1. Exploring the effects of gender and learning styles on computer programming performance: implications for programming pedagogy
Adviser: Ming-Puu Chen
Presenter: Li-Chun Wang
Lau, W. W. F. & Yuen, A. H. K. (2008). Exploring the effects of gender and learning styles on computer programming performance: Implications for programming pedagogy. British Journal of Educational Technology, 1-17.

2. Abstract
This study aims to investigate the effects of gender and learning styles on computer programming performance.
From the curriculum implementation perspectives, learning style helps address the issue of learner differences, resulting in a shift from a teacher-centred approach to a learner-focused approach.
Results indicated that no gender differences in programming performance were found after controlling for the effect of student ability.
Academic ability had a differential effect on programming knowledge.

3. Introduction
Learning to program still plays a role in promoting information literacy in technology education.
Learning to program has been widely recognised as a highly cognitively demanding task for students and has posed a lot of difficulties for them at the outset.
Chamillard and Karolick (1999) argue that learning style data can help students to ‘develop their study habits, to help instructors select their instructional strategies more effectively and to help researchers better understand how different learning styles can affect student performance.
However, most studies have been focused on university students and little is known about the impact of learning styles on secondary school students in a Hong Kong context.

4. Introduction Gender and programming performance
- Research on gender and programming performance tends to produce inconclusive results.
> no gender difference
> women perform better than men : dedication and hard work that may have been brought on by the anxiety of not being expected to do well
Learning styles and programming performance
- Myers-Briggs: personality type
> Sensing students performed better than intuitive students
> Judging students achieved higher results than perceptive students
- Felder-Silverman
> Reflective learners outperformed active learners
> Verbal learners outperformed visual learners
 Certain learning styles are associated with higher academic achievement irrespective of the learning style instruments being used.

5. Introduction Gregorc Style Delineator
The human mind has channels through which information is received and expressed most efficiently and effectively
Mediation abilities:
> Concrete sequential (CS): learners tend to perceive reality through their physical senses and think in an orderly, logical, and sequentially manner
> Concrete random (CR): learners like to experiment with ideas and concepts and think intuitively, instinctively, impulsively and independently
> Abstract sequential (AS): learners are logical and analytical individuals who have a preference for mentally stimulating task and environment
> Abstract random (AR): learners have a strong sense on the world of feeling and emotion and tend to think in a nonlinear and emotional manner

6. Introduction The nature of programming knowledge
- Shneidreman and Mayer (1979):
> Syntactic knowledge: the details of how computation is implemented in a particular programming language
> Semantic knowledge: requires understanding of programming constructs and concepts that are independent of specific programming languages
- Bayman and Mayer (1988):
> Syntactic knowledge: the features and facts of the language
> Conceptual knowledge: a conceptual model of the system
> Strategic knowledge: the use of syntactic and conceptual knowledge to solve novel problems

7. Introduction The nature of programming knowledge
- The programming performance test in this study:
> Declarative knowledge (DK): about knowledge of some factual information like definition of terms and it is described as a type of knowing-what knowledge
> Procedural knowledge (PK): type of knowing-how knowledge
> Conditional knowledge (CK): type of knowing-when knowledge
> Strategic knowledge (SK): type of knowing-why knowledge

8. Method Participants: Secondary 4-5 students
Content: Bubble sorting allgorithm
Grouped: academic performance in primary schools
Instruments: GSD, programming performance test

9. Results
Effects of gender and learning styles on programming performance
Prediction of programming performance by learning styles

10. Results Main effect of band  DK, PK : B1 >B2
AS, CS > CR
CS > CR, AR
Main effect of band  DK, PK : B1 >B2
Main effect of learning styles  DK: AS, CS > CR; SK: CS > CR, AR

11. Discussion
There were no gender differences found in programming performance
Main effect of band  DK, PK : B1 >B2
- Academic ability has a differential effect on programming knowledge.
Main effect of learning styles  DK: AS, CS > CR; SK: CS > CR, AR
- Their preference for an unstructured learning condition make learning computer programming a stumbling block to them as writing a program is a highly structured and procedural endeavour.

12. Implications
First, students’ ability has a positive influence on programming performance
- To provide additional resources to support the weaker students, a remedial program could be offered.
Second, although there were statistically significant learning style group effects on programming performance, programming scores decreased towards the higher level of programming knowledge.
- Using interventions like planning strategy for algorithmic development and programming through interactive teaching approach
Third, sequential learners outperformed random learners in all measures of programming knowledge
- Random students are potentially at risk in computer science or computer application courses
- Random learners should be given extra attention in learning to program
- Group discussions