A Latent Profile Analysis of Teachers’ Knowledge About and Perceived Usefulness of Computational Thinking and How Teacher Profiles Relate to Student Achievement

 

Introduction

In today’s rapidly evolving technological landscape, computational thinking (CT) has become a critical skill for students. It involves problem-solving techniques such as decomposition, pattern recognition, abstraction, and algorithm design, which are essential for tackling complex issues. However, the success of integrating CT into classrooms heavily depends on teachers’ knowledge and their perceived usefulness of CT. This blog explores a study that uses Latent Profile Analysis (LPA) to identify teacher profiles based on their CT knowledge and perceived usefulness. It also examines how these profiles relate to student achievement in CT-related skills.

What is Latent Profile Analysis (LPA)?

Latent Profile Analysis (LPA) is a statistical method used to identify subgroups (or profiles) within a population based on continuous variables. In this context, LPA is applied to classify teachers based on:

  • Their knowledge of CT concepts and applications.
  • Their perceived usefulness of CT in educational practices.

Teacher Profiles Identified Through LPA

The study typically reveals three to four distinct teacher profiles:

  1. High Knowledge, High Usefulness (HK-HU):

    • These teachers have a strong understanding of CT concepts.
    • They recognize CT’s value and frequently incorporate it into their teaching practices.
    • Their students tend to perform significantly better in CT-related tasks.

  2. Moderate Knowledge, Moderate Usefulness (MK-MU):

    • Teachers in this group have a fair grasp of CT but lack deeper expertise.
    • They view CT as moderately useful but do not regularly apply it.
    • Students show moderate achievement in CT-related assessments.

  3. Low Knowledge, Low Usefulness (LK-LU):

    • These teachers have limited knowledge of CT.
    • They perceive it as less useful or irrelevant to their subject areas.
    • Their students demonstrate lower CT proficiency.

  4. Mixed Knowledge and Usefulness (MK-LU) (Optional profile in some studies):

    • Teachers with moderate knowledge but low perceived usefulness.
    • They understand CT concepts but do not consider them valuable, leading to minimal classroom integration.

Key Findings: Teacher Profiles and Student Achievement

The LPA-based study shows a direct correlation between teacher profiles and student performance:

  • HK-HU teachers’ students outperform others in problem-solving, coding, and logical reasoning skills.
  • MK-MU teachers’ students achieve moderate success, reflecting the teachers' limited application of CT strategies.
  • LK-LU teachers’ students struggle with CT concepts, indicating that teacher competence and belief in CT’s usefulness significantly impact student outcomes.

Implications for Educational Practice

  1. Targeted Professional Development:

    • Schools should offer differentiated training programs to meet teachers’ varying CT knowledge levels.
    • Professional development initiatives should focus on both content knowledge and practical application strategies.

  2. Promoting Positive Perceptions:

    • Enhancing teachers' confidence in using CT through hands-on workshops can increase their perceived usefulness.
    • Real-world examples and student success stories can help teachers recognize CT’s value.

  3. Curriculum Integration:

    • Teachers with high knowledge but low perceived usefulness should receive support to integrate CT meaningfully into their curriculum.
    • Cross-disciplinary collaborations (e.g., combining CT with science, math, or arts) can make CT more relevant.

✅ Conclusion

The Latent Profile Analysis of teachers’ knowledge and perceived usefulness of CT highlights the crucial role of teacher competency and attitude in influencing student achievement. The study reveals that students perform best when teachers have both high knowledge and a positive perception of CT’s value. To improve student outcomes, educational institutions must invest in targeted teacher training programs that enhance both CT knowledge and its perceived classroom applicability.

By empowering teachers with the right skills and mindset, schools can better prepare students for the technology-driven future, where CT will be an essential skill across all disciplines.


2nd Edition of Applied Scientist Awards | 28-29 March 2025|San Francisco, United States.

Nomination Link

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