Experience-based Learning
Experience-based Learning - Related Videos
06:19
Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Cited by 4 •
2025
Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.
10:25
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Cited by 54 •
2022
This is a method for training a multi-slice U-Net for multi-class segmentation of cryo-electron tomograms using a portion of one tomogram as a training input. We describe how to infer this network to other tomograms and how to extract segmentations for further analyses, such as subtomogram averaging and filament tracing.
06:58
The Factorial Experiment
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2023
Source: Laboratories of Gary Lewandowski, Dave Strohmetz, and Natalie Ciarocco—Monmouth University A factorial design is a common type of experiment where there are two or more independent variables. This video demonstrates a 2 x 2 factorial design used to explore how self-awareness and self-esteem may influence the ability to decipher nonverbal signals. This video leads students through the basics of a factorial design including, the nature of a factorial design and what distinguishes it from...
04:04
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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2025
This study employed voice signal analysis and machine learning methods, utilizing MATLAB to extract distinctive voice features for non-invasive early detection of asthma. The Support Vector Machine (SVM) and Random Forest (RF) algorithms demonstrated comparable performance in terms of overall classification accuracy, although SVM may achieve a better balance between sensitivity and specificity.
13:44
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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Cited by 6 •
2022
Here, we present a protocol for the behavioral analysis of a project-based learning methodology for health sciences students (20-56 years old). The protocol facilitates the comparison of the participants' performance in e-Learning versus blended-Learning (b-Learning) through a monitoring tool. The results are analyzed using Educational Data Mining and qualitative techniques.
FAQs
Its effectiveness comes from linking four activities: concrete experience, reflection on what happened, connection of observations to concepts, and active experimentation with revised strategies. Each stage informs the next, so learners do more than complete an activity. They examine outcomes, adjust their understanding, and test improved approaches, making learning more responsive to feedback and practical demands.
Reflection turns participation into analyzed experience. Learners consider the results of an activity, identify what their observations suggest, and connect those observations with relevant concepts. This step helps prevent experience from remaining isolated or purely procedural. In psychology, reflective analysis can clarify how abstract principles relate to behavior, decisions, communication, or problem-solving in a particular situation.
Transfer is supported when learners move beyond one successful activity and apply revised strategies through active experimentation. They compare what occurred with the concepts they have learned, use feedback to modify their approach, and test it in another real or simulated context. This repeated connection between action, reflection, and application helps knowledge remain usable outside the original learning setting.
Begin with a meaningful activity that gives learners a concrete situation to examine. Next, provide time to reflect on outcomes and observations, then guide learners in connecting those observations to psychological concepts. Finally, have them apply revised strategies through active experimentation. This sequence can be adapted to laboratory exercises, simulations, fieldwork, clinical training, or service learning.
Psychology educators can use it in laboratory exercises, clinical training, fieldwork, simulations, and service learning. These settings give learners opportunities to connect psychological knowledge with situations that require practical engagement. The specific format may differ, but each setting can support observation, reflection, conceptual understanding, and strategy application while building competence relevant to psychological work or study.
Experience-based activities can strengthen problem-solving, decision-making, communication, and retention when learners connect abstract principles with real or simulated situations. They also support practical competence by requiring learners to examine outcomes and adjust strategies. In psychology, these outcomes are relevant because students must often use conceptual knowledge while responding to situations that involve observation, judgment, interaction, or applied practice.