Research master thesis | Developmental Psychopathology in Education and Child Studies (research) (MSc)
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Prediction-based and repetition-based learning are two learning strategies, differing most profoundly in their depth of processing. In repetition-based learning, students repeat information shortly...Show morePrediction-based and repetition-based learning are two learning strategies, differing most profoundly in their depth of processing. In repetition-based learning, students repeat information shortly after learning it, while in prediction-based learning, students make a prediction before learning the information. This study aims to compare the effectiveness of the two learning strategies for memory recall, as well as consider the influence of age, the magnitude of the prediction errors, and the involved brain areas. It seeks to enhance the educational debate on these learning strategies by uncovering the strategies' mechanisms and guiding educators on their effective use. To accomplish this, 28 young adolescents and 46 young adults were scanned in an MRI scanner while learning numerical facts using both strategies: predicting and repeating. The study explored the influence of strategy, age group, and prediction error on memory recall. Furthermore, it investigated strategy-specific and age-specific differences in the medial temporal lobe (MTL), striatum, anterior cingulate cortex (ACC), and dorsolateral prefrontal cortex (DLPFC). While it was expected that prediction-based learning would result in better learning for both age groups as it requires deeper processing, results showed that adults had improved memory for repetition compared to prediction, whereas adolescents did not show a significant difference between the two learning strategies. Within prediction-based learning, adults showed increased memory for small and large prediction errors, while adolescents only did for large prediction errors. Lastly, among the investigated brain areas, the ACC, which is involved in error detection, showed the most prominent role in prediction-based learning.Show less
Passive and active learning strategies are well-known strategies which effectiveness has been analyzed for many years. The main purpose of this study is to examine age-related differences in the...Show morePassive and active learning strategies are well-known strategies which effectiveness has been analyzed for many years. The main purpose of this study is to examine age-related differences in the effectiveness of a passive learning strategy (direct instruction) and an active learning strategy (generating predictions). Participants in this study were 66 children aged 10-13 and 45 young adults aged 18-26 who performed a numerical facts learning task. This study used a between-subjects design containing a prediction condition where participants had to generate a prediction before seeing the correct answer and a repetition condition where participants had to repeat the correct answer in order to learn the numerical facts. This study has found a significant age x learning strategy interaction effect, with children remembering more facts after generating predictions rather than repeating, whereas the strategies were similarly effective for young adults. Another significant learning effect was found for the distance from the prediction to the correct answer. Both children and young adults remembered more facts that were predicted correctly or facts that were predicted poorly (with a distance of three to eight), compared to facts that were predicted almost correctly (with a distance of one or two). This provides evidence for the surprise-effect as an underlying working mechanism in learning based on generating predictions because the distance of a prediction to the correct answer is an indicator for the degree of experienced surprise. Furthermore, this study has found wrong predictions more likely to be corrected instead of repeated for both children and young adults. This provides evidence for the effectiveness of learning based on generating predictions. These findings suggest that there are differences in effectiveness among learning strategies for different ages, with generating predictions as an effective strategy for children of 10-13 years.Show less
