E-learning Modality
E-learning Modality - Related Videos
13:51
Cross-Modal Multivariate Pattern Analysis
0 Views •
Cited by 6 •
2011
Classical multivariate pattern analysis predicts sensory stimuli a subject perceives from neural activity in the corresponding cortices (e.g. visual stimuli from activity in visual cortex). Here, we apply pattern analysis cross-modally and show that sound- and touch-implying visual stimuli can be predicted from activity in auditory and somatosensory cortices, respectively.
01:15
Sensory Modalities
0 Views •
2024
Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing. General senses refer to the broad category of sensory information detected by receptors in the body and can be further grouped into somatic and visceral senses. Somatic sensations include touch, pressure, temperature, and pain and are essential for navigating our environment and...
08:05
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
0 Views •
Cited by 16 •
2020
Presented here is a protocol introducing a set of child-friendly statistical learning tasks geared towards examining children’s learning of temporal statistical patterns across domains and sensory modalities. The developed tasks collect behavioral data using the web-based platform and task-based functional magnetic resonance imaging (fMRI) data for examining neural engagement during statistical learning.
07:13
Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
0 Views •
Cited by 3 •
2023
The combination of multiple imaging modalities is often necessary to gain a comprehensive understanding of pathophysiology. This approach utilizes phantoms to generate a differential transformation between the coordinate systems of two modalities, which is then applied for co-registration. This method eliminates the need for fiducials in production scans.
13:44
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
0 Views •
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
Design choices shape more than where lessons appear: they determine how learners encounter content, communicate, receive feedback, and demonstrate understanding. In statistics education, a modality can combine visual demonstrations, interactive simulations, software-based work, and online assessments. This integration may support engagement, accessibility, self-paced review, and data literacy, making modality selection an instructional decision rather than merely a technology choice.
Synchronous and asynchronous formats differ in how instructional timing is organized. Synchronous delivery structures participation around coordinated interaction, while asynchronous delivery supports access to materials and activities at a learner’s own pace. Recorded lectures and online tools can support asynchronous review of complex statistical concepts. The choice affects how content, communication, feedback, and assessment are arranged.
Different tools contribute distinct instructional functions. Interactive simulations can demonstrate probability, data visualization can make patterns visible, statistical software can support analysis, discussion forums can facilitate communication, and online assessments can structure evaluation. When coordinated through a learning management system, these components connect statistical content, interaction, feedback, and review within one educational environment.
Selection should begin with the learning experience the course must provide, then align the delivery format, digital tools, communication channels, and assessment practices with that purpose. For statistics, the design can incorporate probability demonstrations, visualizations, software, collaborative analysis, and opportunities to revisit difficult concepts. This alignment helps technology support statistical learning rather than simply relocate classroom materials online.
These approaches are useful when instruction needs to extend beyond a traditional classroom while still organizing content, communication, and feedback. A fully online format can bring lectures, activities, discussion, and assessment into digital systems, while blended delivery can combine online resources with classroom experiences. Both can support statistical software, data visualization, and probability demonstrations in academic or professional settings.
Comparison can focus on engagement, accessibility, assessment practices, feedback, and the development of data literacy. In a statistics course, educators can also consider whether the format provides suitable access to demonstrations, visualizations, software, collaborative analysis, and self-paced review. These criteria connect the delivery approach to educational aims rather than judging a modality only by its digital tools.