine possible transfer effects from learning the scores on the retention tests will be compared to the scores on the transfer tests.	—

Measure

Time frame

Scores for placing pegs (amount per minute), beanbag toss (amount of 25 correctly tossed) and long-jump (distance in centimeters) will be used to map the learning curves. The steepness and ceiling levels of the curves and the decrement in variability will be determined. Differences between pre- and post training measurements of the M-ABC will be compared. The factors possible of influence on the individual learning curve, such as personal factors (age, height, weight and gender), the baseline scores on the related M-ABC task and the amount of sport (questionnaire) will be determined. For each task a curve-fit procedure will be done to find the most appropriate model for norm-referenced learning curves using a statistical model that contains two phases: modeling from the reference values out of the norm population by state-space modeling (Standard Error of Measurement state-space model) and on the other site the optimal estimation from individual curves using a Kalman filter and smoother. To determine whether personal factors (age, gender, length and weight) or the baseline scores on de M-ABC related task have a relationship with the steepness and ceiling values of the learning curve, these factors will be used in the model to support the estimation. To determine possible transfer effects from learning the scores on the retention tests will be compared to the scores on the transfer tests.

—

Secondary

Measure	Time frame
not aplicable	—

Countries

Netherlands

Outcome results