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 Videos

Computerizing reading training: evaluation of a latent semantic analysis space for science text.

Christopher A Kurby1, Katja Wiemer-Hastings, Nagasai Ganduri

  • 1Department of Psychology, Northern Illinois University, DeKalb, Illinois 60115, USA. ckurby@niu.edu

Behavior Research Methods, Instruments, & Computers : a Journal of the Psychonomic Society, Inc
|July 2, 2003
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

The role of event understanding in guiding attentional selection in real-world scenes: The Scene Perception & Event Comprehension Theory (SPECT).

Attention, perception & psychophysics·2026
Same author

Bridging a Gap in Coherence: The Coordination of Comprehension Processes When Viewing Visual Narratives.

Vision (Basel, Switzerland)·2024
Same author

Events shape long-term memory for story information.

Discourse processes·2023
Same author

Relationships between task awareness, comprehension strategies, and literacy outcomes.

Frontiers in psychology·2023
Same author

Automated Strategy Feedback Can Improve the Readability of Physicians' Electronic Communications to Simulated Patients.

International journal of human-computer studies·2023
Same author

Leveraging a multidimensional linguistic analysis of constructed responses produced by college readers.

Frontiers in psychology·2022

Domain-specific latent semantic analysis (LSA) effectively assesses reading strategies by correlating highly with human judgments. A science-focused LSA space offers advantages for analyzing student think-aloud protocols in science texts.

Area of Science:

  • Educational Technology
  • Natural Language Processing
  • Cognitive Science

Background:

  • Assessing student reading strategies is crucial for effective learning.
  • Latent Semantic Analysis (LSA) is a computational method for analyzing semantic relationships in text.
  • Domain-specific LSA may offer improved accuracy over general LSA in specialized fields.

Purpose of the Study:

  • To evaluate the effectiveness of a domain-specific LSA in assessing reading strategies.
  • To compare a science-specific LSA with a general reading LSA for this task.
  • To explore the utility of LSA in providing feedback for self-explanation reading training (SERT).

Main Methods:

  • Students engaged in self-explanation reading training (SERT) and provided think-aloud protocols after each sentence.

Related Experiment Videos

  • Human raters (novice and expert) and two LSA spaces (general reading, science) evaluated protocol similarity to benchmark reading strategies (minimal, local, global).
  • Cosine similarity scores from LSA were analyzed for correlation with human judgments and ability to differentiate strategy levels.

    • Main Results:

    • The science-specific LSA space demonstrated a high correlation with human judgments of reading strategy similarity.
    • The science LSA outperformed the general reading LSA in aligning with human assessments.
    • LSA cosines could distinguish between different semantic similarity levels but struggled with local processing protocols.

    Conclusions:

    • A domain-specific LSA space is advantageous for assessing reading strategies, irrespective of its size.
    • The findings support the application of science-specific LSA for developing computer-based SERT with online feedback.
    • This approach has potential for enhancing science education through automated analysis of student comprehension.