EXTRA CREDIT #2 PSYC 2210

Article Overview

  • Title: Web-Mediated Problem-Based Learning and Computer Programming: Effects of Study Approach on Academic Achievement and Attitude

  • Author: Mustafa Yagcı

  • Published in: Journal of Educational Computing Research, 2018, Vol. 56(2)

  • Abstract: An exploration into whether learning programming requires high-level thinking skills and how individual differences affect outcomes.

Research Aim

  • Investigate how the study approach affects:

    • Students' attitudes toward programming

    • Academic achievement in an online problem-based learning environment

Methodology

Study Design

  • Type: Single-factor, pretest-posttest, single group, semi-empirical method.

  • Participants: 41 students from a public university in Turkey.

  • Platform: Moodle utilized for collaborative group activities.

  • Duration: 12-week application.

  • Data Collection Tools:

    • Study Approach Scale (SAS)

    • Attitude Toward Programming Scale (ATPS)

    • Academic Achievement Test (AAT)

Statistical Analysis

  • Analyzed using t-test and covariance analyses.

Key Findings

Effectiveness of Study Approach

  • Deep Study Approach: More successful in achieving better outcomes than the superficial approach.

  • Positive Impact on Attitudes: The PBL approach enhanced students’ attitudes towards programming; however, the study approach was not significantly related to attitudes.

Problem-Based Learning (PBL)

Description of PBL

  • PBL focuses on problem-solving and high-level thinking skills. It transitions students from passive learning to active, self-directed learning.

  • Teachers act as mentors rather than direct sources of information.

  • Learning becomes personalized, enhancing problem-solving and critical thinking skills.

Characteristics of PBL implementation

  • Students are divided into small groups to investigate issues collaboratively.

  • They develop solutions and assess information in a supportive, guided manner.

  • Focus on real-life complexities requiring algorithmic thinking.

Historical Context and Adoption

  • PBL was introduced in medical education in the 1950s at Case W. University and later at McMaster University in Canada.

  • Significant research has been conducted mainly in medicine, engineering, and natural sciences with fewer studies in programming.

Study Approaches

Deep vs. Superficial Approaches

  • Deep Learning Approach: Focused on comprehension, finding connections, and having positive attitudes toward learning.

    • Students strive to understand concepts profoundly and enjoy the learning experience.

  • Superficial Learning Approach: Focused on minimal task completion to pass evaluations.

    • Characterized by memorization and lack of deep understanding, leading to poorer learning outcomes.

Measurement of Study Approaches

  • The Study Approach Scale distinguishes between deep and superficial approaches, each with ten items rated on a 5-point Likert scale.

    • Reliability: Cronbach’s Alpha for deep approach: 0.79; superficial approach: 0.73.

Results on Attitudes

ATP Pretest and Posttest Scores

  • Deep Approach: Pretest Mean ATP = 3.08; Posttest Mean ATP = 3.21.

  • Superficial Approach: Pretest Mean ATP = 3.10; Posttest Mean ATP = 3.22.

  • Significance of Results: t-test shows significant change in ATP scores after PBL implementation (t(40) = -7.65, p = .00).

ANCOVA Analysis

  • No significant difference in ATP scores across study approaches post-application, indicating the approach did not influence student attitudes significantly (F(1,38) = 64.85, p > .005).

Results on Academic Achievement

Achievement Test Results

  • Deep Approach: Pretest Mean = 49.79; Posttest Mean = 68.00.

  • Superficial Approach: Pretest Mean = 49.33; Posttest Mean = 54.50.

  • Overall: Pretest Mean = 49.66; Posttest Mean = 64.05.

T-Test Findings on Achievement Scores

  • Significant improvement noted in students' academic achievement (t(40) = -6.60, p = .00).

  • Conclusion: Online PBL resulted in improved academic achievement scores, particularly for students adopting a deep study approach (effect size = 0.280).

Discussion and Conclusion

  • PBL in programming positively affects ATP, essential for learning in programming courses.

  • Individual differences among students crucially affect the efficiency of programming education, underpinning the need for tailored approaches.

  • Limitations include sample size and students’ unfamiliarity with PBL.

Suggestions for Future Research

  • Large-scale studies to enhance generalizability of findings.

  • Further exploration of online PBL impact considering personal characteristics and optimized course design.

References

  • A comprehensive list of all literature cited in the study, providing further reading for interested scholars in the field.

Author's Background

  • Mustafa Yagcı, associate professor, specializing in programming training, educational technology, distance education, and computer-aided education.

The research article titled "Web-Mediated Problem-Based Learning and Computer Programming: Effects of Study Approach on Academic Achievement and Attitude" was authored by Mustafa Yagcı and published in the Journal of Educational Computing Research in 2018, Vol. 56(2). The authors aimed to investigate how a student's study approach influences both their attitudes toward programming and their academic achievement within an online problem-based learning (PBL) environment. Specifically, they questioned whether learning programming requires high-level thinking skills and how individual differences affect these learning outcomes. This exploration was crucial for understanding the efficacy of web-mediated PBL in a programming context.

The study utilized a single-factor, pretest-posttest, single group, semi-empirical design with 41 university students. Regarding students' attitudes toward programming (ATP), a t-test revealed a significant overall improvement in ATP scores after the PBL implementation, with the mean ATP scores increasing from _$3.08 to 3.21 for the deep approach and from _$3.10 to 3.22 for the superficial approach (t(40) = -7.65, p = .00) . However, an ANCOVA, which controls for other variables like initial ability before assessing the independent variable's impact, showed no significant difference in ATP scores across the study approaches post-application (F(1,38) = 64.85, p > .005) . This indicates that while PBL generally enhanced attitudes, the specific study approach (deep vs. superficial) did not significantly influence the degree of this attitudinal change.

Concerning academic achievement, a t-test indicated a significant improvement in students' overall academic achievement scores following the online PBL intervention (t(40) = -6.60, p = .00) . The mean achievement score for students adopting a deep study approach increased notably from 49.79 to 68.00, whereas for those with a superficial approach, the mean increased from 49.33 to 54.50. This data strongly suggests that online PBL is an effective method for improving academic outcomes in programming courses, particularly benefiting students who engage with the material using a deep study approach. The findings underscore the importance of individual learning strategies in maximizing the benefits of innovative educational methods like PBL.