Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 12, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Transforming a Large-Enrollment Neuroscience Course With Active Learning: Using AI-Based Text Analysis to Capture

Mariana Teles1, Erin Clabough1, Taylor Byron1

  • 1Psychology University of Virginia.

Journal of Undergraduate Neuroscience Education : JUNE : a Publication of FUN, Faculty for Undergraduate Neuroscience
|July 11, 2026
PubMed
Summary

Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

How retrieval processes change with age: Exploring age differences in semantic network and retrieval dynamics.

Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale·2024
Same author

Acute Ethanol Exposure during Synaptogenesis Rapidly Alters Medium Spiny Neuron Morphology and Synaptic Protein Expression in the Dorsal Striatum.

International journal of molecular sciences·2022
Same author

Longitudinal association between subjective and objective memory in older adults: a study with the Virginia Cognitive Aging Project sample.

Neuropsychology, development, and cognition. Section B, Aging, neuropsychology and cognition·2021
Same author

Seroprevalence in Bats and Detection of <i>Borrelia burgdorferi</i> in Bat Ectoparasites.

Microorganisms·2020
Same author

Analyzing Spatial Learning and Prosocial Behavior in Mice Using the Barnes Maze and Damsel-in-Distress Paradigms.

Journal of visualized experiments : JoVE·2018

Redesigning large neuroscience courses to a hybrid active learning format improved student engagement and learning experiences. This approach, analyzed with AI tools, revealed benefits missed by traditional surveys.

Area of Science:

  • Neuroscience Education
  • Higher Education Pedagogy
  • Educational Technology

Background:

  • Traditional large-enrollment undergraduate neuroscience courses often utilize lecture formats, potentially limiting student engagement with complex topics.
  • Active learning strategies are increasingly recognized for their potential to enhance student understanding and participation in science education.

Purpose of the Study:

  • To redesign a large-enrollment neuroscience course into a hybrid active learning format.
  • To evaluate the impact of this pedagogical redesign on student learning experience and engagement.
  • To compare the effectiveness of AI-driven qualitative analysis with traditional Likert-scale assessments.

Main Methods:

  • Implemented a hybrid active learning model combining asynchronous online lectures and in-person small-group sessions.
Keywords:
active learninglarge enrollment coursesstudent feedback analysis

Related Experiment Videos

Last Updated: Jul 12, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Analyzed 547 open-ended student comments from 232 students across two course formats (traditional vs. active learning) using zero-shot classification with a large language model (BART).
  • Compared qualitative findings from AI analysis with quantitative data from Likert-scale surveys.

    • Main Results:

    • The active learning format demonstrated significant improvements in students' perceived learning experience and engagement.
    • These gains in student engagement and perceived learning were not captured by traditional Likert-scale survey data.
    • AI-powered analysis of open-ended comments provided deeper insights into the benefits of the pedagogical innovation.

    Conclusions:

    • Hybrid active learning formats can significantly enhance student engagement and learning experiences in large undergraduate neuroscience courses.
    • AI tools offer a powerful and accessible method for analyzing qualitative student feedback to evaluate pedagogical innovations, overcoming limitations of traditional quantitative methods.
    • Neuroscience educators can adopt this framework to redesign large courses and assess their impact effectively using accessible AI technologies.