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 Concept Videos

Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

729
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
729
Introduction to Learning01:18

Introduction to Learning

359
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
359
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
4.9K
Metacognition01:26

Metacognition

150
Metacognition is a conscious process where individuals are aware of their cognitive and executive processes, such as planning before solving a problem or self-monitoring during reading. For instance, a writer may need help with composing a piece. The situation involves a writer who is working on a piece of writing, but while doing so, they realize that something is missing. They notice that their characters lack depth or details. This realization occurs because the writer is reflecting on their...
150
Direct Motor Pathways01:11

Direct Motor Pathways

1.9K
The direct motor pathways, also known as the pyramidal tracts, are a group of neural pathways that originate in the brain and descend through the spinal cord. They control the voluntary movement of the body. There are two major direct motor pathways: the corticospinal and the corticobulbar tracts.
The corticospinal tract is responsible for the voluntary movement of the limbs and trunk. It originates in the cerebral cortex of the brain and descends through the cerebrum's internal capsule and...
1.9K
Purposive Learning01:22

Purposive Learning

110
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
110

You might also read

Related Articles

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

Sort by
Same author

Pregnancy outcomes in RA and SLE patients: analysis of the 2019 Nationwide Inpatient Sample Database.

EULAR rheumatology open·2026
Same author

USP Gene Network Modulation and Osmoprotection Define Salt Resilience in Chenopodium quinoa Genotypes.

Scientific reports·2026
Same author

Machine learning-based modeling of pharmaceutical sorption in soils: Integrating conformal prediction and Shapley additive explanations analysis for robust risk assessment.

Environmental toxicology and chemistry·2026
Same author

Discontinuation of Oral Anticoagulants After Successful Atrial Fibrillation Ablation: A Systematic Review and Meta-Analysis.

Cardiology in review·2026
Same author

Defect Passivation and Crystallization Regulation in Wide-Bandgap Perovskites via p-Cyanobenzenesulphonamide Molecular Additive.

The journal of physical chemistry letters·2026
Same author

Revisiting inflammaging: a critical appraisal of its role in alveolar bone loss.

Journal of bone and mineral metabolism·2026
Same journal

Novel Parent Survey Measures Sensory Behaviors Incorporating Sensory Modality and Stimulus Intensity.

Heliyon·2026
Same journal

Expression of concern: "SQSTM1/p62 promotes the progression of gastric cancer through epithelial-mesenchymal transition" [Heliyon 10 (2024) e24409].

Heliyon·2026
Same journal

Expression of concern: "TL1A promotes metastasis and EMT process of colorectal cancer" [Heliyon 10 (2024) e24392].

Heliyon·2026
Same journal

Expression of concern: "Factors affecting timing of surgery following neoadjuvant chemoradiation for esophageal cancer" [Heliyon 9 (2023) e23212].

Heliyon·2026
Same journal

Expression of concern: "On stratified single-valued soft topogenous structures" [Heliyon 10 (2024) e27926].

Heliyon·2026
Same journal

Expression of concern: "Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN" [Heliyon 10 (2024) e27198].

Heliyon·2026
See all related articles

Related Experiment Video

Updated: Jun 22, 2025

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

3.5K

Integrating deep learning techniques for personalized learning pathways in higher education.

Fawad Naseer1, Muhammad Nasir Khan2, Muhammad Tahir3

  • 1Computer Science and Software Engineering Department, Beaconhouse International College, Pakistan.

Heliyon
|July 4, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep learning (DL) create personalized learning pathways in higher education. This AI-driven platform significantly improved student grades, scores, and engagement by 25% compared to traditional methods.

Keywords:
Deep learningHigher educationPersonalized learning

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K

Related Experiment Videos

Last Updated: Jun 22, 2025

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

3.5K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K

Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Deep Learning Applications

Background:

  • Traditional higher education often uses a one-size-fits-all approach, failing to meet diverse student needs.
  • Rapid advancements in AI present opportunities for personalized learning experiences.
  • There is a critical need to bridge the gap between static educational content and dynamic student requirements.

Purpose of the Study:

  • To investigate the effectiveness of an AI-driven adaptive learning platform for personalized education in higher education.
  • To create customized learning pathways for students using deep learning techniques.
  • To provide a validated framework for implementing AI in education at scale.

Main Methods:

  • Implementation of an AI-driven adaptive learning platform across four university courses with 300 students.
  • A controlled experiment comparing outcomes between students using the AI platform and those in traditional instruction.
  • Quantitative analysis of grades, test scores, and engagement metrics, alongside qualitative feedback and deep learning analysis of performance data.

Main Results:

  • A statistically significant 25% improvement in grades, test scores, and engagement for students using the AI platform (p-value = 0.00045).
  • Qualitative feedback indicated enhanced student experiences due to personalized learning pathways.
  • Deep learning analysis confirmed enhanced learning outcomes and engagement metrics over time.

Conclusions:

  • AI platforms leveraging deep learning and robust analytics can significantly enhance student academic performance, engagement, and satisfaction.
  • The study provides an empirically validated, data-driven framework for institutions to implement personalized, AI-driven education.
  • These findings have significant implications for the future of adaptive and student-centric higher education methodologies.